├── utils ├── lib │ ├── __init__.py │ ├── Makefile │ ├── caverages.pxd │ └── averages_wrapper.pyx ├── __init__.py ├── types.f90 ├── caverages.h ├── txt2npy.py ├── Makefile ├── constants.f90 ├── caverages.f90 ├── averages.f90 └── utils.f90 ├── data ├── .gitattributes ├── annual.png ├── decadal.png ├── annual-with-forcing-small.png ├── decadal-with-forcing-small.png ├── UAH_LT_1979_thru_June_2013_v5.6.png ├── uat4_tb_v03r03_avrg_chtlt_197812_201504.nc3.nc ├── ZonAnn.Ts+dSST.txt ├── GLB.Ts+dSST.txt ├── std_atmosphere_wt_function_chan_TLS.txt ├── std_atmosphere_wt_function_chan_TTS.txt ├── std_atmosphere_wt_function_chan_tlt_land.txt ├── std_atmosphere_wt_function_chan_tlt_ocean.txt ├── std_atmosphere_wt_function_chan_tmt_land.txt └── std_atmosphere_wt_function_chan_tmt_ocean.txt ├── TODO ├── LICENSE ├── README.md ├── Sea Level Trend New York City.ipynb └── CO2 temperature analysis.ipynb /utils/lib/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /data/.gitattributes: -------------------------------------------------------------------------------- 1 | *.nc filter=lfs diff=lfs merge=lfs -crlf 2 | -------------------------------------------------------------------------------- /data/annual.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/certik/climate/HEAD/data/annual.png -------------------------------------------------------------------------------- /data/decadal.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/certik/climate/HEAD/data/decadal.png -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | from lib.averages_wrapper import arit, arit2, rmean, smean 2 | -------------------------------------------------------------------------------- /data/annual-with-forcing-small.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/certik/climate/HEAD/data/annual-with-forcing-small.png -------------------------------------------------------------------------------- /data/decadal-with-forcing-small.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/certik/climate/HEAD/data/decadal-with-forcing-small.png -------------------------------------------------------------------------------- /data/UAH_LT_1979_thru_June_2013_v5.6.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/certik/climate/HEAD/data/UAH_LT_1979_thru_June_2013_v5.6.png -------------------------------------------------------------------------------- /data/uat4_tb_v03r03_avrg_chtlt_197812_201504.nc3.nc: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:ab931a190bda511758fae65cfc8d80c0f163c84782e06284002bba364e05c46f 3 | size 19611680 4 | -------------------------------------------------------------------------------- /utils/lib/Makefile: -------------------------------------------------------------------------------- 1 | all: averages_wrapper.so 2 | 3 | averages_wrapper.so: averages_wrapper.o ../libaverages.a 4 | g++ -shared -o averages_wrapper.so averages_wrapper.o -L.. -laverages -lgfortran 5 | 6 | averages_wrapper.o: averages_wrapper.cpp ../caverages.h 7 | g++ -O2 -fPIC -I$(PYTHONHPC)/include/python2.7 -I../ -o averages_wrapper.o -c averages_wrapper.cpp 8 | 9 | averages_wrapper.cpp: caverages.pxd averages_wrapper.pyx 10 | cython --cplus averages_wrapper.pyx 11 | 12 | clean: 13 | rm -f averages_wrapper.so averages_wrapper.o averages_wrapper.cpp 14 | -------------------------------------------------------------------------------- /utils/lib/caverages.pxd: -------------------------------------------------------------------------------- 1 | cdef extern from "caverages.h": 2 | 3 | void averages_arit(int n_long, int n_lat, double *longitude, 4 | double *latitude, double *field, int *mask, double *r) 5 | void averages_rmean(int n_long, int n_lat, double *longitude, 6 | double *latitude, double *field, int *mask, double r, double *m) 7 | void averages_smean(int n_long, int n_lat, double *longitude, 8 | double *latitude, double *field, int *mask, double s, double *m) 9 | void averages_arit2(int n_long, int n_lat, double *longitude, 10 | double *latitude, double *field, int *mask, double *r) 11 | -------------------------------------------------------------------------------- /utils/types.f90: -------------------------------------------------------------------------------- 1 | module types 2 | implicit none 3 | private 4 | public dp, hp, ivector, dvector, zvector 5 | 6 | integer, parameter :: dp=kind(0.d0), & ! double precision 7 | hp=selected_real_kind(15) ! high precision 8 | 9 | type ivector ! allocatable integer vector 10 | integer, pointer :: vec(:) => null() 11 | end type 12 | 13 | type dvector ! allocatable real double precision vector 14 | real(dp), pointer :: vec(:) => null() 15 | end type 16 | 17 | type zvector ! allocatable complex double precision vector 18 | complex(dp), pointer :: vec(:) => null() 19 | end type 20 | 21 | end module 22 | -------------------------------------------------------------------------------- /TODO: -------------------------------------------------------------------------------- 1 | * Interpolate years using: 2 | 3 | >>> ["%.3f" % (i/12. + 1/24.) for i in range(12)] 4 | ['0.042', '0.125', '0.208', '0.292', '0.375', '0.458', '0.542', '0.625', 5 | '0.708', '0.792', '0.875', '0.958'] 6 | 7 | * Investigate the role of averaging twice (e.g. "month->year->10 years" vs. 8 | "month->10 years"). It seems to produce different results. The problem seems 9 | to be how to properly average yearly data on a monthly grid --- should we 10 | average over 10*12 points, or only 10 points? The answer seems 10*12 points, 11 | to take into account the 10 year average, but then it seems that it is 12 | averaged more than it should be. So we should plot this and create a nice 13 | notebook that clarifies the issue. 14 | -------------------------------------------------------------------------------- /utils/caverages.h: -------------------------------------------------------------------------------- 1 | #ifndef _caverages_h 2 | #define _caverages_h 3 | 4 | #if defined (__cplusplus) 5 | extern "C" { 6 | #endif 7 | 8 | 9 | void averages_arit(int n_long, int n_lat, double *longitude, double *latitude, 10 | double *field, int *mask, double *r); 11 | void averages_rmean(int n_long, int n_lat, double *longitude, double *latitude, 12 | double *field, int *mask, double r, double *m); 13 | void averages_smean(int n_long, int n_lat, double *longitude, double *latitude, 14 | double *field, int *mask, double s, double *m); 15 | void averages_arit2(int n_long, int n_lat, double *longitude, double *latitude, 16 | double *field, int *mask, double *r); 17 | 18 | #if defined (__cplusplus) 19 | } 20 | #endif 21 | 22 | #endif 23 | -------------------------------------------------------------------------------- /utils/txt2npy.py: -------------------------------------------------------------------------------- 1 | # Reads data.txt into NumPy array and saves the array in binary format. 2 | # It is memory efficient. 3 | 4 | # You can work with the saved array as follows: 5 | # >>> from numpy import load, shape, average 6 | # >>> D = load("data.npy") 7 | # >>> shape(D) 8 | # (7, 14645083) 9 | # >>> average(D[2, :]) 10 | # 1965.7348883489171 11 | # >>> average(D[3, :]) 12 | # 11.469809257683686 13 | 14 | from numpy import empty, array, save 15 | N = 14645083 16 | D = empty((7, N)) 17 | f = open("data.txt") 18 | i = 0 19 | for line in f: 20 | if line.startswith("%"): continue 21 | D[:, i] = array([float(x) for x in line.split()]) 22 | i += 1 23 | if i % 100000 == 0: print "%.3f%%" % (100. * i / N) 24 | print "Saving..." 25 | save("data.npy", D) 26 | print "Done." 27 | -------------------------------------------------------------------------------- /utils/Makefile: -------------------------------------------------------------------------------- 1 | # Modify these for your Fortran compiler: 2 | 3 | # GFortran 4 | F90 = gfortran 5 | F90FLAGS = -Wall -Wextra -Wimplicit-interface -fPIC 6 | # Debug flags: 7 | F90FLAGS += -g -fbounds-check 8 | # Release flags: 9 | #F90FLAGS += -O3 -march=native -ffast-math -funroll-loops 10 | 11 | # Intel ifort 12 | #F90 = ifort-12.0.191 13 | #F90FLAGS = -stand f95 -warn all 14 | # Debug flags: 15 | #F90FLAGS += -check all 16 | # Release flags: 17 | #F90FLAGS += -xHOST -O3 -no-prec-div -static 18 | 19 | PROG = libaverages.a 20 | 21 | OBJS = caverages.o averages.o constants.o types.o 22 | 23 | all: $(PROG) 24 | cd lib; make 25 | 26 | $(PROG): $(OBJS) 27 | ar rcs $@ $(OBJS) 28 | 29 | clean: 30 | rm -f $(PROG) $(OBJS) *.mod 31 | cd lib; make clean 32 | 33 | %.o: %.f90 34 | $(F90) $(F90FLAGS) -c $< 35 | 36 | averages.o: types.o constants.o 37 | caverages.o: averages.o 38 | utils.o: types.o 39 | constants.o: types.o 40 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Copyright (c) 2013 Ondřej Čertík 2 | 3 | Permission is hereby granted, free of charge, to any person obtaining a copy 4 | of this software and associated documentation files (the "Software"), to deal 5 | in the Software without restriction, including without limitation the rights 6 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 7 | copies of the Software, and to permit persons to whom the Software is 8 | furnished to do so, subject to the following conditions: 9 | 10 | The above copyright notice and this permission notice shall be included in 11 | all copies or substantial portions of the Software. 12 | 13 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 14 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 15 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 16 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 17 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 18 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN 19 | THE SOFTWARE. 20 | -------------------------------------------------------------------------------- /utils/constants.f90: -------------------------------------------------------------------------------- 1 | module constants 2 | use types, only: dp 3 | implicit none 4 | private 5 | public pi, e_, i_, bohr2ang, ang2bohr, Ha2eV 6 | 7 | ! Constants contain more digits than double precision, so that 8 | ! they are rounded correctly. Single letter constants contain underscore so 9 | ! that they do not clash with user variables ("e" and "i" are frequently used as 10 | ! loop variables) 11 | real(dp), parameter :: pi = 3.1415926535897932384626433832795_dp 12 | real(dp), parameter :: e_ = 2.7182818284590452353602874713527_dp 13 | complex(dp), parameter :: i_ = (0, 1) 14 | 15 | real(dp), parameter :: Na = 6.02214129e23_dp ! Avogadro constant 16 | ! Standard uncertainty 0.00000027 (Source: 2010 CODATA) 17 | real(dp), parameter :: Ha2eV = 27.21138505_dp ! 1 Ha = (1 * Ha2eV) eV 18 | ! Standard uncertainty is 0.00000060 eV (Source: 2010 CODATA) 19 | 20 | real(dp), parameter :: J2Ha = 2.29371248e17_dp ! 1 J = (1 * J2Ha) Ha 21 | ! Standard uncertainty is 0.00000010 eV (Source: 2010 CODATA) 22 | 23 | ! Covert Ha to cm^{-1} (energy equivalent) 24 | ! 1 Ha = (1 * Ha2invcm) cm^{-1} 25 | real(dp), parameter :: Ha2invcm = 219474.6313708_dp 26 | ! Standard uncertainty is 0.0000011 cm^{-1} (Source: 2010 CODATA) 27 | 28 | ! Conversion between Bohr (Hartree atomic units) and Angstrom 29 | real(dp), parameter :: bohr2ang = 0.529177249_dp 30 | real(dp), parameter :: ang2bohr = 1 / bohr2ang 31 | 32 | 33 | ! Converts eV to kcal/mol, it is assumed, that the number in eV is given per 34 | ! molecule. 1 eV = (1 * eV2kcalmol) kcal/mol 35 | real(dp), parameter :: kcalmol2kJmol = 4.184_dp 36 | real(dp), parameter :: kJmol2Ha = 1000 * J2Ha / Na 37 | 38 | end module 39 | -------------------------------------------------------------------------------- /utils/lib/averages_wrapper.pyx: -------------------------------------------------------------------------------- 1 | from numpy cimport ndarray 2 | from numpy import empty 3 | 4 | cimport caverages 5 | 6 | def arit(ndarray[double, mode="c"] longitude not None, 7 | ndarray[double, mode="c"] latitude not None, 8 | ndarray[double, ndim=2, mode="c"] field not None, 9 | ndarray[int, ndim=2, mode="c"] mask not None): 10 | cdef int n_long = len(longitude) 11 | cdef int n_lat = len(latitude) 12 | cdef double r 13 | caverages.averages_arit(n_long, n_lat, &longitude[0], &latitude[0], 14 | &field[0, 0], &mask[0, 0], &r) 15 | return r 16 | 17 | def rmean(ndarray[double, mode="c"] longitude not None, 18 | ndarray[double, mode="c"] latitude not None, 19 | ndarray[double, ndim=2, mode="c"] field not None, 20 | ndarray[int, ndim=2, mode="c"] mask not None, 21 | double r): 22 | cdef int n_long = len(longitude) 23 | cdef int n_lat = len(latitude) 24 | cdef double m 25 | caverages.averages_rmean(n_long, n_lat, &longitude[0], &latitude[0], 26 | &field[0, 0], &mask[0, 0], r, &m) 27 | return m 28 | 29 | def smean(ndarray[double, mode="c"] longitude not None, 30 | ndarray[double, mode="c"] latitude not None, 31 | ndarray[double, ndim=2, mode="c"] field not None, 32 | ndarray[int, ndim=2, mode="c"] mask not None, 33 | double s): 34 | cdef int n_long = len(longitude) 35 | cdef int n_lat = len(latitude) 36 | cdef double m 37 | caverages.averages_smean(n_long, n_lat, &longitude[0], &latitude[0], 38 | &field[0, 0], &mask[0, 0], s, &m) 39 | return m 40 | 41 | def arit2(ndarray[double, mode="c"] longitude not None, 42 | ndarray[double, mode="c"] latitude not None, 43 | ndarray[double, ndim=2, mode="c"] field not None, 44 | ndarray[int, ndim=2, mode="c"] mask not None): 45 | cdef int n_long = len(longitude) 46 | cdef int n_lat = len(latitude) 47 | cdef double r 48 | caverages.averages_arit2(n_long, n_lat, &longitude[0], &latitude[0], 49 | &field[0, 0], &mask[0, 0], &r) 50 | return r 51 | -------------------------------------------------------------------------------- /utils/caverages.f90: -------------------------------------------------------------------------------- 1 | module c_dftatom 2 | 3 | use iso_c_binding, only: c_int, c_double, c_bool 4 | use averages, only: arit, arit2, rmean, smean 5 | implicit none 6 | 7 | contains 8 | 9 | subroutine averages_arit(n_long, n_lat, longitude, latitude, field, mask, r) bind(c) 10 | integer(c_int), intent(in), value :: n_long, n_lat 11 | real(c_double), intent(in) :: longitude(n_long), latitude(n_lat), field(n_long, n_lat) 12 | ! mask == 0 is .false. and 1 is .true. 13 | integer(c_int), intent(in) :: mask(n_long, n_lat) 14 | real(c_double), intent(out) :: r 15 | logical :: mask2(n_long, n_lat) 16 | where (mask == 0) 17 | mask2 = .false. 18 | else where 19 | mask2 = .true. 20 | end where 21 | r = arit(longitude, latitude, field, mask2) 22 | end subroutine 23 | 24 | subroutine averages_rmean(n_long, n_lat, longitude, latitude, field, mask, r, m) bind(c) 25 | integer(c_int), intent(in), value :: n_long, n_lat 26 | real(c_double), intent(in) :: longitude(n_long), latitude(n_lat), field(n_long, n_lat) 27 | ! mask == 0 is .false. and 1 is .true. 28 | integer(c_int), intent(in) :: mask(n_long, n_lat) 29 | real(c_double), intent(in), value :: r 30 | real(c_double), intent(out) :: m 31 | logical :: mask2(n_long, n_lat) 32 | where (mask == 0) 33 | mask2 = .false. 34 | else where 35 | mask2 = .true. 36 | end where 37 | m = rmean(longitude, latitude, field, mask2, r) 38 | end subroutine 39 | 40 | subroutine averages_smean(n_long, n_lat, longitude, latitude, field, mask, s, m) bind(c) 41 | integer(c_int), intent(in), value :: n_long, n_lat 42 | real(c_double), intent(in) :: longitude(n_long), latitude(n_lat), field(n_long, n_lat) 43 | ! mask == 0 is .false. and 1 is .true. 44 | integer(c_int), intent(in) :: mask(n_long, n_lat) 45 | real(c_double), intent(in), value :: s 46 | real(c_double), intent(out) :: m 47 | logical :: mask2(n_long, n_lat) 48 | where (mask == 0) 49 | mask2 = .false. 50 | else where 51 | mask2 = .true. 52 | end where 53 | m = smean(longitude, latitude, field, mask2, s) 54 | end subroutine 55 | 56 | subroutine averages_arit2(n_long, n_lat, longitude, latitude, field, mask, r) bind(c) 57 | integer(c_int), intent(in), value :: n_long, n_lat 58 | real(c_double), intent(in) :: longitude(n_long), latitude(n_lat), field(n_long, n_lat) 59 | ! mask == 0 is .false. and 1 is .true. 60 | integer(c_int), intent(in) :: mask(n_long, n_lat) 61 | real(c_double), intent(out) :: r 62 | logical :: mask2(n_long, n_lat) 63 | where (mask == 0) 64 | mask2 = .false. 65 | else where 66 | mask2 = .true. 67 | end where 68 | r = arit2(longitude, latitude, field, mask2) 69 | end subroutine 70 | 71 | end module 72 | -------------------------------------------------------------------------------- /utils/averages.f90: -------------------------------------------------------------------------------- 1 | module averages 2 | use types, only: dp 3 | use constants, only: pi 4 | implicit none 5 | private 6 | public arit, arit2, rmean, smean 7 | 8 | contains 9 | 10 | real(dp) function arit(longitude, latitude, field, mask) result(r) 11 | ! field is (long, lat), mask is the same shape, 12 | ! only .true. elements will be considered 13 | ! longitude is [-180, 180], latitude is [-90, 90] 14 | real(dp), intent(in) :: longitude(:), latitude(:), field(:, :) 15 | logical, intent(in) :: mask(:, :) 16 | real(dp) :: w(size(field, 1), size(field, 2)) 17 | w = spread(cos(latitude*pi/180), 1, size(longitude)) 18 | w = w / sum(w) 19 | r = sum(field*w, mask=mask) / sum(w, mask=mask) 20 | end function 21 | 22 | real(dp) function rmean(longitude, latitude, field, mask, r) result(m) 23 | ! field is (long, lat), mask is the same shape, 24 | ! only .true. elements will be considered 25 | ! longitude is [-180, 180], latitude is [-90, 90] 26 | real(dp), intent(in) :: longitude(:), latitude(:), field(:, :) 27 | logical, intent(in) :: mask(:, :) 28 | real(dp), intent(in) :: r 29 | real(dp) :: w(size(field, 1), size(field, 2)) 30 | w = spread(cos(latitude*pi/180), 1, size(longitude)) 31 | w = w / sum(w) 32 | m = (sum(field**r * w, mask=mask) / sum(w, mask=mask))**(1/r) 33 | end function 34 | 35 | real(dp) function smean(longitude, latitude, field, mask, s) result(m) 36 | ! field is (long, lat), mask is the same shape, 37 | ! only .true. elements will be considered 38 | ! longitude is [-180, 180], latitude is [-90, 90] 39 | real(dp), intent(in) :: longitude(:), latitude(:), field(:, :) 40 | logical, intent(in) :: mask(:, :) 41 | real(dp), intent(in) :: s 42 | real(dp) :: w(size(field, 1), size(field, 2)) 43 | w = spread(cos(latitude*pi/180), 1, size(longitude)) 44 | w = w / sum(w) 45 | m = log(sum(exp(s*field) * w, mask=mask) / sum(w, mask=mask)) / s 46 | end function 47 | 48 | real(dp) function arit2(longitude, latitude, field, mask) result(r) 49 | ! field is (long, lat), mask is the same shape, 50 | ! only .true. elements will be considered 51 | ! longitude is [-180, 180], latitude is [-90, 90] 52 | real(dp), intent(in) :: longitude(:), latitude(:), field(:, :) 53 | logical, intent(in) :: mask(:, :) 54 | real(dp) :: tmp(size(mask, 2)), w(size(mask, 2)) 55 | integer :: tmp2(size(mask, 1)), n 56 | logical :: tmp_mask(size(mask, 2)) 57 | integer :: i 58 | w = sin(pi/144) * cos(latitude*pi/180) 59 | tmp2 = 1 60 | do i = 1, size(field, 2) 61 | n = sum(tmp2, mask=mask(:, i)) 62 | if (n == 0) then 63 | tmp_mask(i) = .false. 64 | else 65 | tmp(i) = sum(field(:, i), mask=mask(:, i)) / n 66 | tmp_mask(i) = .true. 67 | end if 68 | end do 69 | r = sum(tmp*w, mask=tmp_mask) / sum(w, mask=tmp_mask) 70 | end function 71 | 72 | end module 73 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Climate 2 | 3 | ## Summary 4 | 5 | The goal of this project is to provide climate data and analysis in computable 6 | form (in terms of [IPython Notebooks](http://ipython.org/notebook.html)). Part 7 | of our goal is to reproduce graphs and results from literature. Ultimately we 8 | would like to start from the raw data (e.g. from temperature stations, 9 | satellites, ice cores and other measurements) and provide the analysis in a 10 | notebook, so that anyone can easily rerun it and see exactly how the final 11 | results and graphs were obtained from original data. 12 | 13 | ## Preview the IPython Notebooks 14 | 15 | If you just want to see the notebooks, the best way is to preview them with the 16 | links below. When you're ready to develop or run the code yourself, pull the 17 | source for this GitHub repository and execute the notebooks on your own 18 | machine. 19 | 20 | * [Satellite Temperature (RSS)](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/RSS.ipynb) 21 | * [Satellite Temperature (RSS) maps](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/RSS%2520plots.ipynb) 22 | * [Berkeley Fit](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/Berkeley%2520fit.ipynb) 23 | * [Temperature Data](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/Temperature%2520Data.ipynb) 24 | * [Sea Ice Index](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/Sea%2520Ice%2520Index.ipynb) 25 | * [Sea Ice Index Arctic](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/Sea%2520Ice%2520Index%2520Arctic.ipynb) 26 | * [Temperature Fits Reproduction](http://nbviewer.ipython.org/urls/raw.github.com/certik/climate/master/Temperature%2520Fits%2520Reproduction.ipynb) 27 | * [Sea Level Trend New York City](http://nbviewer.ipython.org/github/certik/climate/blob/master/Sea%20Level%20Trend%20New%20York%20City.ipynb) 28 | 29 | ## Motivation 30 | 31 | The motivation for this work is the following: 32 | 33 | * Reproduce graphs and results from literature. 34 | * Provide notebooks that anyone can easily rerun to reproduce all our results 35 | exactly (including all the graphs). 36 | * Provide a "pipeline", that takes raw data and calculates final results, so 37 | that anyone can easily see what exact analysis was done. 38 | * Let the data speak for itself, we try hard not to "jump to conclusions" (or 39 | have "leaps of faith"), that does not strictly (scientifically) follow from 40 | the data. 41 | * Following Feynman's "never trust the experts", we want anybody to form his or 42 | her opinion about the issue, and this project might help by doing the hard 43 | work (of getting the data, understanging it, analysing it and calculating 44 | results/graphs), that one has to do anyway in order to understand the issues. 45 | By the nature of the process, the analysis can be wrong, but by making all 46 | the analysis public in a computable form, it can be discussed, improved or 47 | dismissed. 48 | * In particular (as implied by the previous points), we try to strictly only 49 | include analysis that is scientifically agreed upon and undisputed, or if 50 | that is not possible, to provide all possible weaknesses of the given 51 | approach. 52 | 53 | Please send us ideas for improvements (for example you can open a GitHub 54 | [issue](https://github.com/certik/climate/issues) or send us an 55 | email) or send a GitHub [pull 56 | request](https://help.github.com/articles/using-pull-requests) (even better 57 | :). 58 | 59 | ## License 60 | 61 | All files (notebooks, Python code) are licensed under MIT license, see the 62 | [LICENSE](https://raw.github.com/certik/climate/master/LICENSE) for more 63 | information. The `data` directory contains files that were downloaded from 64 | various sources and they are covered by their own respective licenses. 65 | -------------------------------------------------------------------------------- /Sea Level Trend New York City.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "metadata": { 3 | "name": "", 4 | "signature": "sha256:f9c90eb38d0373a36a06c9eb169325635139c45e7b34e6491372b4b65c2f6dfa" 5 | }, 6 | "nbformat": 3, 7 | "nbformat_minor": 0, 8 | "worksheets": [ 9 | { 10 | "cells": [ 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "Scientific American published an article [New York City Could See 6-Foot Sea Rise, Tripling of Heat Waves by 2100](http://www.scientificamerican.com/article/new-york-city-could-see-6-foot-sea-rise-tripling-of-heat-waves-by-2100/) and the following claims:\n", 16 | "\n", 17 | "* ... with sea levels now predicted by a new report to climb by as much as 6 feet by 2100.\n", 18 | "* New York could see a 6-foot increase under a worst-case scenario that has been revised from previous estimates that 2 to 4 feet would be the maximum rise.\n", 19 | "\n", 20 | "The full report is at http://doi.org/10.1111/nyas.12591" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "The full report has a Figure ES.2., which shows New York City sea level rise observations and projections. They don't specify where the observations were taken from, but they wrote it averages 1.2in/decade. That seems to be consistent e.g. with [Mean Sea Level Trend, 8518750 The Battery, New York](http://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?stnid=8518750) (by NOAA), where the average is $(2.83\\pm0.09)\\,\\textrm{mm/yr}$, which is equal to $(1.11\\pm0.04)\\,\\textrm{in/decade}$ (the difference probably caused by them averaging from 1900, while NOAA from 1850):" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "collapsed": false, 33 | "input": [ 34 | "2.83 * 10 / 25.4, 0.09 * 10 / 25.4" 35 | ], 36 | "language": "python", 37 | "metadata": {}, 38 | "outputs": [ 39 | { 40 | "metadata": {}, 41 | "output_type": "pyout", 42 | "prompt_number": 1, 43 | "text": [ 44 | "(1.1141732283464567, 0.03543307086614173)" 45 | ] 46 | } 47 | ], 48 | "prompt_number": 1 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "Let's take 1.2in/decade, the total rise in 2100 assuming linear trend from now (i.e. in 85 years) is 10.2in:" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "collapsed": false, 60 | "input": [ 61 | "1.2 * 8.5" 62 | ], 63 | "language": "python", 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "metadata": {}, 68 | "output_type": "pyout", 69 | "prompt_number": 2, 70 | "text": [ 71 | "10.2" 72 | ] 73 | } 74 | ], 75 | "prompt_number": 2 76 | }, 77 | { 78 | "cell_type": "markdown", 79 | "metadata": {}, 80 | "source": [ 81 | "Which is about 0.85 ft:" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "collapsed": false, 87 | "input": [ 88 | "_ / 12." 89 | ], 90 | "language": "python", 91 | "metadata": {}, 92 | "outputs": [ 93 | { 94 | "metadata": {}, 95 | "output_type": "pyout", 96 | "prompt_number": 3, 97 | "text": [ 98 | "0.85" 99 | ] 100 | } 101 | ], 102 | "prompt_number": 3 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "metadata": {}, 107 | "source": [ 108 | "That seems to be consistent with the Figure ES.2." 109 | ] 110 | }, 111 | { 112 | "cell_type": "markdown", 113 | "metadata": {}, 114 | "source": [ 115 | "The report then claims the following:\n", 116 | "\n", 117 | "* Sea level rise is very likely to accelerate as the century progresses.\n", 118 | "* Projections for sea level rise in New York City are 11 to 21 inches by the 2050s, 18 to 39 inches by the 2080s, and could reach as high as 6 feet by 2100." 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": {}, 124 | "source": [ 125 | "Notes:\n", 126 | "\n", 127 | "* Why would the sea level rise accelerate? The CO2 concentrations have been rising througout the 20th century, but the sea level rise is perfectly linear so far, so one would expect some other reason for the possible acceleration of the sea level rise.\n", 128 | "* The projections are based on the blue \"Middle range\" bar, i.e. 4-9in by 2020, 11-21in by 2050, 18-39in by 2080 and it looks like 21-50in by 2100.\n", 129 | "* The \"low/high estimate\" looks like is 0/10in by 2020, 9/30in by 2050, 11/59in by 2080, 15/75in by 2100.\n", 130 | "* The \"could reach as high as 6 feet by 2100\" is based on the \"high estimate\".\n", 131 | "* If the \"high estimate\" is true, then already by 2020 we should see 10in rise, which is the same as the linear trend at 2100. If the rise in 2020 is instead 0.6in (linear trend), then we are not following the \"Middle range\" or \"High range\" estimate." 132 | ] 133 | } 134 | ], 135 | "metadata": {} 136 | } 137 | ] 138 | } -------------------------------------------------------------------------------- /utils/utils.f90: -------------------------------------------------------------------------------- 1 | module utils 2 | 3 | ! Various general utilities. 4 | ! Based on a code by John E. Pask, LLNL. 5 | 6 | use types, only: dp 7 | implicit none 8 | private 9 | public upcase, lowcase, whitechar, blank, num_strings, getstring, & 10 | stop_error, arange, loadtxt, savetxt, newunit, assert, str, init_random, & 11 | zeros 12 | 13 | interface str 14 | module procedure str_int, str_real, str_real_n 15 | end interface 16 | 17 | contains 18 | 19 | function upcase(s) result(t) 20 | ! Returns string 's' in uppercase 21 | character(*), intent(in) :: s 22 | character(len(s)) :: t 23 | integer :: i, diff 24 | t = s; diff = ichar('A')-ichar('a') 25 | do i = 1, len(t) 26 | if (ichar(t(i:i)) >= ichar('a') .and. ichar(t(i:i)) <= ichar('z')) then 27 | ! if lowercase, make uppercase 28 | t(i:i) = char(ichar(t(i:i)) + diff) 29 | end if 30 | end do 31 | end function 32 | 33 | function lowcase(s) result(t) 34 | ! Returns string 's' in lowercase 35 | character(*), intent(in) :: s 36 | character(len(s)) :: t 37 | integer :: i, diff 38 | t = s; diff = ichar('A')-ichar('a') 39 | do i = 1, len(t) 40 | if (ichar(t(i:i)) >= ichar('A') .and. ichar(t(i:i)) <= ichar('Z')) then 41 | ! if uppercase, make lowercase 42 | t(i:i) = char(ichar(t(i:i)) - diff) 43 | end if 44 | end do 45 | end function 46 | 47 | logical function whitechar(char) ! white character 48 | ! returns .true. if char is space (32) or tab (9), .false. otherwise 49 | character, intent(in) :: char 50 | if (iachar(char) == 32 .or. iachar(char) == 9) then 51 | whitechar = .true. 52 | else 53 | whitechar = .false. 54 | end if 55 | end function 56 | 57 | logical function blank(string) 58 | ! Returns true if string contains only white characters 59 | character(*), intent(in) :: string 60 | integer :: i 61 | do i = 1, len(string) 62 | if (.not. whitechar(string(i:i))) exit 63 | end do 64 | blank = (i>len(string)) 65 | end function 66 | 67 | integer function num_strings(s) result(n) 68 | ! Returns number of substrings contained in input string 's' delimited 69 | ! by white space. 70 | character(*), intent(in) :: s ! input string 71 | character(len(s)+2) :: t ! temporary string to facilitate analysis 72 | integer :: i 73 | t = " " // s // " " 74 | n = 0 75 | do i = 1, len(t)-1 76 | if (whitechar(t(i:i)) .and. .not. whitechar(t(i+1:i+1))) n = n + 1 77 | end do 78 | end function 79 | 80 | !--------------------------------------------------------------------------------------------------! 81 | 82 | subroutine getstring(s,is,ss) 83 | ! Returns first substring ss in string s, delimited by white space, starting at 84 | ! index is in s. If ss is found, is is set to (index of last character of ss in 85 | ! s) + 1; else is is set to 0. If is is out of range on input, routine 86 | ! terminates with is = -1. 87 | character(*), intent(in) :: s ! input string 88 | integer, intent(inout) :: is ! on input: starting index for search for ss in 89 | ! s on output: (index of last character of ss in 90 | ! s) + 1 91 | character(*), intent(out) :: ss ! first substring in s, starting from index is 92 | character(len(s)+1) :: t ! temporary string to facilitate search 93 | integer i, i1, i2 94 | logical prevwhite, curwhite 95 | if (is <= 0 .or. is > len(s)) then 96 | ss = ""; is = -1; return 97 | end if 98 | t = s // " " 99 | if (is == 1) then 100 | prevwhite = .true. 101 | else 102 | prevwhite = whitechar(t(is-1:is-1)) 103 | end if 104 | i1 = 0; i2 = 0 105 | do i = is, len(t) 106 | curwhite = whitechar(t(i:i)) 107 | if (prevwhite .and. .not. curwhite) i1 = i ! beginning of substring 108 | if (i1>0 .and. curwhite) then ! end of substring 109 | i2 = i-1; exit 110 | end if 111 | prevwhite=curwhite 112 | end do 113 | if (i2 > 0) then 114 | ss = t(i1:i2); is = i2+1 115 | else 116 | ss = ""; is = 0 117 | end if 118 | end subroutine 119 | 120 | integer function newunit(unit) result(n) 121 | ! Returns lowest i/o unit number not in use (to be used in older compilers). 122 | ! 123 | ! Starting at 10 to avoid lower numbers which are sometimes reserved. 124 | ! Note: largest valid unit number may be system-dependent. 125 | ! 126 | ! Arguments 127 | ! --------- 128 | ! 129 | ! If present, the new unit will be returned into it 130 | integer, intent(out), optional :: unit 131 | ! 132 | ! Example 133 | ! ------- 134 | ! 135 | ! integer :: u 136 | ! open(newunit(u), file="log.txt", status="old") 137 | ! read(u, *) a, b 138 | ! close(u) 139 | ! 140 | ! In new compilers, just use the "newunit" keyword argument: 141 | ! 142 | ! integer :: u 143 | ! open(newunit=u, file="log.txt", status="old") 144 | ! read(u, *) a, b 145 | ! close(u) 146 | 147 | logical inuse 148 | integer, parameter :: nmin=10 ! avoid lower numbers which are sometimes reserved 149 | integer, parameter :: nmax=999 ! may be system-dependent 150 | do n = nmin, nmax 151 | inquire(unit=n, opened=inuse) 152 | if (.not. inuse) then 153 | if (present(unit)) unit=n 154 | return 155 | end if 156 | end do 157 | call stop_error("newunit ERROR: available unit not found.") 158 | end function 159 | 160 | subroutine stop_error(msg) 161 | ! Aborts the program with nonzero exit code 162 | ! 163 | ! The statement "stop msg" will return 0 exit code when compiled using 164 | ! gfortran. stop_error() uses the statement "stop 1" which returns an exit code 165 | ! 1 and a print statement to print the message. 166 | ! 167 | ! Example 168 | ! ------- 169 | ! 170 | ! call stop_error("Invalid argument") 171 | 172 | character(len=*) :: msg ! Message to print on stdout 173 | print *, msg 174 | stop 1 175 | end subroutine 176 | 177 | subroutine loadtxt(filename, d) 178 | ! Loads a 2D array from a text file. 179 | ! 180 | ! Arguments 181 | ! --------- 182 | ! 183 | ! Filename to load the array from 184 | character(len=*), intent(in) :: filename 185 | ! The array 'd' will be automatically allocated with the correct dimensions 186 | real(dp), allocatable, intent(out) :: d(:, :) 187 | ! 188 | ! Example 189 | ! ------- 190 | ! 191 | ! real(dp), allocatable :: data(:, :) 192 | ! call loadtxt("log.txt", data) ! 'data' will be automatically allocated 193 | ! 194 | ! Where 'log.txt' contains for example:: 195 | ! 196 | ! 1 2 3 197 | ! 2 4 6 198 | ! 8 9 10 199 | ! 11 12 13 200 | ! ... 201 | ! 202 | character :: c 203 | integer :: s, ncol, nrow, ios, i 204 | logical :: lastwhite 205 | real(dp) :: r 206 | 207 | open(newunit(s), file=filename, status="old") 208 | 209 | ! determine number of columns 210 | ncol = 0 211 | lastwhite = .true. 212 | do 213 | read(s, '(a)', advance='no', iostat=ios) c 214 | if (ios /= 0) exit 215 | if (lastwhite .and. .not. whitechar(c)) ncol = ncol + 1 216 | lastwhite = whitechar(c) 217 | end do 218 | 219 | rewind(s) 220 | 221 | ! determine number or rows 222 | nrow = 0 223 | do 224 | read(s, *, iostat=ios) r 225 | if (ios /= 0) exit 226 | nrow = nrow + 1 227 | end do 228 | 229 | rewind(s) 230 | 231 | allocate(d(nrow, ncol)) 232 | do i = 1, nrow 233 | read(s, *) d(i, :) 234 | end do 235 | close(s) 236 | end subroutine 237 | 238 | subroutine savetxt(filename, d) 239 | ! Saves a 2D array into a textfile. 240 | ! 241 | ! Arguments 242 | ! --------- 243 | ! 244 | character(len=*), intent(in) :: filename ! File to save the array to 245 | real(dp), intent(in) :: d(:, :) ! The 2D array to save 246 | ! 247 | ! Example 248 | ! ------- 249 | ! 250 | ! real(dp) :: data(3, 2) 251 | ! call savetxt("log.txt", data) 252 | 253 | integer :: s, i 254 | open(newunit(s), file=filename, status="replace") 255 | do i = 1, size(d, 1) 256 | write(s, *) d(i, :) 257 | end do 258 | close(s) 259 | end subroutine 260 | 261 | subroutine arange(a, b, dx, u) 262 | ! Returns an array u = [a, a+dx, a+2*dx, ..., b-dx] 263 | ! 264 | ! Arguments 265 | ! --------- 266 | ! 267 | real(dp), intent(in) :: a, b, dx 268 | real(dp), allocatable, intent(out) :: u(:) 269 | ! 270 | ! Example 271 | ! ------- 272 | ! 273 | ! real(dp), allocatable :: u(:) 274 | ! call arange(1, 5, 1, u) ! u = [1, 2, 3, 4] 275 | integer :: n, i 276 | n = int((b-a) / dx) 277 | allocate(u(n)) 278 | do i = 1, n 279 | u(i) = a + (i-1)*dx 280 | end do 281 | end subroutine 282 | 283 | function zeros(n) result(x) 284 | integer, intent(in) :: n 285 | real(dp) :: x(n) 286 | x = 0 287 | end function 288 | 289 | subroutine assert(condition) 290 | ! If condition == .false., it aborts the program. 291 | ! 292 | ! Arguments 293 | ! --------- 294 | ! 295 | logical, intent(in) :: condition 296 | ! 297 | ! Example 298 | ! ------- 299 | ! 300 | ! call assert(a == 5) 301 | 302 | if (.not. condition) call stop_error("Assert failed.") 303 | end subroutine 304 | 305 | pure integer function str_int_len(i) result(sz) 306 | ! Returns the length of the string representation of 'i' 307 | integer, intent(in) :: i 308 | integer, parameter :: MAX_STR = 100 309 | character(MAX_STR) :: s 310 | ! If 's' is too short (MAX_STR too small), Fortan will abort with: 311 | ! "Fortran runtime error: End of record" 312 | write(s, '(i0)') i 313 | sz = len_trim(s) 314 | end function 315 | 316 | pure function str_int(i) result(s) 317 | ! Converts integer "i" to string 318 | integer, intent(in) :: i 319 | character(len=str_int_len(i)) :: s 320 | write(s, '(i0)') i 321 | end function 322 | 323 | pure integer function str_real_len(r, fmt) result(sz) 324 | ! Returns the length of the string representation of 'i' 325 | real(dp), intent(in) :: r 326 | character(len=*), intent(in) :: fmt 327 | integer, parameter :: MAX_STR = 100 328 | character(MAX_STR) :: s 329 | ! If 's' is too short (MAX_STR too small), Fortan will abort with: 330 | ! "Fortran runtime error: End of record" 331 | write(s, fmt) r 332 | sz = len_trim(s) 333 | end function 334 | 335 | pure function str_real(r) result(s) 336 | ! Converts the real number "r" to string with 7 decimal digits. 337 | real(dp), intent(in) :: r 338 | character(len=*), parameter :: fmt="(f0.6)" 339 | character(len=str_real_len(r, fmt)) :: s 340 | write(s, fmt) r 341 | end function 342 | 343 | pure function str_real_n(r, n) result(s) 344 | ! Converts the real number "r" to string with 'n' decimal digits. 345 | real(dp), intent(in) :: r 346 | integer, intent(in) :: n 347 | character(len=str_real_len(r, "(f0." // str_int(n) // ")")) :: s 348 | write(s, "(f0." // str_int(n) // ")") r 349 | end function 350 | 351 | subroutine init_random() 352 | ! Initializes the random number generator based on the system's time. 353 | integer :: i, n, clock 354 | integer, allocatable :: seed(:) 355 | call random_seed(size=n) 356 | allocate(seed(n)) 357 | call system_clock(count=clock) 358 | seed = clock + 37 * [(i - 1, i = 1, n)] 359 | call random_seed(put=seed) 360 | end subroutine 361 | 362 | end module 363 | -------------------------------------------------------------------------------- /data/ZonAnn.Ts+dSST.txt: -------------------------------------------------------------------------------- 1 | Annual mean Land-Ocean Temperature Index in .01 degrees Celsius 2 | selected zonal means 3 | -------------------- 4 | sources: GHCN-v3 1880-06/2013 + SST: 1880-06/2013 ERSST 5 | using elimination of outliers and homogeneity adjustment 6 | Note: ***** = missing - base period: 1951-1980 7 | 8 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 9 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 10 | 1880 -22 -36 -9 -41 -23 -3 -111 -47 -28 -27 -18 -8 2 63 1880 11 | 1881 -14 -24 -3 -32 -7 -5 -71 -44 -18 -12 -1 -9 -1 36 1881 12 | 1882 -17 -26 -7 -28 -20 -2 -141 -25 -9 -24 -15 -8 4 53 1882 13 | 1883 -20 -31 -8 -41 -18 -1 -40 -72 -20 -17 -18 -8 7 50 1883 14 | 1884 -27 -43 -12 -58 -16 -12 -132 -66 -38 -20 -12 -19 -3 34 1884 15 | 1885 -25 -36 -14 -55 -13 -12 -121 -56 -41 -9 -18 -23 2 52 1885 16 | 1886 -24 -34 -14 -43 -21 -10 -124 -38 -30 -22 -20 -14 -8 56 1886 17 | 1887 -32 -35 -28 -39 -30 -26 -161 -37 -16 -29 -32 -30 -22 28 1887 18 | 1888 -20 -21 -18 -37 3 -33 -140 -44 -13 2 4 -38 -29 33 1888 19 | 1889 -10 -12 -8 -20 1 -16 -84 -12 -12 0 3 -22 -10 45 1889 20 | 1890 -33 -36 -30 -41 -36 -22 -131 -41 -22 -30 -42 -22 -24 31 1890 21 | 1891 -27 -27 -26 -39 -19 -25 -130 -27 -28 -10 -29 -26 -25 9 1891 22 | 1892 -32 -38 -25 -39 -36 -18 -126 -51 -15 -36 -36 -13 -26 -6 1892 23 | 1893 -36 -42 -29 -41 -44 -20 -85 -41 -31 -44 -44 -15 -31 -9 1893 24 | 1894 -33 -36 -29 -31 -35 -30 -134 -20 -13 -44 -27 -27 -38 -14 1894 25 | 1895 -25 -29 -22 -37 -13 -29 -95 -41 -20 -16 -11 -31 -28 -7 1895 26 | 1896 -18 -24 -13 -37 -1 -23 -125 -34 -17 -4 2 -26 -21 18 1896 27 | 1897 -19 -20 -19 -31 -2 -31 -85 -34 -15 -4 0 -33 -30 45 1897 28 | 1898 -32 -32 -33 -33 -32 -30 -125 -21 -18 -29 -36 -36 -23 57 1898 29 | 1899 -21 -21 -21 -20 -19 -24 -108 -5 -8 -22 -16 -27 -21 61 1899 30 | 1900 -15 -11 -19 -17 0 -32 -63 -10 -10 -1 1 -37 -29 36 1900 31 | 32 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 33 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 34 | 1901 -21 -13 -28 -12 -15 -37 -58 -5 -5 -14 -16 -42 -30 12 1901 35 | 1902 -31 -34 -27 -47 -16 -34 -157 -46 -17 -15 -18 -36 -32 16 1902 36 | 1903 -37 -35 -38 -41 -32 -38 -50 -33 -43 -26 -39 -43 -32 -22 1903 37 | 1904 -44 -43 -44 -44 -47 -39 -36 -54 -39 -43 -51 -42 -35 -99 1904 38 | 1905 -30 -30 -30 -36 -19 -38 -18 -18 -53 -20 -17 -43 -34 -5 1905 39 | 1906 -27 -23 -30 -22 -26 -31 -33 -2 -33 -25 -28 -31 -34 -40 1906 40 | 1907 -43 -49 -36 -57 -38 -34 -77 -72 -44 -37 -40 -31 -39 -94 1907 41 | 1908 -44 -45 -42 -44 -50 -35 -50 -48 -40 -47 -53 -38 -37 92 1908 42 | 1909 -47 -44 -49 -45 -49 -44 -84 -49 -33 -43 -56 -47 -43 -22 1909 43 | 1910 -46 -45 -46 -39 -53 -41 -78 -17 -44 -53 -54 -41 -47 49 1910 44 | 1911 -44 -40 -49 -36 -45 -51 -38 -32 -38 -45 -46 -53 -53 38 1911 45 | 1912 -41 -49 -33 -58 -28 -42 -74 -71 -46 -35 -20 -49 -30 -128 1912 46 | 1913 -39 -47 -32 -43 -39 -37 -63 -30 -46 -53 -24 -37 -39 -65 1913 47 | 1914 -23 -23 -23 -24 -17 -29 -60 -7 -26 -21 -14 -28 -36 -10 1914 48 | 1915 -17 -14 -19 -20 -8 -25 -63 -8 -16 -4 -11 -19 -32 -168 1915 49 | 1916 -36 -38 -35 -31 -46 -30 -38 -43 -21 -48 -43 -29 -32 -93 1916 50 | 1917 -44 -53 -35 -45 -58 -25 -83 -42 -37 -65 -50 -27 -27 45 1917 51 | 1918 -32 -38 -26 -37 -34 -23 -121 -18 -25 -39 -29 -21 -31 13 1918 52 | 1919 -29 -35 -23 -45 -19 -27 -93 -49 -29 -21 -17 -25 -35 52 1919 53 | 1920 -28 -28 -27 -21 -32 -29 -6 -12 -31 -39 -24 -25 -36 -58 1920 54 | 55 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 56 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 57 | 1921 -21 -12 -30 2 -31 -30 -12 22 -6 -32 -31 -29 -35 -39 1921 58 | 1922 -30 -26 -33 -21 -34 -32 -39 -32 -10 -33 -34 -29 -41 5 1922 59 | 1923 -26 -20 -31 -11 -32 -33 14 -6 -21 -35 -28 -30 -41 -29 1923 60 | 1924 -24 -14 -35 -8 -24 -42 23 -15 -13 -23 -24 -42 -42 -35 1924 61 | 1925 -22 -10 -33 0 -25 -39 -21 25 -10 -26 -24 -29 -57 -53 1925 62 | 1926 -9 3 -21 4 2 -37 36 20 -15 2 2 -36 -42 -29 1926 63 | 1927 -19 -9 -28 -11 -13 -34 -20 -16 -5 -7 -19 -29 -42 -120 1927 64 | 1928 -17 -6 -28 -2 -13 -36 48 -9 -13 -11 -15 -28 -47 -194 1928 65 | 1929 -32 -28 -36 -31 -25 -41 -6 -49 -28 -22 -28 -37 -47 -89 1929 66 | 1930 -12 5 -30 12 -11 -39 39 18 1 -6 -16 -31 -48 -230 1930 67 | 1931 -7 9 -24 7 5 -40 43 7 -3 11 0 -41 -40 -18 1931 68 | 1932 -11 3 -25 11 -13 -31 20 33 -6 -10 -16 -23 -45 -73 1932 69 | 1933 -25 -22 -29 -21 -25 -30 -41 -39 -4 -23 -27 -19 -48 -88 1933 70 | 1934 -10 4 -23 20 -22 -23 61 40 -4 -21 -24 -13 -41 -25 1934 71 | 1935 -15 -5 -26 0 -16 -29 8 6 -6 -11 -21 -21 -40 -135 1935 72 | 1936 -10 1 -21 0 -9 -23 12 18 -15 1 -19 -20 -32 30 1936 73 | 1937 3 18 -12 24 1 -15 102 10 9 9 -7 -12 -27 79 1937 74 | 1938 6 22 -11 43 -13 -6 117 49 16 -9 -18 1 -19 -64 1938 75 | 1939 0 12 -11 28 -12 -11 47 36 18 -12 -13 -2 -23 -142 1939 76 | 1940 6 14 -1 11 16 -10 82 4 -7 18 13 -6 -21 22 1940 77 | 78 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 79 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 80 | 1941 7 12 2 -4 31 -12 -26 -12 7 37 25 -10 -19 -21 1941 81 | 1942 5 11 -1 11 7 -5 33 -2 12 10 4 1 -14 -63 1942 82 | 1943 5 17 -6 36 -12 -2 113 28 17 -12 -12 2 -14 141 1943 83 | 1944 13 24 3 37 6 -2 98 48 12 4 9 6 -15 -41 1944 84 | 1945 0 4 -3 6 3 -9 41 -4 2 0 6 -3 -15 -99 1945 85 | 1946 -8 1 -16 5 -11 -16 -21 -3 18 -4 -17 -20 -13 32 1946 86 | 1947 -5 7 -16 16 -9 -18 96 -9 6 -5 -14 -20 -20 27 1947 87 | 1948 -10 -1 -20 12 -20 -21 6 35 0 -21 -19 -21 -17 -78 1948 88 | 1949 -11 -3 -20 10 -20 -21 17 16 4 -23 -18 -18 -17 -148 1949 89 | 1950 -19 -17 -20 -8 -30 -14 5 -33 3 -31 -29 -10 -17 -61 1950 90 | 1951 -6 6 -18 9 -7 -21 8 4 13 0 -13 -22 -17 -49 1951 91 | 1952 2 6 -3 11 0 -6 16 -6 20 0 1 -9 1 -21 1952 92 | 1953 9 23 -5 35 5 -11 84 38 17 5 4 -7 -12 -93 1953 93 | 1954 -11 -3 -20 5 -20 -15 63 -20 1 -14 -27 -14 -12 -66 1954 94 | 1955 -12 -9 -16 4 -32 -2 -38 -6 23 -27 -37 -16 -7 132 1955 95 | 1956 -18 -24 -12 -22 -31 3 -16 -46 -10 -27 -35 -11 7 58 1956 96 | 1957 4 4 4 3 6 2 4 22 -11 5 6 -5 -10 59 1957 97 | 1958 4 15 -6 9 18 -18 -15 17 11 24 12 -9 -11 -66 1958 98 | 1959 3 10 -5 14 6 -13 50 16 1 4 8 -4 -17 -36 1959 99 | 1960 -4 7 -15 9 0 -22 34 -6 10 4 -5 -11 -9 -85 1960 100 | 101 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 102 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 103 | 1961 5 9 1 17 -2 2 -21 37 17 -4 0 11 -13 7 1961 104 | 1962 4 17 -9 28 0 -15 55 32 16 1 -1 2 -9 -87 1962 105 | 1963 7 17 -2 16 15 -12 -8 31 15 17 13 -11 -18 -1 1963 106 | 1964 -20 -20 -20 -25 -12 -25 -71 -22 -12 -12 -12 -27 -10 -49 1964 107 | 1965 -10 -13 -8 -17 -2 -14 -23 -23 -11 -7 2 -20 -6 -11 1965 108 | 1966 -4 0 -8 -12 8 -13 -70 -15 8 17 -1 -21 -10 10 1966 109 | 1967 -1 4 -6 12 -11 -1 41 26 -6 -9 -13 -5 -6 23 1967 110 | 1968 -5 -4 -6 -8 -3 -7 -19 -2 -7 1 -6 -9 4 -18 1968 111 | 1969 6 -1 14 -25 32 3 2 -57 -14 35 30 2 9 -3 1969 112 | 1970 4 -3 11 -10 8 13 -17 -16 -4 9 7 12 3 40 1970 113 | 1971 -6 -13 0 -4 -22 11 -5 2 -7 -28 -16 9 9 27 1971 114 | 1972 2 -17 22 -34 18 17 -46 -48 -21 7 28 19 -5 57 1972 115 | 1973 16 11 21 13 18 16 18 27 2 8 28 21 3 30 1973 116 | 1974 -7 -20 5 -17 -19 19 -30 -9 -18 -23 -15 20 2 57 1974 117 | 1975 -1 -5 2 12 -22 14 22 35 -6 -30 -15 12 12 24 1975 118 | 1976 -12 -22 -2 -27 -12 3 -6 -34 -29 -16 -9 8 15 -37 1976 119 | 1977 15 10 20 12 12 21 21 20 4 7 17 18 30 14 1977 120 | 1978 5 -1 10 -5 5 14 -8 -2 -6 6 4 16 22 -5 1978 121 | 1979 12 4 19 -5 20 18 -59 5 6 19 22 23 27 -17 1979 122 | 1980 23 13 33 5 24 39 31 0 -1 26 23 28 27 107 1980 123 | 124 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 125 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 126 | 1981 28 35 22 48 14 28 127 76 6 15 12 24 21 60 1981 127 | 1982 9 3 16 -8 22 9 -30 6 -11 19 26 13 18 -19 1982 128 | 1983 27 23 31 22 38 18 30 67 -8 25 51 15 29 9 1983 129 | 1984 12 2 23 0 14 22 35 7 -15 4 24 13 18 62 1984 130 | 1985 8 -2 19 -4 6 23 36 -27 -3 0 13 24 26 18 1985 131 | 1986 15 10 19 8 19 16 0 23 1 13 24 21 16 1 1986 132 | 1987 29 23 34 5 52 21 -30 13 10 51 54 26 14 21 1987 133 | 1988 35 33 37 38 31 38 71 51 20 26 36 33 14 109 1988 134 | 1989 24 25 23 39 10 28 37 66 23 5 16 33 19 33 1989 135 | 1990 39 47 31 58 31 31 57 82 44 31 31 34 24 34 1990 136 | 1991 38 38 38 43 33 39 71 59 25 30 37 30 24 103 1991 137 | 1992 19 10 29 5 25 25 -23 37 -5 17 34 18 27 47 1992 138 | 1993 21 17 24 12 28 19 61 25 -11 24 31 23 27 -8 1993 139 | 1994 28 35 22 42 28 16 37 46 41 25 30 23 16 -5 1994 140 | 1995 43 57 29 70 40 19 140 96 31 38 42 27 11 12 1995 141 | 1996 33 26 39 24 31 43 78 20 11 29 32 31 29 118 1996 142 | 1997 45 53 38 55 50 29 77 89 27 50 51 40 28 -2 1997 143 | 1998 61 72 50 80 68 33 90 92 69 61 76 40 27 22 1998 144 | 1999 40 50 30 73 22 32 46 83 75 16 27 47 23 1 1999 145 | 2000 40 50 30 71 23 33 106 77 56 19 27 43 16 36 2000 146 | 147 | 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S 148 | Year Glob NHem SHem -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S Year 149 | 2001 53 64 41 80 40 42 103 82 72 40 40 53 21 50 2001 150 | 2002 61 71 52 83 56 47 124 99 60 52 61 48 22 97 2002 151 | 2003 60 71 49 80 59 41 140 94 52 57 61 47 23 60 2003 152 | 2004 51 65 37 74 52 28 60 91 67 53 51 47 14 -3 2004 153 | 2005 65 81 50 97 58 44 194 116 54 56 59 49 21 79 2005 154 | 2006 59 75 42 93 50 37 159 104 64 50 50 49 19 36 2006 155 | 2007 62 80 44 106 42 45 187 129 66 40 43 48 5 123 2007 156 | 2008 49 61 36 83 34 34 128 101 57 28 40 48 3 51 2008 157 | 2009 59 66 52 69 62 44 111 57 64 60 64 54 8 87 2009 158 | 2010 66 84 49 95 63 41 192 84 71 67 60 58 15 38 2010 159 | 2011 54 66 42 87 34 49 194 88 52 35 32 60 11 92 2011 160 | 2012 56 71 42 90 45 37 159 87 70 41 49 52 16 31 2012 161 | Year Glob NHem SHem 24N 24S 90S 64N 44N 24N EQU 24S 44S 64S 90S Year 162 | -90N -24N -24S -90N -64N -44N -24N -EQU -24S -44S -64S 163 | 164 | -------------------------------------------------------------------------------- /data/GLB.Ts+dSST.txt: -------------------------------------------------------------------------------- 1 | GLOBAL Land-Ocean Temperature Index in 0.01 degrees Celsius base period: 1951-1980 2 | 3 | sources: GHCN-v3 1880-06/2013 + SST: ERSST 1880-06/2013 4 | using elimination of outliers and homogeneity adjustment 5 | Notes: 1950 DJF = Dec 1949 - Feb 1950 ; ***** = missing 6 | 7 | AnnMean 8 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 9 | 1880 -34 -28 -23 -31 -17 -24 -20 -12 -21 -19 -17 -22 -22**** ***** -24 -19 -19 1880 10 | 1881 -14 -16 -2 -2 -2 -25 -11 -7 -17 -23 -27 -18 -14 -14 -17 -2 -14 -22 1881 11 | 1882 3 5 -1 -23 -20 -31 -26 -10 -11 -25 -25 -37 -17 -15 -3 -15 -23 -20 1882 12 | 1883 -38 -38 -12 -20 -20 -7 -2 -13 -19 -19 -28 -20 -20 -21 -38 -17 -8 -22 1883 13 | 1884 -19 -14 -30 -35 -33 -35 -31 -24 -28 -25 -29 -25 -27 -27 -18 -33 -30 -27 1884 14 | 1885 -56 -28 -18 -36 -34 -39 -27 -24 -17 -14 -13 1 -25 -28 -37 -29 -30 -15 1885 15 | 1886 -37 -42 -33 -23 -20 -29 -11 -19 -12 -22 -27 -16 -24 -23 -26 -25 -20 -20 1886 16 | 1887 -58 -42 -25 -32 -28 -25 -18 -28 -23 -32 -28 -40 -32 -30 -39 -28 -24 -28 1887 17 | 1888 -43 -40 -38 -24 -26 -20 -10 -13 -10 -3 -1 -9 -20 -22 -41 -30 -14 -5 1888 18 | 1889 -18 16 8 8 0 -9 -12 -18 -18 -21 -31 -29 -10 -9 -4 5 -13 -23 1889 19 | 1890 -41 -36 -36 -33 -43 -28 -24 -32 -31 -20 -44 -29 -33 -33 -35 -37 -28 -32 1890 20 | 1891 -42 -49 -18 -27 -20 -26 -25 -21 -16 -27 -40 -12 -27 -28 -40 -22 -24 -28 1891 21 | 1892 -35 -14 -36 -38 -34 -30 -34 -28 -20 -17 -48 -43 -32 -29 -21 -36 -31 -29 1892 22 | 1893 -85 -58 -28 -35 -44 -34 -16 -28 -23 -22 -20 -37 -36 -36 -62 -36 -26 -22 1893 23 | 1894 -54 -33 -24 -47 -38 -40 -26 -22 -25 -23 -34 -26 -33 -34 -41 -36 -29 -27 1894 24 | 1895 -50 -49 -29 -24 -30 -26 -19 -18 -10 -13 -16 -20 -25 -26 -42 -28 -21 -13 1895 25 | 1896 -31 -21 -34 -39 -25 -17 -10 -10 -7 3 -17 -13 -18 -19 -24 -32 -12 -7 1896 26 | 1897 -22 -22 -24 -12 -12 -23 -13 -18 -14 -19 -25 -28 -19 -18 -19 -16 -18 -19 1897 27 | 1898 -13 -39 -57 -35 -37 -25 -27 -27 -24 -36 -40 -26 -32 -32 -27 -43 -26 -33 1898 28 | 1899 -21 -39 -36 -21 -26 -36 -20 -14 -7 -7 6 -29 -21 -21 -29 -28 -23 -3 1899 29 | 1900 -38 -3 1 -16 -15 -22 -19 -15 -15 -5 -19 -13 -15 -16 -23 -10 -19 -13 1900 30 | 31 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 32 | 1901 -32 -8 0 -12 -23 -17 -18 -23 -25 -34 -25 -34 -21 -19 -18 -11 -19 -28 1901 33 | 1902 -20 -3 -29 -31 -35 -37 -29 -32 -27 -35 -41 -47 -31 -29 -19 -31 -33 -34 1902 34 | 1903 -26 -1 -20 -38 -40 -46 -36 -42 -44 -47 -44 -53 -37 -36 -25 -33 -42 -45 1903 35 | 1904 -66 -55 -43 -50 -50 -44 -46 -43 -47 -37 -18 -25 -44 -46 -58 -48 -44 -34 1904 36 | 1905 -33 -60 -28 -34 -31 -31 -30 -26 -22 -27 -12 -24 -30 -30 -40 -31 -29 -20 1905 37 | 1906 -30 -33 -15 -7 -24 -26 -32 -25 -30 -28 -48 -21 -27 -27 -29 -16 -28 -35 1906 38 | 1907 -46 -54 -33 -45 -51 -45 -38 -36 -33 -26 -53 -52 -43 -40 -40 -43 -40 -37 1907 39 | 1908 -44 -36 -57 -45 -38 -41 -41 -46 -34 -44 -50 -48 -44 -44 -44 -47 -43 -43 1908 40 | 1909 -65 -46 -50 -56 -55 -51 -41 -30 -35 -40 -33 -55 -47 -46 -53 -54 -41 -36 1909 41 | 1910 -45 -44 -48 -46 -44 -44 -36 -37 -38 -44 -55 -66 -46 -45 -48 -46 -39 -46 1910 42 | 1911 -63 -58 -63 -54 -51 -49 -40 -41 -37 -26 -23 -25 -44 -48 -62 -56 -43 -29 1911 43 | 1912 -29 -18 -41 -28 -29 -31 -47 -59 -54 -63 -46 -49 -41 -39 -24 -33 -46 -54 1912 44 | 1913 -48 -48 -46 -41 -49 -48 -39 -38 -39 -41 -26 -11 -39 -43 -48 -45 -42 -35 1913 45 | 1914 -3 -19 -27 -32 -27 -31 -35 -25 -23 -12 -24 -17 -23 -23 -11 -29 -31 -20 1914 46 | 1915 -25 -9 -16 -4 -16 -20 -11 -20 -18 -25 -13 -23 -17 -16 -17 -12 -17 -19 1915 47 | 1916 -20 -23 -33 -32 -34 -46 -33 -34 -34 -32 -41 -75 -36 -32 -22 -33 -38 -36 1916 48 | 1917 -44 -55 -52 -45 -58 -44 -25 -29 -27 -43 -33 -73 -44 -44 -58 -52 -33 -35 1917 49 | 1918 -44 -36 -26 -45 -41 -34 -29 -32 -22 -10 -22 -39 -32 -34 -51 -37 -32 -18 1918 50 | 1919 -26 -29 -30 -23 -33 -34 -29 -26 -23 -21 -39 -37 -29 -29 -32 -28 -30 -28 1919 51 | 1920 -16 -24 -6 -23 -24 -28 -26 -29 -30 -38 -39 -50 -28 -27 -26 -18 -28 -36 1920 52 | 53 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 54 | 1921 -7 -22 -25 -27 -27 -23 -13 -27 -20 -15 -24 -23 -21 -23 -26 -27 -21 -20 1921 55 | 1922 -35 -43 -19 -24 -32 -33 -27 -33 -32 -33 -19 -23 -30 -30 -34 -25 -31 -28 1922 56 | 1923 -27 -36 -30 -38 -34 -27 -34 -32 -30 -15 -3 -5 -26 -27 -29 -34 -31 -16 1923 57 | 1924 -21 -22 -8 -31 -21 -26 -25 -30 -27 -30 -15 -36 -24 -22 -16 -20 -27 -24 1924 58 | 1925 -33 -34 -23 -22 -31 -35 -29 -20 -17 -21 -2 6 -22 -25 -34 -26 -28 -13 1925 59 | 1926 15 7 12 -12 -23 -24 -21 -15 -10 -10 -6 -25 -9 -7 9 -8 -20 -8 1926 60 | 1927 -23 -16 -34 -29 -19 -24 -17 -22 -9 2 -3 -31 -19 -18 -22 -27 -21 -3 1927 61 | 1928 1 -6 -24 -24 -26 -33 -17 -22 -17 -16 -5 -13 -17 -18 -12 -25 -24 -13 1928 62 | 1929 -39 -52 -30 -36 -32 -38 -34 -27 -21 -11 -10 -49 -32 -29 -35 -33 -33 -14 1929 63 | 1930 -23 -22 -5 -21 -22 -18 -16 -12 -11 -8 15 -3 -12 -16 -31 -16 -16 -2 1930 64 | 1931 -5 -19 -6 -15 -18 -8 -3 -2 -5 3 -6 -4 -7 -7 -9 -13 -4 -3 1931 65 | 1932 16 -15 -14 0 -15 -25 -19 -19 -5 0 -19 -17 -11 -10 -1 -10 -21 -8 1932 66 | 1933 -26 -28 -25 -22 -24 -30 -19 -20 -23 -20 -28 -41 -25 -23 -24 -24 -23 -24 1933 67 | 1934 -19 0 -28 -27 -7 -10 -4 -9 -15 -5 6 1 -10 -13 -20 -21 -8 -5 1934 68 | 1935 -29 17 -9 -30 -25 -19 -14 -16 -13 -2 -24 -17 -15 -14 -4 -21 -16 -13 1935 69 | 1936 -21 -33 -20 -16 -12 -13 0 -6 -3 -1 -1 2 -10 -12 -24 -16 -6 -2 1936 70 | 1937 -4 11 -12 -10 -1 2 1 7 16 13 14 -3 3 3 3 -7 3 14 1937 71 | 1938 11 4 15 12 0 -8 2 2 10 18 12 -13 6 6 4 9 -1 14 1938 72 | 1939 -1 -2 -15 -6 -1 -5 -4 -5 -2 -1 5 41 0 -4 -5 -7 -4 1 1939 73 | 1940 -12 8 9 15 7 4 8 -2 10 6 9 13 6 9 12 10 4 8 1940 74 | 75 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 76 | 1941 5 19 -1 9 10 6 13 5 -8 17 6 8 7 8 12 6 8 5 1941 77 | 1942 27 2 6 8 8 3 -2 -3 -3 0 5 7 5 5 12 7 -1 1 1942 78 | 1943 -12 9 -7 8 4 -7 7 0 5 22 15 19 5 4 1 2 0 14 1943 79 | 1944 30 18 19 11 12 10 11 9 18 17 6 -2 13 15 23 14 10 14 1944 80 | 1945 2 -9 -1 9 -1 -8 -5 16 7 9 1 -16 0 1 -3 2 1 6 1945 81 | 1946 14 7 -4 3 -10 -16 -10 -15 -9 -14 -5 -32 -8 -6 2 -3 -14 -9 1946 82 | 1947 -11 -9 4 3 -7 -8 -7 -9 -13 8 4 -10 -5 -6 -18 0 -8 0 1947 83 | 1948 12 -12 -21 -9 1 -7 -13 -15 -17 -10 -15 -19 -10 -10 -3 -10 -11 -14 1948 84 | 1949 11 -17 -5 -12 -12 -25 -17 -12 -14 -7 -9 -18 -11 -12 -8 -10 -18 -10 1949 85 | 1950 -29 -29 -7 -19 -11 -10 -15 -22 -12 -17 -33 -19 -19 -19 -26 -12 -16 -21 1950 86 | 1951 -33 -43 -20 -9 3 -5 0 7 5 10 -2 12 -6 -9 -32 -9 1 4 1951 87 | 1952 13 12 -9 5 0 -1 7 5 7 -2 -15 -1 2 3 12 -1 3 -3 1952 88 | 1953 11 20 13 20 10 6 0 8 6 8 -2 7 9 8 10 14 5 4 1953 89 | 1954 -22 -9 -13 -15 -19 -14 -17 -14 -7 -1 9 -15 -11 -9 -8 -16 -15 1 1954 90 | 1955 17 -16 -32 -18 -20 -10 -8 8 -10 -3 -26 -31 -12 -11 -5 -23 -3 -13 1955 91 | 1956 -16 -23 -19 -24 -25 -14 -10 -27 -15 -22 -15 -9 -18 -20 -24 -23 -17 -17 1956 92 | 1957 -12 -7 -8 -1 5 16 0 19 9 0 8 16 4 2 -9 -1 12 5 1957 93 | 1958 37 24 9 2 5 -12 0 -8 -7 2 2 -1 4 6 26 6 -7 -1 1958 94 | 1959 8 6 17 14 3 2 4 -1 -5 -9 -9 1 3 3 5 11 2 -7 1959 95 | 1960 -4 12 -36 -16 -11 -3 -6 -1 5 6 -12 16 -4 -5 3 -21 -4 0 1960 96 | 97 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 98 | 1961 5 17 8 12 13 11 0 3 5 0 2 -16 5 8 13 11 4 2 1961 99 | 1962 7 15 10 6 -5 4 1 1 -1 -1 8 0 4 2 2 4 2 2 1962 100 | 1963 -1 20 -15 -7 -5 5 12 24 20 16 17 0 7 7 6 -9 14 18 1963 101 | 1964 -7 -11 -23 -30 -25 -5 -6 -24 -29 -29 -18 -28 -20 -17 -6 -26 -12 -26 1964 102 | 1965 -6 -17 -10 -19 -11 -12 -13 -4 -15 -5 -6 -6 -10 -12 -17 -14 -10 -9 1965 103 | 1966 -16 0 6 -12 -9 0 7 -7 0 -13 0 -4 -4 -4 -7 -5 0 -5 1966 104 | 1967 -5 -19 7 -2 14 -8 1 1 -5 9 -6 -1 -1 -1 -9 6 -2 -1 1967 105 | 1968 -21 -12 26 -2 -11 -5 -7 -8 -17 12 -5 -13 -5 -4 -11 4 -7 -3 1968 106 | 1969 -11 -15 0 16 15 6 -2 2 9 12 14 29 6 3 -13 10 2 12 1969 107 | 1970 10 26 9 9 -3 1 0 -8 11 4 2 -12 4 8 22 5 -2 6 1970 108 | 1971 -1 -18 -16 -8 -3 -16 -9 0 2 -2 -2 -5 -6 -7 -10 -9 -8 -1 1971 109 | 1972 -24 -16 3 -1 1 9 4 18 3 8 3 20 2 0 -15 1 10 5 1972 110 | 1973 27 32 26 26 25 18 10 4 9 13 5 -5 16 18 26 26 11 9 1973 111 | 1974 -14 -26 -4 -10 -2 -6 -4 10 -11 -6 -6 -8 -7 -7 -15 -5 0 -8 1974 112 | 1975 8 7 14 4 15 2 0 -20 -3 -10 -16 -18 -1 -1 2 11 -6 -10 1975 113 | 1976 -3 -8 -22 -9 -22 -15 -9 -16 -9 -29 -8 4 -12 -14 -10 -18 -13 -15 1976 114 | 1977 12 15 21 23 27 25 20 16 -3 0 16 4 15 15 11 24 20 4 1977 115 | 1978 6 10 18 11 3 -2 4 -18 6 0 14 6 5 5 6 11 -5 7 1978 116 | 1979 10 -12 15 8 1 9 -3 9 21 19 24 43 12 9 2 8 5 21 1979 117 | 1980 25 37 26 29 30 14 22 23 18 14 23 15 23 25 35 28 20 18 1980 118 | 119 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 120 | 1981 51 38 46 27 20 26 29 29 12 8 17 36 28 27 35 31 28 12 1981 121 | 1982 4 12 -6 5 11 2 11 5 9 8 10 38 9 9 17 3 6 9 1982 122 | 1983 49 35 38 27 34 17 13 27 35 12 28 13 27 29 41 33 19 25 1983 123 | 1984 26 11 24 6 31 3 13 12 14 11 1 -8 12 14 17 21 10 8 1984 124 | 1985 19 -8 13 5 13 15 -4 10 10 8 5 11 8 7 1 11 7 8 1985 125 | 1986 25 33 25 21 22 11 9 8 -3 9 5 10 15 15 23 23 10 4 1986 126 | 1987 30 41 12 21 21 32 41 21 34 26 20 43 29 26 27 18 31 27 1987 127 | 1988 53 36 44 39 39 38 30 40 36 34 7 28 35 37 44 41 36 26 1988 128 | 1989 11 31 32 27 12 11 29 31 32 28 14 32 24 24 23 24 24 25 1989 129 | 1990 35 35 71 49 40 33 38 27 25 37 40 37 39 39 34 54 33 34 1990 130 | 1991 38 46 32 46 34 50 47 37 43 27 27 30 38 39 41 37 45 32 1991 131 | 1992 41 37 43 21 29 22 10 6 -3 6 0 18 19 20 36 31 12 1 1992 132 | 1993 34 36 33 22 23 19 24 11 6 19 4 17 21 21 29 26 18 10 1993 133 | 1994 27 -2 24 38 25 37 26 19 30 40 42 36 28 27 14 29 27 37 1994 134 | 1995 48 75 43 44 26 42 47 45 29 45 41 28 43 43 53 38 45 39 1995 135 | 1996 25 46 31 33 27 24 34 48 24 19 40 39 33 32 33 30 35 28 1996 136 | 1997 31 36 52 35 36 51 33 39 52 61 61 58 45 44 35 41 41 58 1997 137 | 1998 60 86 62 62 68 74 67 68 45 42 47 55 61 61 68 64 69 44 1998 138 | 1999 47 66 31 33 30 37 40 35 40 40 37 45 40 41 56 32 37 39 1999 139 | 2000 23 56 56 58 39 42 40 42 42 27 31 27 40 42 41 51 42 33 2000 140 | 141 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 142 | 2001 41 46 58 51 57 53 58 49 53 48 68 52 53 51 38 55 53 56 2001 143 | 2002 72 73 89 56 63 55 59 53 62 55 59 42 61 62 66 69 55 59 2002 144 | 2003 72 54 55 51 60 47 53 65 63 73 53 72 60 57 56 56 55 63 2003 145 | 2004 56 66 63 57 40 40 24 41 50 61 70 48 51 53 65 53 35 60 2004 146 | 2005 69 54 67 67 60 63 61 59 74 76 71 64 65 64 57 65 61 74 2005 147 | 2006 53 65 58 45 43 59 49 65 59 67 69 74 59 58 61 49 58 65 2006 148 | 2007 93 65 67 70 63 55 57 57 61 56 53 46 62 64 77 67 56 57 2007 149 | 2008 23 31 69 48 46 43 54 38 58 62 62 51 49 48 33 54 45 61 2008 150 | 2009 56 49 49 56 59 61 66 60 65 58 70 58 59 58 52 55 63 64 2009 151 | 2010 66 74 87 82 70 60 56 59 56 65 75 45 66 67 66 80 58 65 2010 152 | 2011 45 44 58 60 47 53 69 68 52 60 49 46 54 54 45 55 64 53 2011 153 | 2012 36 40 50 59 70 60 51 58 68 71 68 44 56 56 41 60 56 69 2012 154 | 2013 61 52 60 47 55 67****************************** ********* 52 54********** 2013 155 | Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec J-D D-N DJF MAM JJA SON Year 156 | 157 | Divide by 100 to get changes in degrees Celsius (deg-C). 158 | Multiply that result by 1.8(=9/5) to get changes in degrees Fahrenheit (deg-F). 159 | 160 | Best estimate for absolute global mean for 1951-1980 is 14.0 deg-C or 57.2 deg-F, 161 | so add that to the temperature change if you want to use an absolute scale 162 | (this note applies to global annual means only, J-D and D-N !) 163 | 164 | Example -- Table Value : 40 165 | change : 0.40 deg-C or 0.72 deg-F 166 | abs. scale if global annual mean : 14.40 deg-C or 57.92 deg-F 167 | -------------------------------------------------------------------------------- /data/std_atmosphere_wt_function_chan_TLS.txt: -------------------------------------------------------------------------------- 1 | Temperature Weighting Function for Channel TLS 2 | -------------------------------------------------------- 3 | Surface Weight 0.00000 4 | -------------------------------------------------------- 5 | level h(m) T(K) P(pa) PV(pa) WEIGHT 6 | -------------------------------------------------------- 7 | 0 0.00 288.15 101325.00 1193.6130 0.00000 8 | 1 300.00 286.20 97704.60 977.2475 0.00000 9 | 2 600.00 284.25 94213.55 800.1025 0.00000 10 | 3 900.00 282.30 90847.25 655.0686 0.00000 11 | 4 1200.00 280.35 87601.23 536.3248 0.00000 12 | 5 1500.00 278.40 84471.19 439.1056 0.00000 13 | 6 1800.00 276.45 81452.98 359.5092 0.00000 14 | 7 2100.00 274.50 78497.20 294.3413 0.00000 15 | 8 2400.00 272.56 75561.19 240.9863 0.00000 16 | 9 2700.00 270.61 72735.00 197.3029 0.00000 17 | 10 3000.00 268.66 70014.51 161.5379 0.00000 18 | 11 3300.00 266.71 67395.77 132.2561 0.00000 19 | 12 3600.00 264.76 64874.99 108.2821 0.00000 20 | 13 3900.00 262.82 62448.49 88.6539 0.00000 21 | 14 4200.00 260.87 60036.17 72.5837 0.00000 22 | 15 4500.00 258.92 57680.26 59.4265 0.00000 23 | 16 4800.00 256.97 55416.80 48.6543 0.00000 24 | 17 5100.00 255.03 53242.16 39.8348 0.00000 25 | 18 5400.00 253.08 51152.86 32.6139 0.00000 26 | 19 5700.00 251.13 49145.55 26.7020 0.00000 27 | 20 6000.00 249.19 47217.00 21.8618 0.00000 28 | 21 6300.00 247.24 45268.32 17.8989 0.00001 29 | 22 6600.00 245.30 43400.07 14.6544 0.00001 30 | 23 6900.00 243.35 41608.92 11.9980 0.00002 31 | 24 7200.00 241.40 39891.69 9.8231 0.00003 32 | 25 7500.00 239.46 38245.33 8.0425 0.00004 33 | 26 7800.00 237.51 36666.91 6.5846 0.00007 34 | 27 8100.00 235.57 35132.69 5.3910 0.00012 35 | 28 8400.00 233.62 33622.54 4.4138 0.00019 36 | 29 8700.00 231.68 32177.29 3.6137 0.00031 37 | 30 9000.00 229.73 30794.17 2.9587 0.00049 38 | 31 9300.00 227.79 29470.50 2.4224 0.00076 39 | 32 9600.00 225.84 28203.73 1.9833 0.00116 40 | 33 9900.00 223.90 26991.41 1.6238 0.00175 41 | 34 10200.00 222.59 25772.51 1.3294 0.00258 42 | 35 10500.00 221.60 24580.67 1.0884 0.00372 43 | 36 10800.00 220.61 23443.96 0.8911 0.00525 44 | 37 11100.00 219.62 22359.80 0.7296 0.00728 45 | 38 11400.00 218.63 21325.79 0.5973 0.00989 46 | 39 11700.00 217.64 20339.59 0.4891 0.01320 47 | 40 12000.00 216.65 19399.00 0.4004 0.01731 48 | 41 12300.00 216.65 18506.23 0.3278 0.02201 49 | 42 12600.00 216.65 17654.54 0.2684 0.02742 50 | 43 12900.00 216.65 16842.05 0.2198 0.03350 51 | 44 13200.00 216.65 16066.96 0.1799 0.04016 52 | 45 13500.00 216.65 15327.53 0.1473 0.04728 53 | 46 13800.00 216.65 14622.13 0.1206 0.05471 54 | 47 14100.00 216.65 13949.30 0.0987 0.06229 55 | 48 14400.00 216.65 13307.63 0.0808 0.06981 56 | 49 14700.00 216.65 12695.47 0.0662 0.07710 57 | 50 15000.00 216.65 12111.48 0.0542 0.08396 58 | 51 15300.00 216.65 11554.34 0.0444 0.09025 59 | 52 15600.00 216.65 11022.84 0.0363 0.09580 60 | 53 15900.00 216.65 10515.78 0.0297 0.10052 61 | 54 16200.00 216.65 10032.38 0.0243 0.10434 62 | 55 16500.00 216.65 9571.35 0.0199 0.10721 63 | 56 16800.00 216.65 9131.51 0.0163 0.10911 64 | 57 17100.00 216.65 8711.88 0.0134 0.11007 65 | 58 17400.00 216.65 8311.54 0.0109 0.11013 66 | 59 17700.00 216.65 7929.59 0.0090 0.10934 67 | 60 18000.00 216.65 7565.20 0.0073 0.10780 68 | 61 18300.00 216.65 7217.68 0.0060 0.10558 69 | 62 18600.00 216.65 6886.13 0.0049 0.10279 70 | 63 18900.00 216.65 6569.81 0.0040 0.09951 71 | 64 19200.00 216.65 6268.02 0.0033 0.09584 72 | 65 19500.00 216.65 5980.09 0.0027 0.09186 73 | 66 19800.00 216.65 5705.39 0.0022 0.08767 74 | 67 20100.00 216.75 5443.72 0.0018 0.08324 75 | 68 20400.00 217.03 5194.86 0.0015 0.07855 76 | 69 20700.00 217.32 4957.37 0.0012 0.07386 77 | 70 21000.00 217.61 4730.73 0.0010 0.06923 78 | 71 21300.00 217.90 4514.46 0.0008 0.06470 79 | 72 21600.00 218.19 4308.08 0.0007 0.06030 80 | 73 21900.00 218.48 4111.13 0.0005 0.05607 81 | 74 22200.00 218.77 3924.36 0.0004 0.05205 82 | 75 22500.00 219.07 3746.63 0.0004 0.04823 83 | 76 22800.00 219.37 3576.96 0.0003 0.04460 84 | 77 23100.00 219.67 3414.97 0.0002 0.04118 85 | 78 23400.00 219.96 3260.31 0.0002 0.03797 86 | 79 23700.00 220.26 3112.66 0.0002 0.03495 87 | 80 24000.00 220.56 2971.70 0.0001 0.03214 88 | 81 24300.00 220.86 2838.38 0.0001 0.02954 89 | 82 24600.00 221.16 2711.04 0.0001 0.02713 90 | 83 24900.00 221.45 2589.41 0.0001 0.02488 91 | 84 25200.00 221.75 2473.24 0.0001 0.02281 92 | 85 25500.00 222.05 2362.28 0.0000 0.02089 93 | 86 25800.00 222.35 2256.30 0.0000 0.01912 94 | 87 26100.00 222.64 2155.39 0.0000 0.01749 95 | 88 26400.00 222.94 2059.58 0.0000 0.01600 96 | 89 26700.00 223.24 1968.04 0.0000 0.01464 97 | 90 27000.00 223.54 1880.56 0.0000 0.01338 98 | 91 27300.00 223.83 1796.97 0.0000 0.01222 99 | 92 27600.00 224.13 1717.10 0.0000 0.01116 100 | 93 27900.00 224.43 1640.78 0.0000 0.01018 101 | 94 28200.00 224.73 1568.31 0.0000 0.00930 102 | 95 28500.00 225.02 1499.25 0.0000 0.00849 103 | 96 28800.00 225.32 1433.24 0.0000 0.00775 104 | 97 29100.00 225.62 1370.13 0.0000 0.00708 105 | 98 29400.00 225.91 1309.80 0.0000 0.00646 106 | 99 29700.00 226.21 1252.13 0.0000 0.00589 107 | 100 30000.00 226.51 1197.00 0.0000 0.00537 108 | 101 30300.00 227.11 1145.43 0.0000 0.00489 109 | 102 30600.00 227.71 1096.09 0.0000 0.00445 110 | 103 30900.00 228.31 1048.87 0.0000 0.00404 111 | 104 31200.00 228.91 1003.69 0.0000 0.00368 112 | 105 31500.00 229.51 960.45 0.0000 0.00335 113 | 106 31800.00 230.11 919.07 0.0000 0.00304 114 | 107 32100.00 230.71 879.48 0.0000 0.00277 115 | 108 32400.00 231.31 841.59 0.0000 0.00252 116 | 109 32700.00 231.91 805.34 0.0000 0.00229 117 | 110 33000.00 232.51 770.64 0.0000 0.00208 118 | 111 33300.00 233.11 737.44 0.0000 0.00189 119 | 112 33600.00 233.71 705.67 0.0000 0.00172 120 | 113 33900.00 234.31 675.27 0.0000 0.00157 121 | 114 34200.00 234.91 646.18 0.0000 0.00142 122 | 115 34500.00 235.51 618.35 0.0000 0.00129 123 | 116 34800.00 236.11 591.71 0.0000 0.00117 124 | 117 35100.00 236.79 566.67 0.0000 0.00107 125 | 118 35400.00 237.62 543.57 0.0000 0.00098 126 | 119 35700.00 238.45 521.41 0.0000 0.00089 127 | 120 36000.00 239.28 500.16 0.0000 0.00081 128 | 121 36300.00 240.11 479.77 0.0000 0.00073 129 | 122 36600.00 240.94 460.21 0.0000 0.00067 130 | 123 36900.00 241.77 441.45 0.0000 0.00061 131 | 124 37200.00 242.60 423.45 0.0000 0.00056 132 | 125 37500.00 243.43 406.19 0.0000 0.00051 133 | 126 37800.00 244.26 389.63 0.0000 0.00046 134 | 127 38100.00 245.09 373.74 0.0000 0.00042 135 | 128 38400.00 245.92 358.51 0.0000 0.00039 136 | 129 38700.00 246.75 343.89 0.0000 0.00035 137 | 130 39000.00 247.58 329.87 0.0000 0.00032 138 | 131 39300.00 248.41 316.42 0.0000 0.00029 139 | 132 39600.00 249.24 303.53 0.0000 0.00026 140 | 133 39900.00 250.07 291.15 0.0000 0.00024 141 | 134 40200.00 250.90 279.71 0.0000 0.00022 142 | 135 40500.00 251.73 268.93 0.0000 0.00020 143 | 136 40800.00 252.56 258.56 0.0000 0.00018 144 | 137 41100.00 253.39 248.59 0.0000 0.00016 145 | 138 41400.00 254.22 239.00 0.0000 0.00015 146 | 139 41700.00 255.05 229.79 0.0000 0.00014 147 | 140 42000.00 255.88 220.93 0.0000 0.00013 148 | 141 42300.00 256.70 212.41 0.0000 0.00012 149 | 142 42600.00 257.53 204.22 0.0000 0.00011 150 | 143 42900.00 258.36 196.34 0.0000 0.00010 151 | 144 43200.00 259.19 188.77 0.0000 0.00009 152 | 145 43500.00 260.02 181.49 0.0000 0.00008 153 | 146 43800.00 260.85 174.50 0.0000 0.00007 154 | 147 44100.00 261.68 167.77 0.0000 0.00007 155 | 148 44400.00 262.51 161.30 0.0000 0.00006 156 | 149 44700.00 263.34 155.08 0.0000 0.00006 157 | 150 45000.00 264.16 149.10 0.0000 0.00005 158 | 151 45300.00 264.55 143.61 0.0000 0.00005 159 | 152 45600.00 264.94 138.32 0.0000 0.00004 160 | 153 45900.00 265.33 133.23 0.0000 0.00004 161 | 154 46200.00 265.72 128.32 0.0000 0.00004 162 | 155 46500.00 266.11 123.60 0.0000 0.00004 163 | 156 46800.00 266.50 119.04 0.0000 0.00003 164 | 157 47100.00 266.89 114.66 0.0000 0.00003 165 | 158 47400.00 267.28 110.44 0.0000 0.00003 166 | 159 47700.00 267.67 106.37 0.0000 0.00003 167 | 160 48000.00 268.06 102.45 0.0000 0.00002 168 | 161 48300.00 268.44 98.68 0.0000 0.00002 169 | 162 48600.00 268.83 95.05 0.0000 0.00002 170 | 163 48900.00 269.22 91.55 0.0000 0.00002 171 | 164 49200.00 269.61 88.17 0.0000 0.00002 172 | 165 49500.00 270.00 84.93 0.0000 0.00002 173 | 166 49800.00 270.39 81.80 0.0000 0.00001 174 | 167 50100.00 270.45 78.78 0.0000 0.00001 175 | 168 50400.00 269.86 75.86 0.0000 0.00001 176 | 169 50700.00 269.27 73.05 0.0000 0.00001 177 | 170 51000.00 268.67 70.35 0.0000 0.00001 178 | 171 51300.00 268.08 67.74 0.0000 0.00001 179 | 172 51600.00 267.49 65.23 0.0000 0.00001 180 | 173 51900.00 266.90 62.81 0.0000 0.00001 181 | 174 52200.00 266.30 60.49 0.0000 0.00001 182 | 175 52500.00 265.71 58.25 0.0000 0.00001 183 | 176 52800.00 265.12 56.09 0.0000 0.00001 184 | 177 53100.00 264.53 54.01 0.0000 0.00001 185 | 178 53400.00 263.93 52.01 0.0000 0.00001 186 | 179 53700.00 263.34 50.08 0.0000 0.00001 187 | 180 54000.00 262.75 48.23 0.0000 0.00001 188 | 181 54300.00 262.15 46.44 0.0000 0.00001 189 | 182 54600.00 261.56 44.72 0.0000 0.00000 190 | 183 54900.00 260.97 43.06 0.0000 0.00000 191 | 184 55200.00 260.22 41.42 0.0000 0.00000 192 | 185 55500.00 259.40 39.81 0.0000 0.00000 193 | 186 55800.00 258.57 38.26 0.0000 0.00000 194 | 187 56100.00 257.75 36.77 0.0000 0.00000 195 | 188 56400.00 256.92 35.34 0.0000 0.00000 196 | 189 56700.00 256.10 33.97 0.0000 0.00000 197 | 190 57000.00 255.27 32.65 0.0000 0.00000 198 | 191 57300.00 254.45 31.38 0.0000 0.00000 199 | 192 57600.00 253.62 30.16 0.0000 0.00000 200 | 193 57900.00 252.80 28.98 0.0000 0.00000 201 | 194 58200.00 251.97 27.86 0.0000 0.00000 202 | 195 58500.00 251.15 26.77 0.0000 0.00000 203 | 196 58800.00 250.32 25.73 0.0000 0.00000 204 | 197 59100.00 249.50 24.73 0.0000 0.00000 205 | 198 59400.00 248.67 23.77 0.0000 0.00000 206 | 199 59700.00 247.85 22.85 0.0000 0.00000 207 | 200 60000.00 247.02 21.96 0.0000 0.00000 208 | 201 60300.00 246.20 21.06 0.0000 0.00000 209 | 202 60600.00 245.37 20.19 0.0000 0.00000 210 | 203 60900.00 244.55 19.37 0.0000 0.00000 211 | 204 61200.00 243.73 18.57 0.0000 0.00000 212 | 205 61500.00 242.90 17.81 0.0000 0.00000 213 | 206 61800.00 242.08 17.08 0.0000 0.00000 214 | 207 62100.00 241.25 16.38 0.0000 0.00000 215 | 208 62400.00 240.43 15.71 0.0000 0.00000 216 | 209 62700.00 239.61 15.06 0.0000 0.00000 217 | 210 63000.00 238.78 14.45 0.0000 0.00000 218 | 211 63300.00 237.96 13.85 0.0000 0.00000 219 | 212 63600.00 237.14 13.29 0.0000 0.00000 220 | 213 63900.00 236.31 12.74 0.0000 0.00000 221 | 214 64200.00 235.49 12.22 0.0000 0.00000 222 | 215 64500.00 234.66 11.72 0.0000 0.00000 223 | 216 64800.00 233.84 11.24 0.0000 0.00000 224 | 217 65100.00 233.02 10.77 0.0000 0.00000 225 | 218 65400.00 232.20 10.30 0.0000 0.00000 226 | 219 65700.00 231.37 9.86 0.0000 0.00000 227 | 220 66000.00 230.55 9.43 0.0000 0.00000 228 | 221 66300.00 229.73 9.02 0.0000 0.00000 229 | 222 66600.00 228.91 8.63 0.0000 0.00000 230 | 223 66900.00 228.08 8.25 0.0000 0.00000 231 | 224 67200.00 227.26 7.90 0.0000 0.00000 232 | 225 67500.00 226.44 7.55 0.0000 0.00000 233 | 226 67800.00 225.62 7.23 0.0000 0.00000 234 | 227 68100.00 224.79 6.91 0.0000 0.00000 235 | 228 68400.00 223.97 6.61 0.0000 0.00000 236 | 229 68700.00 223.15 6.33 0.0000 0.00000 237 | 230 69000.00 222.33 6.05 0.0000 0.00000 238 | 231 69300.00 221.50 5.79 0.0000 0.00000 239 | 232 69600.00 220.68 5.54 0.0000 0.00000 240 | 233 69900.00 219.86 5.30 0.0000 0.00000 241 | 234 70200.00 219.14 5.06 0.0000 0.00000 242 | 235 70500.00 218.47 4.83 0.0000 0.00000 243 | 236 70800.00 217.80 4.61 0.0000 0.00000 244 | 237 71100.00 217.12 4.40 0.0000 0.00000 245 | 238 71400.00 216.45 4.19 0.0000 0.00000 246 | 239 71700.00 215.78 4.00 0.0000 0.00000 247 | 240 72000.00 215.11 3.82 0.0000 0.00000 248 | 241 72300.00 214.44 3.64 0.0000 0.00000 249 | 242 72600.00 213.77 3.48 0.0000 0.00000 250 | 243 72900.00 213.10 3.32 0.0000 0.00000 251 | 244 73200.00 212.43 3.16 0.0000 0.00000 252 | 245 73500.00 211.75 3.02 0.0000 0.00000 253 | 246 73800.00 211.08 2.88 0.0000 0.00000 254 | 247 74100.00 210.41 2.75 0.0000 0.00000 255 | 248 74400.00 209.74 2.62 0.0000 0.00000 256 | 249 74700.00 209.07 2.50 0.0000 0.00000 257 | 250 75000.00 208.40 2.39 0.0000 0.00000 258 | 251 75300.00 207.81 2.27 0.0000 0.00000 259 | 252 75600.00 207.23 2.16 0.0000 0.00000 260 | 253 75900.00 206.64 2.06 0.0000 0.00000 261 | 254 76200.00 206.06 1.96 0.0000 0.00000 262 | 255 76500.00 205.47 1.87 0.0000 0.00000 263 | 256 76800.00 204.89 1.78 0.0000 0.00000 264 | 257 77100.00 204.30 1.69 0.0000 0.00000 265 | 258 77400.00 203.71 1.61 0.0000 0.00000 266 | 259 77700.00 203.13 1.53 0.0000 0.00000 267 | 260 78000.00 202.54 1.46 0.0000 0.00000 268 | 261 78300.00 201.96 1.39 0.0000 0.00000 269 | 262 78600.00 201.37 1.32 0.0000 0.00000 270 | 263 78900.00 200.79 1.26 0.0000 0.00000 271 | 264 79200.00 200.20 1.20 0.0000 0.00000 272 | 265 79500.00 199.62 1.14 0.0000 0.00000 273 | 266 79800.00 199.03 1.09 0.0000 0.00000 274 | 267 80100.00 198.44 1.03 0.0000 0.00000 275 | 268 80400.00 197.86 0.98 0.0000 0.00000 276 | 269 80700.00 197.27 0.93 0.0000 0.00000 277 | 270 81000.00 196.69 0.89 0.0000 0.00000 278 | 271 81300.00 196.11 0.84 0.0000 0.00000 279 | 272 81600.00 195.52 0.80 0.0000 0.00000 280 | 273 81900.00 194.94 0.76 0.0000 0.00000 281 | 274 82200.00 194.35 0.72 0.0000 0.00000 282 | 275 82500.00 193.77 0.68 0.0000 0.00000 283 | 276 82800.00 193.18 0.65 0.0000 0.00000 284 | 277 83100.00 192.60 0.62 0.0000 0.00000 285 | 278 83400.00 192.01 0.59 0.0000 0.00000 286 | 279 83700.00 191.43 0.56 0.0000 0.00000 287 | 280 84000.00 190.84 0.53 0.0000 0.00000 288 | 281 84300.00 190.26 0.50 0.0000 0.00000 289 | 282 84600.00 189.67 0.48 0.0000 0.00000 290 | 283 84900.00 189.09 0.45 0.0000 0.00000 291 | 284 85200.00 188.81 0.43 0.0000 0.00000 292 | 285 85500.00 188.69 0.41 0.0000 0.00000 293 | 286 85800.00 188.57 0.39 0.0000 0.00000 294 | 287 86100.00 188.45 0.37 0.0000 0.00000 295 | 288 86400.00 188.33 0.35 0.0000 0.00000 296 | 289 86700.00 188.21 0.33 0.0000 0.00000 297 | 290 87000.00 188.08 0.31 0.0000 0.00000 298 | 291 87300.00 187.96 0.30 0.0000 0.00000 299 | 292 87600.00 187.84 0.28 0.0000 0.00000 300 | 293 87900.00 187.72 0.27 0.0000 0.00000 301 | 294 88200.00 187.60 0.25 0.0000 0.00000 302 | 295 88500.00 187.48 0.24 0.0000 0.00000 303 | 296 88800.00 187.36 0.23 0.0000 0.00000 304 | 297 89100.00 187.23 0.22 0.0000 0.00000 305 | 298 89400.00 187.11 0.20 0.0000 0.00000 306 | 299 89700.00 186.99 0.19 0.0000 0.00000 307 | -------------------------------------------------------------------------------- /data/std_atmosphere_wt_function_chan_TTS.txt: -------------------------------------------------------------------------------- 1 | Temperature Weighting Function for Channel TTS 2 | -------------------------------------------------------- 3 | Surface Weight 0.00098 4 | -------------------------------------------------------- 5 | level h(m) T(K) P(pa) PV(pa) WEIGHT 6 | -------------------------------------------------------- 7 | 0 0.00 288.15 101325.00 1193.6130 0.00266 8 | 1 300.00 286.20 97704.60 977.2475 0.00295 9 | 2 600.00 284.25 94213.55 800.1025 0.00341 10 | 3 900.00 282.30 90847.25 655.0686 0.00407 11 | 4 1200.00 280.35 87601.23 536.3248 0.00490 12 | 5 1500.00 278.40 84471.19 439.1056 0.00593 13 | 6 1800.00 276.45 81452.98 359.5092 0.00718 14 | 7 2100.00 274.50 78497.20 294.3413 0.00863 15 | 8 2400.00 272.56 75561.19 240.9863 0.01030 16 | 9 2700.00 270.61 72735.00 197.3029 0.01221 17 | 10 3000.00 268.66 70014.51 161.5379 0.01437 18 | 11 3300.00 266.71 67395.77 132.2561 0.01679 19 | 12 3600.00 264.76 64874.99 108.2821 0.01946 20 | 13 3900.00 262.82 62448.49 88.6539 0.02240 21 | 14 4200.00 260.87 60036.17 72.5837 0.02554 22 | 15 4500.00 258.92 57680.26 59.4265 0.02890 23 | 16 4800.00 256.97 55416.80 48.6543 0.03245 24 | 17 5100.00 255.03 53242.16 39.8348 0.03620 25 | 18 5400.00 253.08 51152.86 32.6139 0.04012 26 | 19 5700.00 251.13 49145.55 26.7020 0.04416 27 | 20 6000.00 249.19 47217.00 21.8618 0.04831 28 | 21 6300.00 247.24 45268.32 17.8989 0.05238 29 | 22 6600.00 245.30 43400.07 14.6544 0.05644 30 | 23 6900.00 243.35 41608.92 11.9980 0.06045 31 | 24 7200.00 241.40 39891.69 9.8231 0.06437 32 | 25 7500.00 239.46 38245.33 8.0425 0.06817 33 | 26 7800.00 237.51 36666.91 6.5846 0.07179 34 | 27 8100.00 235.57 35132.69 5.3910 0.07515 35 | 28 8400.00 233.62 33622.54 4.4138 0.07814 36 | 29 8700.00 231.68 32177.29 3.6137 0.08083 37 | 30 9000.00 229.73 30794.17 2.9587 0.08320 38 | 31 9300.00 227.79 29470.50 2.4224 0.08522 39 | 32 9600.00 225.84 28203.73 1.9833 0.08688 40 | 33 9900.00 223.90 26991.41 1.6238 0.08818 41 | 34 10200.00 222.59 25772.51 1.3294 0.08871 42 | 35 10500.00 221.60 24580.67 1.0884 0.08864 43 | 36 10800.00 220.61 23443.96 0.8911 0.08819 44 | 37 11100.00 219.62 22359.80 0.7296 0.08734 45 | 38 11400.00 218.63 21325.79 0.5973 0.08616 46 | 39 11700.00 217.64 20339.59 0.4891 0.08464 47 | 40 12000.00 216.65 19399.00 0.4004 0.08281 48 | 41 12300.00 216.65 18506.23 0.3278 0.08062 49 | 42 12600.00 216.65 17654.54 0.2684 0.07821 50 | 43 12900.00 216.65 16842.05 0.2198 0.07559 51 | 44 13200.00 216.65 16066.96 0.1799 0.07281 52 | 45 13500.00 216.65 15327.53 0.1473 0.06990 53 | 46 13800.00 216.65 14622.13 0.1206 0.06690 54 | 47 14100.00 216.65 13949.30 0.0987 0.06382 55 | 48 14400.00 216.65 13307.63 0.0808 0.06070 56 | 49 14700.00 216.65 12695.47 0.0662 0.05758 57 | 50 15000.00 216.65 12111.48 0.0542 0.05447 58 | 51 15300.00 216.65 11554.34 0.0444 0.05138 59 | 52 15600.00 216.65 11022.84 0.0363 0.04836 60 | 53 15900.00 216.65 10515.78 0.0297 0.04541 61 | 54 16200.00 216.65 10032.38 0.0243 0.04254 62 | 55 16500.00 216.65 9571.35 0.0199 0.03978 63 | 56 16800.00 216.65 9131.51 0.0163 0.03712 64 | 57 17100.00 216.65 8711.88 0.0134 0.03457 65 | 58 17400.00 216.65 8311.54 0.0109 0.03214 66 | 59 17700.00 216.65 7929.59 0.0090 0.02983 67 | 60 18000.00 216.65 7565.20 0.0073 0.02765 68 | 61 18300.00 216.65 7217.68 0.0060 0.02559 69 | 62 18600.00 216.65 6886.13 0.0049 0.02365 70 | 63 18900.00 216.65 6569.81 0.0040 0.02183 71 | 64 19200.00 216.65 6268.02 0.0033 0.02013 72 | 65 19500.00 216.65 5980.09 0.0027 0.01855 73 | 66 19800.00 216.65 5705.39 0.0022 0.01707 74 | 67 20100.00 216.75 5443.72 0.0018 0.01569 75 | 68 20400.00 217.03 5194.86 0.0015 0.01441 76 | 69 20700.00 217.32 4957.37 0.0012 0.01322 77 | 70 21000.00 217.61 4730.73 0.0010 0.01213 78 | 71 21300.00 217.90 4514.46 0.0008 0.01112 79 | 72 21600.00 218.19 4308.08 0.0007 0.01018 80 | 73 21900.00 218.48 4111.13 0.0005 0.00932 81 | 74 22200.00 218.77 3924.36 0.0004 0.00853 82 | 75 22500.00 219.07 3746.63 0.0004 0.00781 83 | 76 22800.00 219.37 3576.96 0.0003 0.00715 84 | 77 23100.00 219.67 3414.97 0.0002 0.00654 85 | 78 23400.00 219.96 3260.31 0.0002 0.00598 86 | 79 23700.00 220.26 3112.66 0.0002 0.00546 87 | 80 24000.00 220.56 2971.70 0.0001 0.00499 88 | 81 24300.00 220.86 2838.38 0.0001 0.00456 89 | 82 24600.00 221.16 2711.04 0.0001 0.00417 90 | 83 24900.00 221.45 2589.41 0.0001 0.00381 91 | 84 25200.00 221.75 2473.24 0.0001 0.00348 92 | 85 25500.00 222.05 2362.28 0.0000 0.00318 93 | 86 25800.00 222.35 2256.30 0.0000 0.00290 94 | 87 26100.00 222.64 2155.39 0.0000 0.00265 95 | 88 26400.00 222.94 2059.58 0.0000 0.00242 96 | 89 26700.00 223.24 1968.04 0.0000 0.00221 97 | 90 27000.00 223.54 1880.56 0.0000 0.00203 98 | 91 27300.00 223.83 1796.97 0.0000 0.00185 99 | 92 27600.00 224.13 1717.10 0.0000 0.00169 100 | 93 27900.00 224.43 1640.78 0.0000 0.00154 101 | 94 28200.00 224.73 1568.31 0.0000 0.00141 102 | 95 28500.00 225.02 1499.25 0.0000 0.00128 103 | 96 28800.00 225.32 1433.24 0.0000 0.00117 104 | 97 29100.00 225.62 1370.13 0.0000 0.00108 105 | 98 29400.00 225.91 1309.80 0.0000 0.00098 106 | 99 29700.00 226.21 1252.13 0.0000 0.00090 107 | 100 30000.00 226.51 1197.00 0.0000 0.00082 108 | 101 30300.00 227.11 1145.43 0.0000 0.00075 109 | 102 30600.00 227.71 1096.09 0.0000 0.00068 110 | 103 30900.00 228.31 1048.87 0.0000 0.00063 111 | 104 31200.00 228.91 1003.69 0.0000 0.00057 112 | 105 31500.00 229.51 960.45 0.0000 0.00053 113 | 106 31800.00 230.11 919.07 0.0000 0.00048 114 | 107 32100.00 230.71 879.48 0.0000 0.00044 115 | 108 32400.00 231.31 841.59 0.0000 0.00040 116 | 109 32700.00 231.91 805.34 0.0000 0.00037 117 | 110 33000.00 232.51 770.64 0.0000 0.00034 118 | 111 33300.00 233.11 737.44 0.0000 0.00031 119 | 112 33600.00 233.71 705.67 0.0000 0.00028 120 | 113 33900.00 234.31 675.27 0.0000 0.00026 121 | 114 34200.00 234.91 646.18 0.0000 0.00023 122 | 115 34500.00 235.51 618.35 0.0000 0.00021 123 | 116 34800.00 236.11 591.71 0.0000 0.00019 124 | 117 35100.00 236.79 566.67 0.0000 0.00018 125 | 118 35400.00 237.62 543.57 0.0000 0.00016 126 | 119 35700.00 238.45 521.41 0.0000 0.00015 127 | 120 36000.00 239.28 500.16 0.0000 0.00014 128 | 121 36300.00 240.11 479.77 0.0000 0.00013 129 | 122 36600.00 240.94 460.21 0.0000 0.00012 130 | 123 36900.00 241.77 441.45 0.0000 0.00011 131 | 124 37200.00 242.60 423.45 0.0000 0.00010 132 | 125 37500.00 243.43 406.19 0.0000 0.00009 133 | 126 37800.00 244.26 389.63 0.0000 0.00008 134 | 127 38100.00 245.09 373.74 0.0000 0.00008 135 | 128 38400.00 245.92 358.51 0.0000 0.00007 136 | 129 38700.00 246.75 343.89 0.0000 0.00006 137 | 130 39000.00 247.58 329.87 0.0000 0.00006 138 | 131 39300.00 248.41 316.42 0.0000 0.00005 139 | 132 39600.00 249.24 303.53 0.0000 0.00005 140 | 133 39900.00 250.07 291.15 0.0000 0.00004 141 | 134 40200.00 250.90 279.71 0.0000 0.00004 142 | 135 40500.00 251.73 268.93 0.0000 0.00004 143 | 136 40800.00 252.56 258.56 0.0000 0.00004 144 | 137 41100.00 253.39 248.59 0.0000 0.00003 145 | 138 41400.00 254.22 239.00 0.0000 0.00003 146 | 139 41700.00 255.05 229.79 0.0000 0.00003 147 | 140 42000.00 255.88 220.93 0.0000 0.00003 148 | 141 42300.00 256.70 212.41 0.0000 0.00002 149 | 142 42600.00 257.53 204.22 0.0000 0.00002 150 | 143 42900.00 258.36 196.34 0.0000 0.00002 151 | 144 43200.00 259.19 188.77 0.0000 0.00002 152 | 145 43500.00 260.02 181.49 0.0000 0.00002 153 | 146 43800.00 260.85 174.50 0.0000 0.00002 154 | 147 44100.00 261.68 167.77 0.0000 0.00001 155 | 148 44400.00 262.51 161.30 0.0000 0.00001 156 | 149 44700.00 263.34 155.08 0.0000 0.00001 157 | 150 45000.00 264.16 149.10 0.0000 0.00001 158 | 151 45300.00 264.55 143.61 0.0000 0.00001 159 | 152 45600.00 264.94 138.32 0.0000 0.00001 160 | 153 45900.00 265.33 133.23 0.0000 0.00001 161 | 154 46200.00 265.72 128.32 0.0000 0.00001 162 | 155 46500.00 266.11 123.60 0.0000 0.00001 163 | 156 46800.00 266.50 119.04 0.0000 0.00001 164 | 157 47100.00 266.89 114.66 0.0000 0.00001 165 | 158 47400.00 267.28 110.44 0.0000 0.00001 166 | 159 47700.00 267.67 106.37 0.0000 0.00001 167 | 160 48000.00 268.06 102.45 0.0000 0.00001 168 | 161 48300.00 268.44 98.68 0.0000 0.00000 169 | 162 48600.00 268.83 95.05 0.0000 0.00000 170 | 163 48900.00 269.22 91.55 0.0000 0.00000 171 | 164 49200.00 269.61 88.17 0.0000 0.00000 172 | 165 49500.00 270.00 84.93 0.0000 0.00000 173 | 166 49800.00 270.39 81.80 0.0000 0.00000 174 | 167 50100.00 270.45 78.78 0.0000 0.00000 175 | 168 50400.00 269.86 75.86 0.0000 0.00000 176 | 169 50700.00 269.27 73.05 0.0000 0.00000 177 | 170 51000.00 268.67 70.35 0.0000 0.00000 178 | 171 51300.00 268.08 67.74 0.0000 0.00000 179 | 172 51600.00 267.49 65.23 0.0000 0.00000 180 | 173 51900.00 266.90 62.81 0.0000 0.00000 181 | 174 52200.00 266.30 60.49 0.0000 0.00000 182 | 175 52500.00 265.71 58.25 0.0000 0.00000 183 | 176 52800.00 265.12 56.09 0.0000 0.00000 184 | 177 53100.00 264.53 54.01 0.0000 0.00000 185 | 178 53400.00 263.93 52.01 0.0000 0.00000 186 | 179 53700.00 263.34 50.08 0.0000 0.00000 187 | 180 54000.00 262.75 48.23 0.0000 0.00000 188 | 181 54300.00 262.15 46.44 0.0000 0.00000 189 | 182 54600.00 261.56 44.72 0.0000 0.00000 190 | 183 54900.00 260.97 43.06 0.0000 0.00000 191 | 184 55200.00 260.22 41.42 0.0000 0.00000 192 | 185 55500.00 259.40 39.81 0.0000 0.00000 193 | 186 55800.00 258.57 38.26 0.0000 0.00000 194 | 187 56100.00 257.75 36.77 0.0000 0.00000 195 | 188 56400.00 256.92 35.34 0.0000 0.00000 196 | 189 56700.00 256.10 33.97 0.0000 0.00000 197 | 190 57000.00 255.27 32.65 0.0000 0.00000 198 | 191 57300.00 254.45 31.38 0.0000 0.00000 199 | 192 57600.00 253.62 30.16 0.0000 0.00000 200 | 193 57900.00 252.80 28.98 0.0000 0.00000 201 | 194 58200.00 251.97 27.86 0.0000 0.00000 202 | 195 58500.00 251.15 26.77 0.0000 0.00000 203 | 196 58800.00 250.32 25.73 0.0000 0.00000 204 | 197 59100.00 249.50 24.73 0.0000 0.00000 205 | 198 59400.00 248.67 23.77 0.0000 0.00000 206 | 199 59700.00 247.85 22.85 0.0000 0.00000 207 | 200 60000.00 247.02 21.96 0.0000 0.00000 208 | 201 60300.00 246.20 21.06 0.0000 0.00000 209 | 202 60600.00 245.37 20.19 0.0000 0.00000 210 | 203 60900.00 244.55 19.37 0.0000 0.00000 211 | 204 61200.00 243.73 18.57 0.0000 0.00000 212 | 205 61500.00 242.90 17.81 0.0000 0.00000 213 | 206 61800.00 242.08 17.08 0.0000 0.00000 214 | 207 62100.00 241.25 16.38 0.0000 0.00000 215 | 208 62400.00 240.43 15.71 0.0000 0.00000 216 | 209 62700.00 239.61 15.06 0.0000 0.00000 217 | 210 63000.00 238.78 14.45 0.0000 0.00000 218 | 211 63300.00 237.96 13.85 0.0000 0.00000 219 | 212 63600.00 237.14 13.29 0.0000 0.00000 220 | 213 63900.00 236.31 12.74 0.0000 0.00000 221 | 214 64200.00 235.49 12.22 0.0000 0.00000 222 | 215 64500.00 234.66 11.72 0.0000 0.00000 223 | 216 64800.00 233.84 11.24 0.0000 0.00000 224 | 217 65100.00 233.02 10.77 0.0000 0.00000 225 | 218 65400.00 232.20 10.30 0.0000 0.00000 226 | 219 65700.00 231.37 9.86 0.0000 0.00000 227 | 220 66000.00 230.55 9.43 0.0000 0.00000 228 | 221 66300.00 229.73 9.02 0.0000 0.00000 229 | 222 66600.00 228.91 8.63 0.0000 0.00000 230 | 223 66900.00 228.08 8.25 0.0000 0.00000 231 | 224 67200.00 227.26 7.90 0.0000 0.00000 232 | 225 67500.00 226.44 7.55 0.0000 0.00000 233 | 226 67800.00 225.62 7.23 0.0000 0.00000 234 | 227 68100.00 224.79 6.91 0.0000 0.00000 235 | 228 68400.00 223.97 6.61 0.0000 0.00000 236 | 229 68700.00 223.15 6.33 0.0000 0.00000 237 | 230 69000.00 222.33 6.05 0.0000 0.00000 238 | 231 69300.00 221.50 5.79 0.0000 0.00000 239 | 232 69600.00 220.68 5.54 0.0000 0.00000 240 | 233 69900.00 219.86 5.30 0.0000 0.00000 241 | 234 70200.00 219.14 5.06 0.0000 0.00000 242 | 235 70500.00 218.47 4.83 0.0000 0.00000 243 | 236 70800.00 217.80 4.61 0.0000 0.00000 244 | 237 71100.00 217.12 4.40 0.0000 0.00000 245 | 238 71400.00 216.45 4.19 0.0000 0.00000 246 | 239 71700.00 215.78 4.00 0.0000 0.00000 247 | 240 72000.00 215.11 3.82 0.0000 0.00000 248 | 241 72300.00 214.44 3.64 0.0000 0.00000 249 | 242 72600.00 213.77 3.48 0.0000 0.00000 250 | 243 72900.00 213.10 3.32 0.0000 0.00000 251 | 244 73200.00 212.43 3.16 0.0000 0.00000 252 | 245 73500.00 211.75 3.02 0.0000 0.00000 253 | 246 73800.00 211.08 2.88 0.0000 0.00000 254 | 247 74100.00 210.41 2.75 0.0000 0.00000 255 | 248 74400.00 209.74 2.62 0.0000 0.00000 256 | 249 74700.00 209.07 2.50 0.0000 0.00000 257 | 250 75000.00 208.40 2.39 0.0000 0.00000 258 | 251 75300.00 207.81 2.27 0.0000 0.00000 259 | 252 75600.00 207.23 2.16 0.0000 0.00000 260 | 253 75900.00 206.64 2.06 0.0000 0.00000 261 | 254 76200.00 206.06 1.96 0.0000 0.00000 262 | 255 76500.00 205.47 1.87 0.0000 0.00000 263 | 256 76800.00 204.89 1.78 0.0000 0.00000 264 | 257 77100.00 204.30 1.69 0.0000 0.00000 265 | 258 77400.00 203.71 1.61 0.0000 0.00000 266 | 259 77700.00 203.13 1.53 0.0000 0.00000 267 | 260 78000.00 202.54 1.46 0.0000 0.00000 268 | 261 78300.00 201.96 1.39 0.0000 0.00000 269 | 262 78600.00 201.37 1.32 0.0000 0.00000 270 | 263 78900.00 200.79 1.26 0.0000 0.00000 271 | 264 79200.00 200.20 1.20 0.0000 0.00000 272 | 265 79500.00 199.62 1.14 0.0000 0.00000 273 | 266 79800.00 199.03 1.09 0.0000 0.00000 274 | 267 80100.00 198.44 1.03 0.0000 0.00000 275 | 268 80400.00 197.86 0.98 0.0000 0.00000 276 | 269 80700.00 197.27 0.93 0.0000 0.00000 277 | 270 81000.00 196.69 0.89 0.0000 0.00000 278 | 271 81300.00 196.11 0.84 0.0000 0.00000 279 | 272 81600.00 195.52 0.80 0.0000 0.00000 280 | 273 81900.00 194.94 0.76 0.0000 0.00000 281 | 274 82200.00 194.35 0.72 0.0000 0.00000 282 | 275 82500.00 193.77 0.68 0.0000 0.00000 283 | 276 82800.00 193.18 0.65 0.0000 0.00000 284 | 277 83100.00 192.60 0.62 0.0000 0.00000 285 | 278 83400.00 192.01 0.59 0.0000 0.00000 286 | 279 83700.00 191.43 0.56 0.0000 0.00000 287 | 280 84000.00 190.84 0.53 0.0000 0.00000 288 | 281 84300.00 190.26 0.50 0.0000 0.00000 289 | 282 84600.00 189.67 0.48 0.0000 0.00000 290 | 283 84900.00 189.09 0.45 0.0000 0.00000 291 | 284 85200.00 188.81 0.43 0.0000 0.00000 292 | 285 85500.00 188.69 0.41 0.0000 0.00000 293 | 286 85800.00 188.57 0.39 0.0000 0.00000 294 | 287 86100.00 188.45 0.37 0.0000 0.00000 295 | 288 86400.00 188.33 0.35 0.0000 0.00000 296 | 289 86700.00 188.21 0.33 0.0000 0.00000 297 | 290 87000.00 188.08 0.31 0.0000 0.00000 298 | 291 87300.00 187.96 0.30 0.0000 0.00000 299 | 292 87600.00 187.84 0.28 0.0000 0.00000 300 | 293 87900.00 187.72 0.27 0.0000 0.00000 301 | 294 88200.00 187.60 0.25 0.0000 0.00000 302 | 295 88500.00 187.48 0.24 0.0000 0.00000 303 | 296 88800.00 187.36 0.23 0.0000 0.00000 304 | 297 89100.00 187.23 0.22 0.0000 0.00000 305 | 298 89400.00 187.11 0.20 0.0000 0.00000 306 | 299 89700.00 186.99 0.19 0.0000 0.00000 307 | -------------------------------------------------------------------------------- /data/std_atmosphere_wt_function_chan_tlt_land.txt: -------------------------------------------------------------------------------- 1 | Temperature Weighting Function for Channel TLT 2 | Surface Type: land 3 | -------------------------------------------------------- 4 | Surface Weight 0.15104 5 | -------------------------------------------------------- 6 | level h(m) T(K) P(pa) PV(pa) Weight 7 | -------------------------------------------------------- 8 | 0 0.00 288.15 101325.00 1193.6130 0.12104 9 | 1 300.00 286.20 97704.60 977.2475 0.12234 10 | 2 600.00 284.25 94213.55 800.1025 0.12372 11 | 3 900.00 282.30 90847.25 655.0686 0.12507 12 | 4 1200.00 280.35 87601.23 536.3248 0.12619 13 | 5 1500.00 278.40 84471.19 439.1056 0.12698 14 | 6 1800.00 276.45 81452.98 359.5092 0.12731 15 | 7 2100.00 274.50 78497.20 294.3413 0.12703 16 | 8 2400.00 272.56 75561.19 240.9863 0.12592 17 | 9 2700.00 270.61 72735.00 197.3029 0.12422 18 | 10 3000.00 268.66 70014.51 161.5379 0.12192 19 | 11 3300.00 266.71 67395.77 132.2561 0.11903 20 | 12 3600.00 264.76 64874.99 108.2821 0.11563 21 | 13 3900.00 262.82 62448.49 88.6539 0.11169 22 | 14 4200.00 260.87 60036.17 72.5837 0.10712 23 | 15 4500.00 258.92 57680.26 59.4265 0.10209 24 | 16 4800.00 256.97 55416.80 48.6543 0.09676 25 | 17 5100.00 255.03 53242.16 39.8348 0.09119 26 | 18 5400.00 253.08 51152.86 32.6139 0.08551 27 | 19 5700.00 251.13 49145.55 26.7020 0.07974 28 | 20 6000.00 249.19 47217.00 21.8618 0.07394 29 | 21 6300.00 247.24 45268.32 17.8989 0.06801 30 | 22 6600.00 245.30 43400.07 14.6544 0.06220 31 | 23 6900.00 243.35 41608.92 11.9980 0.05657 32 | 24 7200.00 241.40 39891.69 9.8231 0.05118 33 | 25 7500.00 239.46 38245.33 8.0425 0.04605 34 | 26 7800.00 237.51 36666.91 6.5846 0.04118 35 | 27 8100.00 235.57 35132.69 5.3910 0.03660 36 | 28 8400.00 233.62 33622.54 4.4138 0.03222 37 | 29 8700.00 231.68 32177.29 3.6137 0.02820 38 | 30 9000.00 229.73 30794.17 2.9587 0.02451 39 | 31 9300.00 227.79 29470.50 2.4224 0.02116 40 | 32 9600.00 225.84 28203.73 1.9833 0.01809 41 | 33 9900.00 223.90 26991.41 1.6238 0.01533 42 | 34 10200.00 222.59 25772.51 1.3294 0.01283 43 | 35 10500.00 221.60 24580.67 1.0884 0.01056 44 | 36 10800.00 220.61 23443.96 0.8911 0.00857 45 | 37 11100.00 219.62 22359.80 0.7296 0.00685 46 | 38 11400.00 218.63 21325.79 0.5973 0.00538 47 | 39 11700.00 217.64 20339.59 0.4891 0.00409 48 | 40 12000.00 216.65 19399.00 0.4004 0.00298 49 | 41 12300.00 216.65 18506.23 0.3278 0.00206 50 | 42 12600.00 216.65 17654.54 0.2684 0.00127 51 | 43 12900.00 216.65 16842.05 0.2198 0.00063 52 | 44 13200.00 216.65 16066.96 0.1799 0.00009 53 | 45 13500.00 216.65 15327.53 0.1473 -0.00037 54 | 46 13800.00 216.65 14622.13 0.1206 -0.00075 55 | 47 14100.00 216.65 13949.30 0.0987 -0.00102 56 | 48 14400.00 216.65 13307.63 0.0808 -0.00126 57 | 49 14700.00 216.65 12695.47 0.0662 -0.00143 58 | 50 15000.00 216.65 12111.48 0.0542 -0.00156 59 | 51 15300.00 216.65 11554.34 0.0444 -0.00164 60 | 52 15600.00 216.65 11022.84 0.0363 -0.00170 61 | 53 15900.00 216.65 10515.78 0.0297 -0.00174 62 | 54 16200.00 216.65 10032.38 0.0243 -0.00174 63 | 55 16500.00 216.65 9571.35 0.0199 -0.00171 64 | 56 16800.00 216.65 9131.51 0.0163 -0.00168 65 | 57 17100.00 216.65 8711.88 0.0134 -0.00166 66 | 58 17400.00 216.65 8311.54 0.0109 -0.00161 67 | 59 17700.00 216.65 7929.59 0.0090 -0.00156 68 | 60 18000.00 216.65 7565.20 0.0073 -0.00148 69 | 61 18300.00 216.65 7217.68 0.0060 -0.00141 70 | 62 18600.00 216.65 6886.13 0.0049 -0.00135 71 | 63 18900.00 216.65 6569.81 0.0040 -0.00129 72 | 64 19200.00 216.65 6268.02 0.0033 -0.00120 73 | 65 19500.00 216.65 5980.09 0.0027 -0.00113 74 | 66 19800.00 216.65 5705.39 0.0022 -0.00108 75 | 67 20100.00 216.75 5443.72 0.0018 -0.00100 76 | 68 20400.00 217.03 5194.86 0.0015 -0.00093 77 | 69 20700.00 217.32 4957.37 0.0012 -0.00088 78 | 70 21000.00 217.61 4730.73 0.0010 -0.00083 79 | 71 21300.00 217.90 4514.46 0.0008 -0.00077 80 | 72 21600.00 218.19 4308.08 0.0007 -0.00071 81 | 73 21900.00 218.48 4111.13 0.0005 -0.00066 82 | 74 22200.00 218.77 3924.36 0.0004 -0.00060 83 | 75 22500.00 219.07 3746.63 0.0004 -0.00057 84 | 76 22800.00 219.37 3576.96 0.0003 -0.00052 85 | 77 23100.00 219.67 3414.97 0.0002 -0.00047 86 | 78 23400.00 219.96 3260.31 0.0002 -0.00044 87 | 79 23700.00 220.26 3112.66 0.0002 -0.00040 88 | 80 24000.00 220.56 2971.70 0.0001 -0.00038 89 | 81 24300.00 220.86 2838.38 0.0001 -0.00033 90 | 82 24600.00 221.16 2711.04 0.0001 -0.00030 91 | 83 24900.00 221.45 2589.41 0.0001 -0.00029 92 | 84 25200.00 221.75 2473.24 0.0001 -0.00027 93 | 85 25500.00 222.05 2362.28 0.0000 -0.00025 94 | 86 25800.00 222.35 2256.30 0.0000 -0.00022 95 | 87 26100.00 222.64 2155.39 0.0000 -0.00021 96 | 88 26400.00 222.94 2059.58 0.0000 -0.00018 97 | 89 26700.00 223.24 1968.04 0.0000 -0.00019 98 | 90 27000.00 223.54 1880.56 0.0000 -0.00016 99 | 91 27300.00 223.83 1796.97 0.0000 -0.00013 100 | 92 27600.00 224.13 1717.10 0.0000 -0.00011 101 | 93 27900.00 224.43 1640.78 0.0000 -0.00012 102 | 94 28200.00 224.73 1568.31 0.0000 -0.00012 103 | 95 28500.00 225.02 1499.25 0.0000 -0.00010 104 | 96 28800.00 225.32 1433.24 0.0000 -0.00008 105 | 97 29100.00 225.62 1370.13 0.0000 -0.00009 106 | 98 29400.00 225.91 1309.80 0.0000 -0.00007 107 | 99 29700.00 226.21 1252.13 0.0000 -0.00008 108 | 100 30000.00 226.51 1197.00 0.0000 -0.00007 109 | 101 30300.00 227.11 1145.43 0.0000 -0.00007 110 | 102 30600.00 227.71 1096.09 0.0000 -0.00007 111 | 103 30900.00 228.31 1048.87 0.0000 -0.00005 112 | 104 31200.00 228.91 1003.69 0.0000 -0.00006 113 | 105 31500.00 229.51 960.45 0.0000 -0.00004 114 | 106 31800.00 230.11 919.07 0.0000 -0.00003 115 | 107 32100.00 230.71 879.48 0.0000 -0.00003 116 | 108 32400.00 231.31 841.59 0.0000 -0.00004 117 | 109 32700.00 231.91 805.34 0.0000 -0.00003 118 | 110 33000.00 232.51 770.64 0.0000 -0.00004 119 | 111 33300.00 233.11 737.44 0.0000 -0.00004 120 | 112 33600.00 233.71 705.67 0.0000 -0.00003 121 | 113 33900.00 234.31 675.27 0.0000 -0.00004 122 | 114 34200.00 234.91 646.18 0.0000 -0.00001 123 | 115 34500.00 235.51 618.35 0.0000 -0.00001 124 | 116 34800.00 236.11 591.71 0.0000 -0.00001 125 | 117 35100.00 236.79 566.67 0.0000 -0.00002 126 | 118 35400.00 237.62 543.57 0.0000 -0.00001 127 | 119 35700.00 238.45 521.41 0.0000 0.00000 128 | 120 36000.00 239.28 500.16 0.0000 -0.00002 129 | 121 36300.00 240.11 479.77 0.0000 -0.00001 130 | 122 36600.00 240.94 460.21 0.0000 -0.00001 131 | 123 36900.00 241.77 441.45 0.0000 0.00000 132 | 124 37200.00 242.60 423.45 0.0000 -0.00000 133 | 125 37500.00 243.43 406.19 0.0000 -0.00001 134 | 126 37800.00 244.26 389.63 0.0000 0.00000 135 | 127 38100.00 245.09 373.74 0.0000 -0.00002 136 | 128 38400.00 245.92 358.51 0.0000 -0.00002 137 | 129 38700.00 246.75 343.89 0.0000 0.00002 138 | 130 39000.00 247.58 329.87 0.0000 -0.00000 139 | 131 39300.00 248.41 316.42 0.0000 -0.00002 140 | 132 39600.00 249.24 303.53 0.0000 -0.00001 141 | 133 39900.00 250.07 291.15 0.0000 0.00001 142 | 134 40200.00 250.90 279.71 0.0000 0.00001 143 | 135 40500.00 251.73 268.93 0.0000 0.00001 144 | 136 40800.00 252.56 258.56 0.0000 0.00000 145 | 137 41100.00 253.39 248.59 0.0000 -0.00002 146 | 138 41400.00 254.22 239.00 0.0000 -0.00002 147 | 139 41700.00 255.05 229.79 0.0000 -0.00001 148 | 140 42000.00 255.88 220.93 0.0000 -0.00001 149 | 141 42300.00 256.70 212.41 0.0000 -0.00001 150 | 142 42600.00 257.53 204.22 0.0000 -0.00001 151 | 143 42900.00 258.36 196.34 0.0000 0.00001 152 | 144 43200.00 259.19 188.77 0.0000 0.00001 153 | 145 43500.00 260.02 181.49 0.0000 0.00001 154 | 146 43800.00 260.85 174.50 0.0000 0.00001 155 | 147 44100.00 261.68 167.77 0.0000 0.00001 156 | 148 44400.00 262.51 161.30 0.0000 0.00001 157 | 149 44700.00 263.34 155.08 0.0000 0.00001 158 | 150 45000.00 264.16 149.10 0.0000 -0.00001 159 | 151 45300.00 264.55 143.61 0.0000 -0.00001 160 | 152 45600.00 264.94 138.32 0.0000 -0.00003 161 | 153 45900.00 265.33 133.23 0.0000 -0.00003 162 | 154 46200.00 265.72 128.32 0.0000 -0.00001 163 | 155 46500.00 266.11 123.60 0.0000 -0.00001 164 | 156 46800.00 266.50 119.04 0.0000 -0.00001 165 | 157 47100.00 266.89 114.66 0.0000 0.00000 166 | 158 47400.00 267.28 110.44 0.0000 0.00000 167 | 159 47700.00 267.67 106.37 0.0000 0.00000 168 | 160 48000.00 268.06 102.45 0.0000 0.00000 169 | 161 48300.00 268.44 98.68 0.0000 0.00000 170 | 162 48600.00 268.83 95.05 0.0000 0.00000 171 | 163 48900.00 269.22 91.55 0.0000 0.00000 172 | 164 49200.00 269.61 88.17 0.0000 0.00000 173 | 165 49500.00 270.00 84.93 0.0000 0.00000 174 | 166 49800.00 270.39 81.80 0.0000 0.00000 175 | 167 50100.00 270.45 78.78 0.0000 0.00000 176 | 168 50400.00 269.86 75.86 0.0000 0.00000 177 | 169 50700.00 269.27 73.05 0.0000 0.00000 178 | 170 51000.00 268.67 70.35 0.0000 0.00000 179 | 171 51300.00 268.08 67.74 0.0000 0.00000 180 | 172 51600.00 267.49 65.23 0.0000 0.00000 181 | 173 51900.00 266.90 62.81 0.0000 0.00000 182 | 174 52200.00 266.30 60.49 0.0000 0.00000 183 | 175 52500.00 265.71 58.25 0.0000 0.00000 184 | 176 52800.00 265.12 56.09 0.0000 0.00000 185 | 177 53100.00 264.53 54.01 0.0000 0.00000 186 | 178 53400.00 263.93 52.01 0.0000 0.00000 187 | 179 53700.00 263.34 50.08 0.0000 0.00000 188 | 180 54000.00 262.75 48.23 0.0000 0.00000 189 | 181 54300.00 262.15 46.44 0.0000 0.00000 190 | 182 54600.00 261.56 44.72 0.0000 0.00000 191 | 183 54900.00 260.97 43.06 0.0000 0.00000 192 | 184 55200.00 260.22 41.42 0.0000 0.00000 193 | 185 55500.00 259.40 39.81 0.0000 0.00000 194 | 186 55800.00 258.57 38.26 0.0000 0.00000 195 | 187 56100.00 257.75 36.77 0.0000 0.00000 196 | 188 56400.00 256.92 35.34 0.0000 0.00000 197 | 189 56700.00 256.10 33.97 0.0000 0.00000 198 | 190 57000.00 255.27 32.65 0.0000 0.00000 199 | 191 57300.00 254.45 31.38 0.0000 0.00000 200 | 192 57600.00 253.62 30.16 0.0000 0.00000 201 | 193 57900.00 252.80 28.98 0.0000 0.00000 202 | 194 58200.00 251.97 27.86 0.0000 0.00000 203 | 195 58500.00 251.15 26.77 0.0000 0.00000 204 | 196 58800.00 250.32 25.73 0.0000 0.00000 205 | 197 59100.00 249.50 24.73 0.0000 0.00000 206 | 198 59400.00 248.67 23.77 0.0000 0.00000 207 | 199 59700.00 247.85 22.85 0.0000 0.00000 208 | 200 60000.00 247.02 21.96 0.0000 0.00000 209 | 201 60300.00 246.20 21.06 0.0000 0.00000 210 | 202 60600.00 245.37 20.19 0.0000 0.00000 211 | 203 60900.00 244.55 19.37 0.0000 0.00000 212 | 204 61200.00 243.73 18.57 0.0000 0.00000 213 | 205 61500.00 242.90 17.81 0.0000 0.00000 214 | 206 61800.00 242.08 17.08 0.0000 0.00000 215 | 207 62100.00 241.25 16.38 0.0000 0.00000 216 | 208 62400.00 240.43 15.71 0.0000 0.00000 217 | 209 62700.00 239.61 15.06 0.0000 0.00000 218 | 210 63000.00 238.78 14.45 0.0000 0.00000 219 | 211 63300.00 237.96 13.85 0.0000 0.00000 220 | 212 63600.00 237.14 13.29 0.0000 0.00000 221 | 213 63900.00 236.31 12.74 0.0000 0.00000 222 | 214 64200.00 235.49 12.22 0.0000 0.00000 223 | 215 64500.00 234.66 11.72 0.0000 0.00000 224 | 216 64800.00 233.84 11.24 0.0000 0.00000 225 | 217 65100.00 233.02 10.77 0.0000 0.00000 226 | 218 65400.00 232.20 10.30 0.0000 0.00000 227 | 219 65700.00 231.37 9.86 0.0000 0.00000 228 | 220 66000.00 230.55 9.43 0.0000 0.00000 229 | 221 66300.00 229.73 9.02 0.0000 0.00000 230 | 222 66600.00 228.91 8.63 0.0000 0.00000 231 | 223 66900.00 228.08 8.25 0.0000 0.00000 232 | 224 67200.00 227.26 7.90 0.0000 0.00000 233 | 225 67500.00 226.44 7.55 0.0000 0.00000 234 | 226 67800.00 225.62 7.23 0.0000 0.00000 235 | 227 68100.00 224.79 6.91 0.0000 0.00000 236 | 228 68400.00 223.97 6.61 0.0000 0.00000 237 | 229 68700.00 223.15 6.33 0.0000 0.00000 238 | 230 69000.00 222.33 6.05 0.0000 0.00000 239 | 231 69300.00 221.50 5.79 0.0000 0.00000 240 | 232 69600.00 220.68 5.54 0.0000 0.00000 241 | 233 69900.00 219.86 5.30 0.0000 0.00000 242 | 234 70200.00 219.14 5.06 0.0000 0.00000 243 | 235 70500.00 218.47 4.83 0.0000 0.00000 244 | 236 70800.00 217.80 4.61 0.0000 0.00000 245 | 237 71100.00 217.12 4.40 0.0000 0.00000 246 | 238 71400.00 216.45 4.19 0.0000 0.00000 247 | 239 71700.00 215.78 4.00 0.0000 0.00000 248 | 240 72000.00 215.11 3.82 0.0000 0.00000 249 | 241 72300.00 214.44 3.64 0.0000 0.00000 250 | 242 72600.00 213.77 3.48 0.0000 0.00000 251 | 243 72900.00 213.10 3.32 0.0000 0.00000 252 | 244 73200.00 212.43 3.16 0.0000 0.00000 253 | 245 73500.00 211.75 3.02 0.0000 0.00000 254 | 246 73800.00 211.08 2.88 0.0000 0.00000 255 | 247 74100.00 210.41 2.75 0.0000 0.00000 256 | 248 74400.00 209.74 2.62 0.0000 0.00000 257 | 249 74700.00 209.07 2.50 0.0000 0.00000 258 | 250 75000.00 208.40 2.39 0.0000 0.00000 259 | 251 75300.00 207.81 2.27 0.0000 0.00000 260 | 252 75600.00 207.23 2.16 0.0000 0.00000 261 | 253 75900.00 206.64 2.06 0.0000 0.00000 262 | 254 76200.00 206.06 1.96 0.0000 0.00000 263 | 255 76500.00 205.47 1.87 0.0000 0.00000 264 | 256 76800.00 204.89 1.78 0.0000 0.00000 265 | 257 77100.00 204.30 1.69 0.0000 0.00000 266 | 258 77400.00 203.71 1.61 0.0000 0.00000 267 | 259 77700.00 203.13 1.53 0.0000 0.00000 268 | 260 78000.00 202.54 1.46 0.0000 0.00000 269 | 261 78300.00 201.96 1.39 0.0000 0.00000 270 | 262 78600.00 201.37 1.32 0.0000 0.00000 271 | 263 78900.00 200.79 1.26 0.0000 0.00000 272 | 264 79200.00 200.20 1.20 0.0000 0.00000 273 | 265 79500.00 199.62 1.14 0.0000 0.00000 274 | 266 79800.00 199.03 1.09 0.0000 0.00000 275 | 267 80100.00 198.44 1.03 0.0000 0.00000 276 | 268 80400.00 197.86 0.98 0.0000 0.00000 277 | 269 80700.00 197.27 0.93 0.0000 0.00000 278 | 270 81000.00 196.69 0.89 0.0000 0.00000 279 | 271 81300.00 196.11 0.84 0.0000 0.00000 280 | 272 81600.00 195.52 0.80 0.0000 0.00000 281 | 273 81900.00 194.94 0.76 0.0000 0.00000 282 | 274 82200.00 194.35 0.72 0.0000 0.00000 283 | 275 82500.00 193.77 0.68 0.0000 0.00000 284 | 276 82800.00 193.18 0.65 0.0000 0.00000 285 | 277 83100.00 192.60 0.62 0.0000 0.00000 286 | 278 83400.00 192.01 0.59 0.0000 0.00000 287 | 279 83700.00 191.43 0.56 0.0000 0.00000 288 | 280 84000.00 190.84 0.53 0.0000 0.00000 289 | 281 84300.00 190.26 0.50 0.0000 0.00000 290 | 282 84600.00 189.67 0.48 0.0000 0.00000 291 | 283 84900.00 189.09 0.45 0.0000 0.00000 292 | 284 85200.00 188.81 0.43 0.0000 0.00000 293 | 285 85500.00 188.69 0.41 0.0000 0.00000 294 | 286 85800.00 188.57 0.39 0.0000 0.00000 295 | 287 86100.00 188.45 0.37 0.0000 0.00000 296 | 288 86400.00 188.33 0.35 0.0000 0.00000 297 | 289 86700.00 188.21 0.33 0.0000 0.00000 298 | 290 87000.00 188.08 0.31 0.0000 0.00000 299 | 291 87300.00 187.96 0.30 0.0000 0.00000 300 | 292 87600.00 187.84 0.28 0.0000 0.00000 301 | 293 87900.00 187.72 0.27 0.0000 0.00000 302 | 294 88200.00 187.60 0.25 0.0000 0.00000 303 | 295 88500.00 187.48 0.24 0.0000 0.00000 304 | 296 88800.00 187.36 0.23 0.0000 0.00000 305 | 297 89100.00 187.23 0.22 0.0000 0.00000 306 | 298 89400.00 187.11 0.20 0.0000 0.00000 307 | 299 89700.00 186.99 0.19 0.0000 0.00000 308 | -------------------------------------------------------------------------------- /data/std_atmosphere_wt_function_chan_tlt_ocean.txt: -------------------------------------------------------------------------------- 1 | Temperature Weighting Function for Channel TLT 2 | Surface Type: ocean 3 | -------------------------------------------------------- 4 | Surface Weight 0.11863 5 | -------------------------------------------------------- 6 | level h(m) T(K) P(pa) PV(pa) Weight 7 | -------------------------------------------------------- 8 | 0 0.00 288.15 101325.00 1193.6130 0.13702 9 | 1 300.00 286.20 97704.60 977.2475 0.13534 10 | 2 600.00 284.25 94213.55 800.1025 0.13446 11 | 3 900.00 282.30 90847.25 655.0686 0.13401 12 | 4 1200.00 280.35 87601.23 536.3248 0.13368 13 | 5 1500.00 278.40 84471.19 439.1056 0.13332 14 | 6 1800.00 276.45 81452.98 359.5092 0.13269 15 | 7 2100.00 274.50 78497.20 294.3413 0.13164 16 | 8 2400.00 272.56 75561.19 240.9863 0.12987 17 | 9 2700.00 270.61 72735.00 197.3029 0.12764 18 | 10 3000.00 268.66 70014.51 161.5379 0.12491 19 | 11 3300.00 266.71 67395.77 132.2561 0.12163 20 | 12 3600.00 264.76 64874.99 108.2821 0.11788 21 | 13 3900.00 262.82 62448.49 88.6539 0.11371 22 | 14 4200.00 260.87 60036.17 72.5837 0.10887 23 | 15 4500.00 258.92 57680.26 59.4265 0.10363 24 | 16 4800.00 256.97 55416.80 48.6543 0.09812 25 | 17 5100.00 255.03 53242.16 39.8348 0.09242 26 | 18 5400.00 253.08 51152.86 32.6139 0.08659 27 | 19 5700.00 251.13 49145.55 26.7020 0.08072 28 | 20 6000.00 249.19 47217.00 21.8618 0.07483 29 | 21 6300.00 247.24 45268.32 17.8989 0.06880 30 | 22 6600.00 245.30 43400.07 14.6544 0.06291 31 | 23 6900.00 243.35 41608.92 11.9980 0.05721 32 | 24 7200.00 241.40 39891.69 9.8231 0.05179 33 | 25 7500.00 239.46 38245.33 8.0425 0.04658 34 | 26 7800.00 237.51 36666.91 6.5846 0.04165 35 | 27 8100.00 235.57 35132.69 5.3910 0.03703 36 | 28 8400.00 233.62 33622.54 4.4138 0.03260 37 | 29 8700.00 231.68 32177.29 3.6137 0.02856 38 | 30 9000.00 229.73 30794.17 2.9587 0.02485 39 | 31 9300.00 227.79 29470.50 2.4224 0.02147 40 | 32 9600.00 225.84 28203.73 1.9833 0.01836 41 | 33 9900.00 223.90 26991.41 1.6238 0.01560 42 | 34 10200.00 222.59 25772.51 1.3294 0.01305 43 | 35 10500.00 221.60 24580.67 1.0884 0.01076 44 | 36 10800.00 220.61 23443.96 0.8911 0.00877 45 | 37 11100.00 219.62 22359.80 0.7296 0.00705 46 | 38 11400.00 218.63 21325.79 0.5973 0.00552 47 | 39 11700.00 217.64 20339.59 0.4891 0.00423 48 | 40 12000.00 216.65 19399.00 0.4004 0.00312 49 | 41 12300.00 216.65 18506.23 0.3278 0.00218 50 | 42 12600.00 216.65 17654.54 0.2684 0.00137 51 | 43 12900.00 216.65 16842.05 0.2198 0.00073 52 | 44 13200.00 216.65 16066.96 0.1799 0.00017 53 | 45 13500.00 216.65 15327.53 0.1473 -0.00027 54 | 46 13800.00 216.65 14622.13 0.1206 -0.00065 55 | 47 14100.00 216.65 13949.30 0.0987 -0.00095 56 | 48 14400.00 216.65 13307.63 0.0808 -0.00118 57 | 49 14700.00 216.65 12695.47 0.0662 -0.00136 58 | 50 15000.00 216.65 12111.48 0.0542 -0.00150 59 | 51 15300.00 216.65 11554.34 0.0444 -0.00158 60 | 52 15600.00 216.65 11022.84 0.0363 -0.00166 61 | 53 15900.00 216.65 10515.78 0.0297 -0.00168 62 | 54 16200.00 216.65 10032.38 0.0243 -0.00168 63 | 55 16500.00 216.65 9571.35 0.0199 -0.00169 64 | 56 16800.00 216.65 9131.51 0.0163 -0.00166 65 | 57 17100.00 216.65 8711.88 0.0134 -0.00162 66 | 58 17400.00 216.65 8311.54 0.0109 -0.00158 67 | 59 17700.00 216.65 7929.59 0.0090 -0.00153 68 | 60 18000.00 216.65 7565.20 0.0073 -0.00146 69 | 61 18300.00 216.65 7217.68 0.0060 -0.00139 70 | 62 18600.00 216.65 6886.13 0.0049 -0.00133 71 | 63 18900.00 216.65 6569.81 0.0040 -0.00125 72 | 64 19200.00 216.65 6268.02 0.0033 -0.00118 73 | 65 19500.00 216.65 5980.09 0.0027 -0.00111 74 | 66 19800.00 216.65 5705.39 0.0022 -0.00104 75 | 67 20100.00 216.75 5443.72 0.0018 -0.00098 76 | 68 20400.00 217.03 5194.86 0.0015 -0.00090 77 | 69 20700.00 217.32 4957.37 0.0012 -0.00088 78 | 70 21000.00 217.61 4730.73 0.0010 -0.00080 79 | 71 21300.00 217.90 4514.46 0.0008 -0.00075 80 | 72 21600.00 218.19 4308.08 0.0007 -0.00068 81 | 73 21900.00 218.48 4111.13 0.0005 -0.00064 82 | 74 22200.00 218.77 3924.36 0.0004 -0.00057 83 | 75 22500.00 219.07 3746.63 0.0004 -0.00055 84 | 76 22800.00 219.37 3576.96 0.0003 -0.00052 85 | 77 23100.00 219.67 3414.97 0.0002 -0.00046 86 | 78 23400.00 219.96 3260.31 0.0002 -0.00040 87 | 79 23700.00 220.26 3112.66 0.0002 -0.00040 88 | 80 24000.00 220.56 2971.70 0.0001 -0.00036 89 | 81 24300.00 220.86 2838.38 0.0001 -0.00033 90 | 82 24600.00 221.16 2711.04 0.0001 -0.00030 91 | 83 24900.00 221.45 2589.41 0.0001 -0.00029 92 | 84 25200.00 221.75 2473.24 0.0001 -0.00027 93 | 85 25500.00 222.05 2362.28 0.0000 -0.00025 94 | 86 25800.00 222.35 2256.30 0.0000 -0.00022 95 | 87 26100.00 222.64 2155.39 0.0000 -0.00021 96 | 88 26400.00 222.94 2059.58 0.0000 -0.00018 97 | 89 26700.00 223.24 1968.04 0.0000 -0.00019 98 | 90 27000.00 223.54 1880.56 0.0000 -0.00016 99 | 91 27300.00 223.83 1796.97 0.0000 -0.00013 100 | 92 27600.00 224.13 1717.10 0.0000 -0.00011 101 | 93 27900.00 224.43 1640.78 0.0000 -0.00012 102 | 94 28200.00 224.73 1568.31 0.0000 -0.00012 103 | 95 28500.00 225.02 1499.25 0.0000 -0.00010 104 | 96 28800.00 225.32 1433.24 0.0000 -0.00008 105 | 97 29100.00 225.62 1370.13 0.0000 -0.00009 106 | 98 29400.00 225.91 1309.80 0.0000 -0.00007 107 | 99 29700.00 226.21 1252.13 0.0000 -0.00008 108 | 100 30000.00 226.51 1197.00 0.0000 -0.00007 109 | 101 30300.00 227.11 1145.43 0.0000 -0.00007 110 | 102 30600.00 227.71 1096.09 0.0000 -0.00004 111 | 103 30900.00 228.31 1048.87 0.0000 -0.00005 112 | 104 31200.00 228.91 1003.69 0.0000 -0.00006 113 | 105 31500.00 229.51 960.45 0.0000 -0.00004 114 | 106 31800.00 230.11 919.07 0.0000 -0.00003 115 | 107 32100.00 230.71 879.48 0.0000 -0.00003 116 | 108 32400.00 231.31 841.59 0.0000 -0.00004 117 | 109 32700.00 231.91 805.34 0.0000 -0.00003 118 | 110 33000.00 232.51 770.64 0.0000 -0.00004 119 | 111 33300.00 233.11 737.44 0.0000 -0.00004 120 | 112 33600.00 233.71 705.67 0.0000 -0.00003 121 | 113 33900.00 234.31 675.27 0.0000 -0.00004 122 | 114 34200.00 234.91 646.18 0.0000 -0.00001 123 | 115 34500.00 235.51 618.35 0.0000 -0.00001 124 | 116 34800.00 236.11 591.71 0.0000 -0.00001 125 | 117 35100.00 236.79 566.67 0.0000 -0.00002 126 | 118 35400.00 237.62 543.57 0.0000 -0.00001 127 | 119 35700.00 238.45 521.41 0.0000 0.00000 128 | 120 36000.00 239.28 500.16 0.0000 -0.00002 129 | 121 36300.00 240.11 479.77 0.0000 -0.00001 130 | 122 36600.00 240.94 460.21 0.0000 -0.00001 131 | 123 36900.00 241.77 441.45 0.0000 0.00000 132 | 124 37200.00 242.60 423.45 0.0000 -0.00000 133 | 125 37500.00 243.43 406.19 0.0000 -0.00001 134 | 126 37800.00 244.26 389.63 0.0000 0.00000 135 | 127 38100.00 245.09 373.74 0.0000 -0.00002 136 | 128 38400.00 245.92 358.51 0.0000 -0.00002 137 | 129 38700.00 246.75 343.89 0.0000 0.00002 138 | 130 39000.00 247.58 329.87 0.0000 -0.00000 139 | 131 39300.00 248.41 316.42 0.0000 -0.00002 140 | 132 39600.00 249.24 303.53 0.0000 -0.00001 141 | 133 39900.00 250.07 291.15 0.0000 0.00001 142 | 134 40200.00 250.90 279.71 0.0000 0.00001 143 | 135 40500.00 251.73 268.93 0.0000 0.00001 144 | 136 40800.00 252.56 258.56 0.0000 0.00000 145 | 137 41100.00 253.39 248.59 0.0000 -0.00002 146 | 138 41400.00 254.22 239.00 0.0000 -0.00002 147 | 139 41700.00 255.05 229.79 0.0000 -0.00001 148 | 140 42000.00 255.88 220.93 0.0000 -0.00001 149 | 141 42300.00 256.70 212.41 0.0000 -0.00001 150 | 142 42600.00 257.53 204.22 0.0000 -0.00001 151 | 143 42900.00 258.36 196.34 0.0000 0.00001 152 | 144 43200.00 259.19 188.77 0.0000 0.00001 153 | 145 43500.00 260.02 181.49 0.0000 0.00001 154 | 146 43800.00 260.85 174.50 0.0000 0.00001 155 | 147 44100.00 261.68 167.77 0.0000 0.00001 156 | 148 44400.00 262.51 161.30 0.0000 0.00001 157 | 149 44700.00 263.34 155.08 0.0000 0.00001 158 | 150 45000.00 264.16 149.10 0.0000 -0.00001 159 | 151 45300.00 264.55 143.61 0.0000 -0.00001 160 | 152 45600.00 264.94 138.32 0.0000 -0.00003 161 | 153 45900.00 265.33 133.23 0.0000 -0.00003 162 | 154 46200.00 265.72 128.32 0.0000 -0.00001 163 | 155 46500.00 266.11 123.60 0.0000 -0.00001 164 | 156 46800.00 266.50 119.04 0.0000 -0.00001 165 | 157 47100.00 266.89 114.66 0.0000 0.00000 166 | 158 47400.00 267.28 110.44 0.0000 0.00000 167 | 159 47700.00 267.67 106.37 0.0000 0.00000 168 | 160 48000.00 268.06 102.45 0.0000 0.00000 169 | 161 48300.00 268.44 98.68 0.0000 0.00000 170 | 162 48600.00 268.83 95.05 0.0000 0.00000 171 | 163 48900.00 269.22 91.55 0.0000 0.00000 172 | 164 49200.00 269.61 88.17 0.0000 0.00000 173 | 165 49500.00 270.00 84.93 0.0000 0.00000 174 | 166 49800.00 270.39 81.80 0.0000 0.00000 175 | 167 50100.00 270.45 78.78 0.0000 0.00000 176 | 168 50400.00 269.86 75.86 0.0000 0.00000 177 | 169 50700.00 269.27 73.05 0.0000 0.00000 178 | 170 51000.00 268.67 70.35 0.0000 0.00000 179 | 171 51300.00 268.08 67.74 0.0000 0.00000 180 | 172 51600.00 267.49 65.23 0.0000 0.00000 181 | 173 51900.00 266.90 62.81 0.0000 0.00000 182 | 174 52200.00 266.30 60.49 0.0000 0.00000 183 | 175 52500.00 265.71 58.25 0.0000 0.00000 184 | 176 52800.00 265.12 56.09 0.0000 0.00000 185 | 177 53100.00 264.53 54.01 0.0000 0.00000 186 | 178 53400.00 263.93 52.01 0.0000 0.00000 187 | 179 53700.00 263.34 50.08 0.0000 0.00000 188 | 180 54000.00 262.75 48.23 0.0000 0.00000 189 | 181 54300.00 262.15 46.44 0.0000 0.00000 190 | 182 54600.00 261.56 44.72 0.0000 0.00000 191 | 183 54900.00 260.97 43.06 0.0000 0.00000 192 | 184 55200.00 260.22 41.42 0.0000 0.00000 193 | 185 55500.00 259.40 39.81 0.0000 0.00000 194 | 186 55800.00 258.57 38.26 0.0000 0.00000 195 | 187 56100.00 257.75 36.77 0.0000 0.00000 196 | 188 56400.00 256.92 35.34 0.0000 0.00000 197 | 189 56700.00 256.10 33.97 0.0000 0.00000 198 | 190 57000.00 255.27 32.65 0.0000 0.00000 199 | 191 57300.00 254.45 31.38 0.0000 0.00000 200 | 192 57600.00 253.62 30.16 0.0000 0.00000 201 | 193 57900.00 252.80 28.98 0.0000 0.00000 202 | 194 58200.00 251.97 27.86 0.0000 0.00000 203 | 195 58500.00 251.15 26.77 0.0000 0.00000 204 | 196 58800.00 250.32 25.73 0.0000 0.00000 205 | 197 59100.00 249.50 24.73 0.0000 0.00000 206 | 198 59400.00 248.67 23.77 0.0000 0.00000 207 | 199 59700.00 247.85 22.85 0.0000 0.00000 208 | 200 60000.00 247.02 21.96 0.0000 0.00000 209 | 201 60300.00 246.20 21.06 0.0000 0.00000 210 | 202 60600.00 245.37 20.19 0.0000 0.00000 211 | 203 60900.00 244.55 19.37 0.0000 0.00000 212 | 204 61200.00 243.73 18.57 0.0000 0.00000 213 | 205 61500.00 242.90 17.81 0.0000 0.00000 214 | 206 61800.00 242.08 17.08 0.0000 0.00000 215 | 207 62100.00 241.25 16.38 0.0000 0.00000 216 | 208 62400.00 240.43 15.71 0.0000 0.00000 217 | 209 62700.00 239.61 15.06 0.0000 0.00000 218 | 210 63000.00 238.78 14.45 0.0000 0.00000 219 | 211 63300.00 237.96 13.85 0.0000 0.00000 220 | 212 63600.00 237.14 13.29 0.0000 0.00000 221 | 213 63900.00 236.31 12.74 0.0000 0.00000 222 | 214 64200.00 235.49 12.22 0.0000 0.00000 223 | 215 64500.00 234.66 11.72 0.0000 0.00000 224 | 216 64800.00 233.84 11.24 0.0000 0.00000 225 | 217 65100.00 233.02 10.77 0.0000 0.00000 226 | 218 65400.00 232.20 10.30 0.0000 0.00000 227 | 219 65700.00 231.37 9.86 0.0000 0.00000 228 | 220 66000.00 230.55 9.43 0.0000 0.00000 229 | 221 66300.00 229.73 9.02 0.0000 0.00000 230 | 222 66600.00 228.91 8.63 0.0000 0.00000 231 | 223 66900.00 228.08 8.25 0.0000 0.00000 232 | 224 67200.00 227.26 7.90 0.0000 0.00000 233 | 225 67500.00 226.44 7.55 0.0000 0.00000 234 | 226 67800.00 225.62 7.23 0.0000 0.00000 235 | 227 68100.00 224.79 6.91 0.0000 0.00000 236 | 228 68400.00 223.97 6.61 0.0000 0.00000 237 | 229 68700.00 223.15 6.33 0.0000 0.00000 238 | 230 69000.00 222.33 6.05 0.0000 0.00000 239 | 231 69300.00 221.50 5.79 0.0000 0.00000 240 | 232 69600.00 220.68 5.54 0.0000 0.00000 241 | 233 69900.00 219.86 5.30 0.0000 0.00000 242 | 234 70200.00 219.14 5.06 0.0000 0.00000 243 | 235 70500.00 218.47 4.83 0.0000 0.00000 244 | 236 70800.00 217.80 4.61 0.0000 0.00000 245 | 237 71100.00 217.12 4.40 0.0000 0.00000 246 | 238 71400.00 216.45 4.19 0.0000 0.00000 247 | 239 71700.00 215.78 4.00 0.0000 0.00000 248 | 240 72000.00 215.11 3.82 0.0000 0.00000 249 | 241 72300.00 214.44 3.64 0.0000 0.00000 250 | 242 72600.00 213.77 3.48 0.0000 0.00000 251 | 243 72900.00 213.10 3.32 0.0000 0.00000 252 | 244 73200.00 212.43 3.16 0.0000 0.00000 253 | 245 73500.00 211.75 3.02 0.0000 0.00000 254 | 246 73800.00 211.08 2.88 0.0000 0.00000 255 | 247 74100.00 210.41 2.75 0.0000 0.00000 256 | 248 74400.00 209.74 2.62 0.0000 0.00000 257 | 249 74700.00 209.07 2.50 0.0000 0.00000 258 | 250 75000.00 208.40 2.39 0.0000 0.00000 259 | 251 75300.00 207.81 2.27 0.0000 0.00000 260 | 252 75600.00 207.23 2.16 0.0000 0.00000 261 | 253 75900.00 206.64 2.06 0.0000 0.00000 262 | 254 76200.00 206.06 1.96 0.0000 0.00000 263 | 255 76500.00 205.47 1.87 0.0000 0.00000 264 | 256 76800.00 204.89 1.78 0.0000 0.00000 265 | 257 77100.00 204.30 1.69 0.0000 0.00000 266 | 258 77400.00 203.71 1.61 0.0000 0.00000 267 | 259 77700.00 203.13 1.53 0.0000 0.00000 268 | 260 78000.00 202.54 1.46 0.0000 0.00000 269 | 261 78300.00 201.96 1.39 0.0000 0.00000 270 | 262 78600.00 201.37 1.32 0.0000 0.00000 271 | 263 78900.00 200.79 1.26 0.0000 0.00000 272 | 264 79200.00 200.20 1.20 0.0000 0.00000 273 | 265 79500.00 199.62 1.14 0.0000 0.00000 274 | 266 79800.00 199.03 1.09 0.0000 0.00000 275 | 267 80100.00 198.44 1.03 0.0000 0.00000 276 | 268 80400.00 197.86 0.98 0.0000 0.00000 277 | 269 80700.00 197.27 0.93 0.0000 0.00000 278 | 270 81000.00 196.69 0.89 0.0000 0.00000 279 | 271 81300.00 196.11 0.84 0.0000 0.00000 280 | 272 81600.00 195.52 0.80 0.0000 0.00000 281 | 273 81900.00 194.94 0.76 0.0000 0.00000 282 | 274 82200.00 194.35 0.72 0.0000 0.00000 283 | 275 82500.00 193.77 0.68 0.0000 0.00000 284 | 276 82800.00 193.18 0.65 0.0000 0.00000 285 | 277 83100.00 192.60 0.62 0.0000 0.00000 286 | 278 83400.00 192.01 0.59 0.0000 0.00000 287 | 279 83700.00 191.43 0.56 0.0000 0.00000 288 | 280 84000.00 190.84 0.53 0.0000 0.00000 289 | 281 84300.00 190.26 0.50 0.0000 0.00000 290 | 282 84600.00 189.67 0.48 0.0000 0.00000 291 | 283 84900.00 189.09 0.45 0.0000 0.00000 292 | 284 85200.00 188.81 0.43 0.0000 0.00000 293 | 285 85500.00 188.69 0.41 0.0000 0.00000 294 | 286 85800.00 188.57 0.39 0.0000 0.00000 295 | 287 86100.00 188.45 0.37 0.0000 0.00000 296 | 288 86400.00 188.33 0.35 0.0000 0.00000 297 | 289 86700.00 188.21 0.33 0.0000 0.00000 298 | 290 87000.00 188.08 0.31 0.0000 0.00000 299 | 291 87300.00 187.96 0.30 0.0000 0.00000 300 | 292 87600.00 187.84 0.28 0.0000 0.00000 301 | 293 87900.00 187.72 0.27 0.0000 0.00000 302 | 294 88200.00 187.60 0.25 0.0000 0.00000 303 | 295 88500.00 187.48 0.24 0.0000 0.00000 304 | 296 88800.00 187.36 0.23 0.0000 0.00000 305 | 297 89100.00 187.23 0.22 0.0000 0.00000 306 | 298 89400.00 187.11 0.20 0.0000 0.00000 307 | 299 89700.00 186.99 0.19 0.0000 0.00000 308 | -------------------------------------------------------------------------------- /data/std_atmosphere_wt_function_chan_tmt_land.txt: -------------------------------------------------------------------------------- 1 | Temperature Weighting Function for Channel TMT 2 | Surface Type: land 3 | -------------------------------------------------------- 4 | Surface Weight 0.06527 5 | -------------------------------------------------------- 6 | level h(m) T(K) P(pa) PV(pa) Weight 7 | -------------------------------------------------------- 8 | 0 0.00 288.15 101325.00 1193.6130 0.05664 9 | 1 300.00 286.20 97704.60 977.2475 0.05774 10 | 2 600.00 284.25 94213.55 800.1025 0.05932 11 | 3 900.00 282.30 90847.25 655.0686 0.06124 12 | 4 1200.00 280.35 87601.23 536.3248 0.06338 13 | 5 1500.00 278.40 84471.19 439.1056 0.06563 14 | 6 1800.00 276.45 81452.98 359.5092 0.06792 15 | 7 2100.00 274.50 78497.20 294.3413 0.07014 16 | 8 2400.00 272.56 75561.19 240.9863 0.07214 17 | 9 2700.00 270.61 72735.00 197.3029 0.07400 18 | 10 3000.00 268.66 70014.51 161.5379 0.07570 19 | 11 3300.00 266.71 67395.77 132.2561 0.07721 20 | 12 3600.00 264.76 64874.99 108.2821 0.07850 21 | 13 3900.00 262.82 62448.49 88.6539 0.07956 22 | 14 4200.00 260.87 60036.17 72.5837 0.08023 23 | 15 4500.00 258.92 57680.26 59.4265 0.08057 24 | 16 4800.00 256.97 55416.80 48.6543 0.08065 25 | 17 5100.00 255.03 53242.16 39.8348 0.08049 26 | 18 5400.00 253.08 51152.86 32.6139 0.08009 27 | 19 5700.00 251.13 49145.55 26.7020 0.07946 28 | 20 6000.00 249.19 47217.00 21.8618 0.07862 29 | 21 6300.00 247.24 45268.32 17.8989 0.07732 30 | 22 6600.00 245.30 43400.07 14.6544 0.07584 31 | 23 6900.00 243.35 41608.92 11.9980 0.07420 32 | 24 7200.00 241.40 39891.69 9.8231 0.07241 33 | 25 7500.00 239.46 38245.33 8.0425 0.07050 34 | 26 7800.00 237.51 36666.91 6.5846 0.06848 35 | 27 8100.00 235.57 35132.69 5.3910 0.06632 36 | 28 8400.00 233.62 33622.54 4.4138 0.06397 37 | 29 8700.00 231.68 32177.29 3.6137 0.06157 38 | 30 9000.00 229.73 30794.17 2.9587 0.05915 39 | 31 9300.00 227.79 29470.50 2.4224 0.05673 40 | 32 9600.00 225.84 28203.73 1.9833 0.05429 41 | 33 9900.00 223.90 26991.41 1.6238 0.05188 42 | 34 10200.00 222.59 25772.51 1.3294 0.04927 43 | 35 10500.00 221.60 24580.67 1.0884 0.04662 44 | 36 10800.00 220.61 23443.96 0.8911 0.04406 45 | 37 11100.00 219.62 22359.80 0.7296 0.04158 46 | 38 11400.00 218.63 21325.79 0.5973 0.03919 47 | 39 11700.00 217.64 20339.59 0.4891 0.03689 48 | 40 12000.00 216.65 19399.00 0.4004 0.03468 49 | 41 12300.00 216.65 18506.23 0.3278 0.03258 50 | 42 12600.00 216.65 17654.54 0.2684 0.03059 51 | 43 12900.00 216.65 16842.05 0.2198 0.02870 52 | 44 13200.00 216.65 16066.96 0.1799 0.02690 53 | 45 13500.00 216.65 15327.53 0.1473 0.02521 54 | 46 13800.00 216.65 14622.13 0.1206 0.02360 55 | 47 14100.00 216.65 13949.30 0.0987 0.02208 56 | 48 14400.00 216.65 13307.63 0.0808 0.02064 57 | 49 14700.00 216.65 12695.47 0.0662 0.01928 58 | 50 15000.00 216.65 12111.48 0.0542 0.01800 59 | 51 15300.00 216.65 11554.34 0.0444 0.01679 60 | 52 15600.00 216.65 11022.84 0.0363 0.01564 61 | 53 15900.00 216.65 10515.78 0.0297 0.01456 62 | 54 16200.00 216.65 10032.38 0.0243 0.01355 63 | 55 16500.00 216.65 9571.35 0.0199 0.01259 64 | 56 16800.00 216.65 9131.51 0.0163 0.01170 65 | 57 17100.00 216.65 8711.88 0.0134 0.01085 66 | 58 17400.00 216.65 8311.54 0.0109 0.01006 67 | 59 17700.00 216.65 7929.59 0.0090 0.00932 68 | 60 18000.00 216.65 7565.20 0.0073 0.00862 69 | 61 18300.00 216.65 7217.68 0.0060 0.00797 70 | 62 18600.00 216.65 6886.13 0.0049 0.00736 71 | 63 18900.00 216.65 6569.81 0.0040 0.00679 72 | 64 19200.00 216.65 6268.02 0.0033 0.00627 73 | 65 19500.00 216.65 5980.09 0.0027 0.00577 74 | 66 19800.00 216.65 5705.39 0.0022 0.00531 75 | 67 20100.00 216.75 5443.72 0.0018 0.00489 76 | 68 20400.00 217.03 5194.86 0.0015 0.00450 77 | 69 20700.00 217.32 4957.37 0.0012 0.00414 78 | 70 21000.00 217.61 4730.73 0.0010 0.00381 79 | 71 21300.00 217.90 4514.46 0.0008 0.00350 80 | 72 21600.00 218.19 4308.08 0.0007 0.00321 81 | 73 21900.00 218.48 4111.13 0.0005 0.00295 82 | 74 22200.00 218.77 3924.36 0.0004 0.00271 83 | 75 22500.00 219.07 3746.63 0.0004 0.00248 84 | 76 22800.00 219.37 3576.96 0.0003 0.00228 85 | 77 23100.00 219.67 3414.97 0.0002 0.00209 86 | 78 23400.00 219.96 3260.31 0.0002 0.00191 87 | 79 23700.00 220.26 3112.66 0.0002 0.00175 88 | 80 24000.00 220.56 2971.70 0.0001 0.00161 89 | 81 24300.00 220.86 2838.38 0.0001 0.00148 90 | 82 24600.00 221.16 2711.04 0.0001 0.00135 91 | 83 24900.00 221.45 2589.41 0.0001 0.00123 92 | 84 25200.00 221.75 2473.24 0.0001 0.00113 93 | 85 25500.00 222.05 2362.28 0.0000 0.00104 94 | 86 25800.00 222.35 2256.30 0.0000 0.00095 95 | 87 26100.00 222.64 2155.39 0.0000 0.00087 96 | 88 26400.00 222.94 2059.58 0.0000 0.00079 97 | 89 26700.00 223.24 1968.04 0.0000 0.00072 98 | 90 27000.00 223.54 1880.56 0.0000 0.00066 99 | 91 27300.00 223.83 1796.97 0.0000 0.00061 100 | 92 27600.00 224.13 1717.10 0.0000 0.00056 101 | 93 27900.00 224.43 1640.78 0.0000 0.00051 102 | 94 28200.00 224.73 1568.31 0.0000 0.00047 103 | 95 28500.00 225.02 1499.25 0.0000 0.00043 104 | 96 28800.00 225.32 1433.24 0.0000 0.00039 105 | 97 29100.00 225.62 1370.13 0.0000 0.00036 106 | 98 29400.00 225.91 1309.80 0.0000 0.00033 107 | 99 29700.00 226.21 1252.13 0.0000 0.00030 108 | 100 30000.00 226.51 1197.00 0.0000 0.00028 109 | 101 30300.00 227.11 1145.43 0.0000 0.00025 110 | 102 30600.00 227.71 1096.09 0.0000 0.00023 111 | 103 30900.00 228.31 1048.87 0.0000 0.00021 112 | 104 31200.00 228.91 1003.69 0.0000 0.00019 113 | 105 31500.00 229.51 960.45 0.0000 0.00018 114 | 106 31800.00 230.11 919.07 0.0000 0.00016 115 | 107 32100.00 230.71 879.48 0.0000 0.00015 116 | 108 32400.00 231.31 841.59 0.0000 0.00014 117 | 109 32700.00 231.91 805.34 0.0000 0.00012 118 | 110 33000.00 232.51 770.64 0.0000 0.00011 119 | 111 33300.00 233.11 737.44 0.0000 0.00011 120 | 112 33600.00 233.71 705.67 0.0000 0.00010 121 | 113 33900.00 234.31 675.27 0.0000 0.00009 122 | 114 34200.00 234.91 646.18 0.0000 0.00008 123 | 115 34500.00 235.51 618.35 0.0000 0.00007 124 | 116 34800.00 236.11 591.71 0.0000 0.00007 125 | 117 35100.00 236.79 566.67 0.0000 0.00006 126 | 118 35400.00 237.62 543.57 0.0000 0.00006 127 | 119 35700.00 238.45 521.41 0.0000 0.00005 128 | 120 36000.00 239.28 500.16 0.0000 0.00005 129 | 121 36300.00 240.11 479.77 0.0000 0.00005 130 | 122 36600.00 240.94 460.21 0.0000 0.00004 131 | 123 36900.00 241.77 441.45 0.0000 0.00004 132 | 124 37200.00 242.60 423.45 0.0000 0.00004 133 | 125 37500.00 243.43 406.19 0.0000 0.00003 134 | 126 37800.00 244.26 389.63 0.0000 0.00003 135 | 127 38100.00 245.09 373.74 0.0000 0.00003 136 | 128 38400.00 245.92 358.51 0.0000 0.00003 137 | 129 38700.00 246.75 343.89 0.0000 0.00002 138 | 130 39000.00 247.58 329.87 0.0000 0.00002 139 | 131 39300.00 248.41 316.42 0.0000 0.00002 140 | 132 39600.00 249.24 303.53 0.0000 0.00002 141 | 133 39900.00 250.07 291.15 0.0000 0.00002 142 | 134 40200.00 250.90 279.71 0.0000 0.00002 143 | 135 40500.00 251.73 268.93 0.0000 0.00001 144 | 136 40800.00 252.56 258.56 0.0000 0.00001 145 | 137 41100.00 253.39 248.59 0.0000 0.00001 146 | 138 41400.00 254.22 239.00 0.0000 0.00001 147 | 139 41700.00 255.05 229.79 0.0000 0.00001 148 | 140 42000.00 255.88 220.93 0.0000 0.00001 149 | 141 42300.00 256.70 212.41 0.0000 0.00001 150 | 142 42600.00 257.53 204.22 0.0000 0.00001 151 | 143 42900.00 258.36 196.34 0.0000 0.00001 152 | 144 43200.00 259.19 188.77 0.0000 0.00001 153 | 145 43500.00 260.02 181.49 0.0000 0.00001 154 | 146 43800.00 260.85 174.50 0.0000 0.00001 155 | 147 44100.00 261.68 167.77 0.0000 0.00001 156 | 148 44400.00 262.51 161.30 0.0000 0.00001 157 | 149 44700.00 263.34 155.08 0.0000 0.00001 158 | 150 45000.00 264.16 149.10 0.0000 0.00000 159 | 151 45300.00 264.55 143.61 0.0000 0.00000 160 | 152 45600.00 264.94 138.32 0.0000 0.00000 161 | 153 45900.00 265.33 133.23 0.0000 0.00000 162 | 154 46200.00 265.72 128.32 0.0000 0.00000 163 | 155 46500.00 266.11 123.60 0.0000 0.00000 164 | 156 46800.00 266.50 119.04 0.0000 0.00000 165 | 157 47100.00 266.89 114.66 0.0000 0.00000 166 | 158 47400.00 267.28 110.44 0.0000 0.00000 167 | 159 47700.00 267.67 106.37 0.0000 0.00000 168 | 160 48000.00 268.06 102.45 0.0000 0.00000 169 | 161 48300.00 268.44 98.68 0.0000 0.00000 170 | 162 48600.00 268.83 95.05 0.0000 0.00000 171 | 163 48900.00 269.22 91.55 0.0000 0.00000 172 | 164 49200.00 269.61 88.17 0.0000 0.00000 173 | 165 49500.00 270.00 84.93 0.0000 0.00000 174 | 166 49800.00 270.39 81.80 0.0000 0.00000 175 | 167 50100.00 270.45 78.78 0.0000 0.00000 176 | 168 50400.00 269.86 75.86 0.0000 0.00000 177 | 169 50700.00 269.27 73.05 0.0000 0.00000 178 | 170 51000.00 268.67 70.35 0.0000 0.00000 179 | 171 51300.00 268.08 67.74 0.0000 0.00000 180 | 172 51600.00 267.49 65.23 0.0000 0.00000 181 | 173 51900.00 266.90 62.81 0.0000 0.00000 182 | 174 52200.00 266.30 60.49 0.0000 0.00000 183 | 175 52500.00 265.71 58.25 0.0000 0.00000 184 | 176 52800.00 265.12 56.09 0.0000 0.00000 185 | 177 53100.00 264.53 54.01 0.0000 0.00000 186 | 178 53400.00 263.93 52.01 0.0000 0.00000 187 | 179 53700.00 263.34 50.08 0.0000 0.00000 188 | 180 54000.00 262.75 48.23 0.0000 0.00000 189 | 181 54300.00 262.15 46.44 0.0000 0.00000 190 | 182 54600.00 261.56 44.72 0.0000 0.00000 191 | 183 54900.00 260.97 43.06 0.0000 0.00000 192 | 184 55200.00 260.22 41.42 0.0000 0.00000 193 | 185 55500.00 259.40 39.81 0.0000 0.00000 194 | 186 55800.00 258.57 38.26 0.0000 0.00000 195 | 187 56100.00 257.75 36.77 0.0000 0.00000 196 | 188 56400.00 256.92 35.34 0.0000 0.00000 197 | 189 56700.00 256.10 33.97 0.0000 0.00000 198 | 190 57000.00 255.27 32.65 0.0000 0.00000 199 | 191 57300.00 254.45 31.38 0.0000 0.00000 200 | 192 57600.00 253.62 30.16 0.0000 0.00000 201 | 193 57900.00 252.80 28.98 0.0000 0.00000 202 | 194 58200.00 251.97 27.86 0.0000 0.00000 203 | 195 58500.00 251.15 26.77 0.0000 0.00000 204 | 196 58800.00 250.32 25.73 0.0000 0.00000 205 | 197 59100.00 249.50 24.73 0.0000 0.00000 206 | 198 59400.00 248.67 23.77 0.0000 0.00000 207 | 199 59700.00 247.85 22.85 0.0000 0.00000 208 | 200 60000.00 247.02 21.96 0.0000 0.00000 209 | 201 60300.00 246.20 21.06 0.0000 0.00000 210 | 202 60600.00 245.37 20.19 0.0000 0.00000 211 | 203 60900.00 244.55 19.37 0.0000 0.00000 212 | 204 61200.00 243.73 18.57 0.0000 0.00000 213 | 205 61500.00 242.90 17.81 0.0000 0.00000 214 | 206 61800.00 242.08 17.08 0.0000 0.00000 215 | 207 62100.00 241.25 16.38 0.0000 0.00000 216 | 208 62400.00 240.43 15.71 0.0000 0.00000 217 | 209 62700.00 239.61 15.06 0.0000 0.00000 218 | 210 63000.00 238.78 14.45 0.0000 0.00000 219 | 211 63300.00 237.96 13.85 0.0000 0.00000 220 | 212 63600.00 237.14 13.29 0.0000 0.00000 221 | 213 63900.00 236.31 12.74 0.0000 0.00000 222 | 214 64200.00 235.49 12.22 0.0000 0.00000 223 | 215 64500.00 234.66 11.72 0.0000 0.00000 224 | 216 64800.00 233.84 11.24 0.0000 0.00000 225 | 217 65100.00 233.02 10.77 0.0000 0.00000 226 | 218 65400.00 232.20 10.30 0.0000 0.00000 227 | 219 65700.00 231.37 9.86 0.0000 0.00000 228 | 220 66000.00 230.55 9.43 0.0000 0.00000 229 | 221 66300.00 229.73 9.02 0.0000 0.00000 230 | 222 66600.00 228.91 8.63 0.0000 0.00000 231 | 223 66900.00 228.08 8.25 0.0000 0.00000 232 | 224 67200.00 227.26 7.90 0.0000 0.00000 233 | 225 67500.00 226.44 7.55 0.0000 0.00000 234 | 226 67800.00 225.62 7.23 0.0000 0.00000 235 | 227 68100.00 224.79 6.91 0.0000 0.00000 236 | 228 68400.00 223.97 6.61 0.0000 0.00000 237 | 229 68700.00 223.15 6.33 0.0000 0.00000 238 | 230 69000.00 222.33 6.05 0.0000 0.00000 239 | 231 69300.00 221.50 5.79 0.0000 0.00000 240 | 232 69600.00 220.68 5.54 0.0000 0.00000 241 | 233 69900.00 219.86 5.30 0.0000 0.00000 242 | 234 70200.00 219.14 5.06 0.0000 0.00000 243 | 235 70500.00 218.47 4.83 0.0000 0.00000 244 | 236 70800.00 217.80 4.61 0.0000 0.00000 245 | 237 71100.00 217.12 4.40 0.0000 0.00000 246 | 238 71400.00 216.45 4.19 0.0000 0.00000 247 | 239 71700.00 215.78 4.00 0.0000 0.00000 248 | 240 72000.00 215.11 3.82 0.0000 0.00000 249 | 241 72300.00 214.44 3.64 0.0000 0.00000 250 | 242 72600.00 213.77 3.48 0.0000 0.00000 251 | 243 72900.00 213.10 3.32 0.0000 0.00000 252 | 244 73200.00 212.43 3.16 0.0000 0.00000 253 | 245 73500.00 211.75 3.02 0.0000 0.00000 254 | 246 73800.00 211.08 2.88 0.0000 0.00000 255 | 247 74100.00 210.41 2.75 0.0000 0.00000 256 | 248 74400.00 209.74 2.62 0.0000 0.00000 257 | 249 74700.00 209.07 2.50 0.0000 0.00000 258 | 250 75000.00 208.40 2.39 0.0000 0.00000 259 | 251 75300.00 207.81 2.27 0.0000 0.00000 260 | 252 75600.00 207.23 2.16 0.0000 0.00000 261 | 253 75900.00 206.64 2.06 0.0000 0.00000 262 | 254 76200.00 206.06 1.96 0.0000 0.00000 263 | 255 76500.00 205.47 1.87 0.0000 0.00000 264 | 256 76800.00 204.89 1.78 0.0000 0.00000 265 | 257 77100.00 204.30 1.69 0.0000 0.00000 266 | 258 77400.00 203.71 1.61 0.0000 0.00000 267 | 259 77700.00 203.13 1.53 0.0000 0.00000 268 | 260 78000.00 202.54 1.46 0.0000 0.00000 269 | 261 78300.00 201.96 1.39 0.0000 0.00000 270 | 262 78600.00 201.37 1.32 0.0000 0.00000 271 | 263 78900.00 200.79 1.26 0.0000 0.00000 272 | 264 79200.00 200.20 1.20 0.0000 0.00000 273 | 265 79500.00 199.62 1.14 0.0000 0.00000 274 | 266 79800.00 199.03 1.09 0.0000 0.00000 275 | 267 80100.00 198.44 1.03 0.0000 0.00000 276 | 268 80400.00 197.86 0.98 0.0000 0.00000 277 | 269 80700.00 197.27 0.93 0.0000 0.00000 278 | 270 81000.00 196.69 0.89 0.0000 0.00000 279 | 271 81300.00 196.11 0.84 0.0000 0.00000 280 | 272 81600.00 195.52 0.80 0.0000 0.00000 281 | 273 81900.00 194.94 0.76 0.0000 0.00000 282 | 274 82200.00 194.35 0.72 0.0000 0.00000 283 | 275 82500.00 193.77 0.68 0.0000 0.00000 284 | 276 82800.00 193.18 0.65 0.0000 0.00000 285 | 277 83100.00 192.60 0.62 0.0000 0.00000 286 | 278 83400.00 192.01 0.59 0.0000 0.00000 287 | 279 83700.00 191.43 0.56 0.0000 0.00000 288 | 280 84000.00 190.84 0.53 0.0000 0.00000 289 | 281 84300.00 190.26 0.50 0.0000 0.00000 290 | 282 84600.00 189.67 0.48 0.0000 0.00000 291 | 283 84900.00 189.09 0.45 0.0000 0.00000 292 | 284 85200.00 188.81 0.43 0.0000 0.00000 293 | 285 85500.00 188.69 0.41 0.0000 0.00000 294 | 286 85800.00 188.57 0.39 0.0000 0.00000 295 | 287 86100.00 188.45 0.37 0.0000 0.00000 296 | 288 86400.00 188.33 0.35 0.0000 0.00000 297 | 289 86700.00 188.21 0.33 0.0000 0.00000 298 | 290 87000.00 188.08 0.31 0.0000 0.00000 299 | 291 87300.00 187.96 0.30 0.0000 0.00000 300 | 292 87600.00 187.84 0.28 0.0000 0.00000 301 | 293 87900.00 187.72 0.27 0.0000 0.00000 302 | 294 88200.00 187.60 0.25 0.0000 0.00000 303 | 295 88500.00 187.48 0.24 0.0000 0.00000 304 | 296 88800.00 187.36 0.23 0.0000 0.00000 305 | 297 89100.00 187.23 0.22 0.0000 0.00000 306 | 298 89400.00 187.11 0.20 0.0000 0.00000 307 | 299 89700.00 186.99 0.19 0.0000 0.00000 308 | -------------------------------------------------------------------------------- /data/std_atmosphere_wt_function_chan_tmt_ocean.txt: -------------------------------------------------------------------------------- 1 | Temperature Weighting Function for Channel TMT 2 | Surface Type: ocean 3 | -------------------------------------------------------- 4 | Surface Weight 0.04976 5 | -------------------------------------------------------- 6 | level h(m) T(K) P(pa) PV(pa) Weight 7 | -------------------------------------------------------- 8 | 0 0.00 288.15 101325.00 1193.6130 0.06385 9 | 1 300.00 286.20 97704.60 977.2475 0.06366 10 | 2 600.00 284.25 94213.55 800.1025 0.06425 11 | 3 900.00 282.30 90847.25 655.0686 0.06537 12 | 4 1200.00 280.35 87601.23 536.3248 0.06686 13 | 5 1500.00 278.40 84471.19 439.1056 0.06859 14 | 6 1800.00 276.45 81452.98 359.5092 0.07046 15 | 7 2100.00 274.50 78497.20 294.3413 0.07232 16 | 8 2400.00 272.56 75561.19 240.9863 0.07402 17 | 9 2700.00 270.61 72735.00 197.3029 0.07564 18 | 10 3000.00 268.66 70014.51 161.5379 0.07713 19 | 11 3300.00 266.71 67395.77 132.2561 0.07846 20 | 12 3600.00 264.76 64874.99 108.2821 0.07960 21 | 13 3900.00 262.82 62448.49 88.6539 0.08054 22 | 14 4200.00 260.87 60036.17 72.5837 0.08109 23 | 15 4500.00 258.92 57680.26 59.4265 0.08133 24 | 16 4800.00 256.97 55416.80 48.6543 0.08133 25 | 17 5100.00 255.03 53242.16 39.8348 0.08110 26 | 18 5400.00 253.08 51152.86 32.6139 0.08063 27 | 19 5700.00 251.13 49145.55 26.7020 0.07995 28 | 20 6000.00 249.19 47217.00 21.8618 0.07906 29 | 21 6300.00 247.24 45268.32 17.8989 0.07772 30 | 22 6600.00 245.30 43400.07 14.6544 0.07620 31 | 23 6900.00 243.35 41608.92 11.9980 0.07452 32 | 24 7200.00 241.40 39891.69 9.8231 0.07271 33 | 25 7500.00 239.46 38245.33 8.0425 0.07076 34 | 26 7800.00 237.51 36666.91 6.5846 0.06873 35 | 27 8100.00 235.57 35132.69 5.3910 0.06654 36 | 28 8400.00 233.62 33622.54 4.4138 0.06417 37 | 29 8700.00 231.68 32177.29 3.6137 0.06176 38 | 30 9000.00 229.73 30794.17 2.9587 0.05933 39 | 31 9300.00 227.79 29470.50 2.4224 0.05688 40 | 32 9600.00 225.84 28203.73 1.9833 0.05443 41 | 33 9900.00 223.90 26991.41 1.6238 0.05201 42 | 34 10200.00 222.59 25772.51 1.3294 0.04939 43 | 35 10500.00 221.60 24580.67 1.0884 0.04673 44 | 36 10800.00 220.61 23443.96 0.8911 0.04416 45 | 37 11100.00 219.62 22359.80 0.7296 0.04167 46 | 38 11400.00 218.63 21325.79 0.5973 0.03926 47 | 39 11700.00 217.64 20339.59 0.4891 0.03696 48 | 40 12000.00 216.65 19399.00 0.4004 0.03475 49 | 41 12300.00 216.65 18506.23 0.3278 0.03265 50 | 42 12600.00 216.65 17654.54 0.2684 0.03065 51 | 43 12900.00 216.65 16842.05 0.2198 0.02875 52 | 44 13200.00 216.65 16066.96 0.1799 0.02695 53 | 45 13500.00 216.65 15327.53 0.1473 0.02525 54 | 46 13800.00 216.65 14622.13 0.1206 0.02364 55 | 47 14100.00 216.65 13949.30 0.0987 0.02212 56 | 48 14400.00 216.65 13307.63 0.0808 0.02068 57 | 49 14700.00 216.65 12695.47 0.0662 0.01931 58 | 50 15000.00 216.65 12111.48 0.0542 0.01802 59 | 51 15300.00 216.65 11554.34 0.0444 0.01681 60 | 52 15600.00 216.65 11022.84 0.0363 0.01567 61 | 53 15900.00 216.65 10515.78 0.0297 0.01459 62 | 54 16200.00 216.65 10032.38 0.0243 0.01357 63 | 55 16500.00 216.65 9571.35 0.0199 0.01262 64 | 56 16800.00 216.65 9131.51 0.0163 0.01171 65 | 57 17100.00 216.65 8711.88 0.0134 0.01087 66 | 58 17400.00 216.65 8311.54 0.0109 0.01008 67 | 59 17700.00 216.65 7929.59 0.0090 0.00933 68 | 60 18000.00 216.65 7565.20 0.0073 0.00863 69 | 61 18300.00 216.65 7217.68 0.0060 0.00798 70 | 62 18600.00 216.65 6886.13 0.0049 0.00737 71 | 63 18900.00 216.65 6569.81 0.0040 0.00680 72 | 64 19200.00 216.65 6268.02 0.0033 0.00627 73 | 65 19500.00 216.65 5980.09 0.0027 0.00578 74 | 66 19800.00 216.65 5705.39 0.0022 0.00532 75 | 67 20100.00 216.75 5443.72 0.0018 0.00490 76 | 68 20400.00 217.03 5194.86 0.0015 0.00451 77 | 69 20700.00 217.32 4957.37 0.0012 0.00415 78 | 70 21000.00 217.61 4730.73 0.0010 0.00382 79 | 71 21300.00 217.90 4514.46 0.0008 0.00350 80 | 72 21600.00 218.19 4308.08 0.0007 0.00322 81 | 73 21900.00 218.48 4111.13 0.0005 0.00295 82 | 74 22200.00 218.77 3924.36 0.0004 0.00271 83 | 75 22500.00 219.07 3746.63 0.0004 0.00249 84 | 76 22800.00 219.37 3576.96 0.0003 0.00228 85 | 77 23100.00 219.67 3414.97 0.0002 0.00209 86 | 78 23400.00 219.96 3260.31 0.0002 0.00192 87 | 79 23700.00 220.26 3112.66 0.0002 0.00176 88 | 80 24000.00 220.56 2971.70 0.0001 0.00161 89 | 81 24300.00 220.86 2838.38 0.0001 0.00148 90 | 82 24600.00 221.16 2711.04 0.0001 0.00135 91 | 83 24900.00 221.45 2589.41 0.0001 0.00123 92 | 84 25200.00 221.75 2473.24 0.0001 0.00113 93 | 85 25500.00 222.05 2362.28 0.0000 0.00104 94 | 86 25800.00 222.35 2256.30 0.0000 0.00095 95 | 87 26100.00 222.64 2155.39 0.0000 0.00087 96 | 88 26400.00 222.94 2059.58 0.0000 0.00079 97 | 89 26700.00 223.24 1968.04 0.0000 0.00073 98 | 90 27000.00 223.54 1880.56 0.0000 0.00066 99 | 91 27300.00 223.83 1796.97 0.0000 0.00061 100 | 92 27600.00 224.13 1717.10 0.0000 0.00056 101 | 93 27900.00 224.43 1640.78 0.0000 0.00051 102 | 94 28200.00 224.73 1568.31 0.0000 0.00047 103 | 95 28500.00 225.02 1499.25 0.0000 0.00043 104 | 96 28800.00 225.32 1433.24 0.0000 0.00040 105 | 97 29100.00 225.62 1370.13 0.0000 0.00036 106 | 98 29400.00 225.91 1309.80 0.0000 0.00033 107 | 99 29700.00 226.21 1252.13 0.0000 0.00030 108 | 100 30000.00 226.51 1197.00 0.0000 0.00028 109 | 101 30300.00 227.11 1145.43 0.0000 0.00025 110 | 102 30600.00 227.71 1096.09 0.0000 0.00023 111 | 103 30900.00 228.31 1048.87 0.0000 0.00021 112 | 104 31200.00 228.91 1003.69 0.0000 0.00019 113 | 105 31500.00 229.51 960.45 0.0000 0.00018 114 | 106 31800.00 230.11 919.07 0.0000 0.00016 115 | 107 32100.00 230.71 879.48 0.0000 0.00015 116 | 108 32400.00 231.31 841.59 0.0000 0.00014 117 | 109 32700.00 231.91 805.34 0.0000 0.00013 118 | 110 33000.00 232.51 770.64 0.0000 0.00011 119 | 111 33300.00 233.11 737.44 0.0000 0.00011 120 | 112 33600.00 233.71 705.67 0.0000 0.00010 121 | 113 33900.00 234.31 675.27 0.0000 0.00009 122 | 114 34200.00 234.91 646.18 0.0000 0.00008 123 | 115 34500.00 235.51 618.35 0.0000 0.00007 124 | 116 34800.00 236.11 591.71 0.0000 0.00007 125 | 117 35100.00 236.79 566.67 0.0000 0.00006 126 | 118 35400.00 237.62 543.57 0.0000 0.00006 127 | 119 35700.00 238.45 521.41 0.0000 0.00005 128 | 120 36000.00 239.28 500.16 0.0000 0.00005 129 | 121 36300.00 240.11 479.77 0.0000 0.00005 130 | 122 36600.00 240.94 460.21 0.0000 0.00004 131 | 123 36900.00 241.77 441.45 0.0000 0.00004 132 | 124 37200.00 242.60 423.45 0.0000 0.00004 133 | 125 37500.00 243.43 406.19 0.0000 0.00003 134 | 126 37800.00 244.26 389.63 0.0000 0.00003 135 | 127 38100.00 245.09 373.74 0.0000 0.00003 136 | 128 38400.00 245.92 358.51 0.0000 0.00003 137 | 129 38700.00 246.75 343.89 0.0000 0.00002 138 | 130 39000.00 247.58 329.87 0.0000 0.00002 139 | 131 39300.00 248.41 316.42 0.0000 0.00002 140 | 132 39600.00 249.24 303.53 0.0000 0.00002 141 | 133 39900.00 250.07 291.15 0.0000 0.00002 142 | 134 40200.00 250.90 279.71 0.0000 0.00002 143 | 135 40500.00 251.73 268.93 0.0000 0.00001 144 | 136 40800.00 252.56 258.56 0.0000 0.00001 145 | 137 41100.00 253.39 248.59 0.0000 0.00001 146 | 138 41400.00 254.22 239.00 0.0000 0.00001 147 | 139 41700.00 255.05 229.79 0.0000 0.00001 148 | 140 42000.00 255.88 220.93 0.0000 0.00001 149 | 141 42300.00 256.70 212.41 0.0000 0.00001 150 | 142 42600.00 257.53 204.22 0.0000 0.00001 151 | 143 42900.00 258.36 196.34 0.0000 0.00001 152 | 144 43200.00 259.19 188.77 0.0000 0.00001 153 | 145 43500.00 260.02 181.49 0.0000 0.00001 154 | 146 43800.00 260.85 174.50 0.0000 0.00001 155 | 147 44100.00 261.68 167.77 0.0000 0.00001 156 | 148 44400.00 262.51 161.30 0.0000 0.00001 157 | 149 44700.00 263.34 155.08 0.0000 0.00001 158 | 150 45000.00 264.16 149.10 0.0000 0.00000 159 | 151 45300.00 264.55 143.61 0.0000 0.00000 160 | 152 45600.00 264.94 138.32 0.0000 0.00000 161 | 153 45900.00 265.33 133.23 0.0000 0.00000 162 | 154 46200.00 265.72 128.32 0.0000 0.00000 163 | 155 46500.00 266.11 123.60 0.0000 0.00000 164 | 156 46800.00 266.50 119.04 0.0000 0.00000 165 | 157 47100.00 266.89 114.66 0.0000 0.00000 166 | 158 47400.00 267.28 110.44 0.0000 0.00000 167 | 159 47700.00 267.67 106.37 0.0000 0.00000 168 | 160 48000.00 268.06 102.45 0.0000 0.00000 169 | 161 48300.00 268.44 98.68 0.0000 0.00000 170 | 162 48600.00 268.83 95.05 0.0000 0.00000 171 | 163 48900.00 269.22 91.55 0.0000 0.00000 172 | 164 49200.00 269.61 88.17 0.0000 0.00000 173 | 165 49500.00 270.00 84.93 0.0000 0.00000 174 | 166 49800.00 270.39 81.80 0.0000 0.00000 175 | 167 50100.00 270.45 78.78 0.0000 0.00000 176 | 168 50400.00 269.86 75.86 0.0000 0.00000 177 | 169 50700.00 269.27 73.05 0.0000 0.00000 178 | 170 51000.00 268.67 70.35 0.0000 0.00000 179 | 171 51300.00 268.08 67.74 0.0000 0.00000 180 | 172 51600.00 267.49 65.23 0.0000 0.00000 181 | 173 51900.00 266.90 62.81 0.0000 0.00000 182 | 174 52200.00 266.30 60.49 0.0000 0.00000 183 | 175 52500.00 265.71 58.25 0.0000 0.00000 184 | 176 52800.00 265.12 56.09 0.0000 0.00000 185 | 177 53100.00 264.53 54.01 0.0000 0.00000 186 | 178 53400.00 263.93 52.01 0.0000 0.00000 187 | 179 53700.00 263.34 50.08 0.0000 0.00000 188 | 180 54000.00 262.75 48.23 0.0000 0.00000 189 | 181 54300.00 262.15 46.44 0.0000 0.00000 190 | 182 54600.00 261.56 44.72 0.0000 0.00000 191 | 183 54900.00 260.97 43.06 0.0000 0.00000 192 | 184 55200.00 260.22 41.42 0.0000 0.00000 193 | 185 55500.00 259.40 39.81 0.0000 0.00000 194 | 186 55800.00 258.57 38.26 0.0000 0.00000 195 | 187 56100.00 257.75 36.77 0.0000 0.00000 196 | 188 56400.00 256.92 35.34 0.0000 0.00000 197 | 189 56700.00 256.10 33.97 0.0000 0.00000 198 | 190 57000.00 255.27 32.65 0.0000 0.00000 199 | 191 57300.00 254.45 31.38 0.0000 0.00000 200 | 192 57600.00 253.62 30.16 0.0000 0.00000 201 | 193 57900.00 252.80 28.98 0.0000 0.00000 202 | 194 58200.00 251.97 27.86 0.0000 0.00000 203 | 195 58500.00 251.15 26.77 0.0000 0.00000 204 | 196 58800.00 250.32 25.73 0.0000 0.00000 205 | 197 59100.00 249.50 24.73 0.0000 0.00000 206 | 198 59400.00 248.67 23.77 0.0000 0.00000 207 | 199 59700.00 247.85 22.85 0.0000 0.00000 208 | 200 60000.00 247.02 21.96 0.0000 0.00000 209 | 201 60300.00 246.20 21.06 0.0000 0.00000 210 | 202 60600.00 245.37 20.19 0.0000 0.00000 211 | 203 60900.00 244.55 19.37 0.0000 0.00000 212 | 204 61200.00 243.73 18.57 0.0000 0.00000 213 | 205 61500.00 242.90 17.81 0.0000 0.00000 214 | 206 61800.00 242.08 17.08 0.0000 0.00000 215 | 207 62100.00 241.25 16.38 0.0000 0.00000 216 | 208 62400.00 240.43 15.71 0.0000 0.00000 217 | 209 62700.00 239.61 15.06 0.0000 0.00000 218 | 210 63000.00 238.78 14.45 0.0000 0.00000 219 | 211 63300.00 237.96 13.85 0.0000 0.00000 220 | 212 63600.00 237.14 13.29 0.0000 0.00000 221 | 213 63900.00 236.31 12.74 0.0000 0.00000 222 | 214 64200.00 235.49 12.22 0.0000 0.00000 223 | 215 64500.00 234.66 11.72 0.0000 0.00000 224 | 216 64800.00 233.84 11.24 0.0000 0.00000 225 | 217 65100.00 233.02 10.77 0.0000 0.00000 226 | 218 65400.00 232.20 10.30 0.0000 0.00000 227 | 219 65700.00 231.37 9.86 0.0000 0.00000 228 | 220 66000.00 230.55 9.43 0.0000 0.00000 229 | 221 66300.00 229.73 9.02 0.0000 0.00000 230 | 222 66600.00 228.91 8.63 0.0000 0.00000 231 | 223 66900.00 228.08 8.25 0.0000 0.00000 232 | 224 67200.00 227.26 7.90 0.0000 0.00000 233 | 225 67500.00 226.44 7.55 0.0000 0.00000 234 | 226 67800.00 225.62 7.23 0.0000 0.00000 235 | 227 68100.00 224.79 6.91 0.0000 0.00000 236 | 228 68400.00 223.97 6.61 0.0000 0.00000 237 | 229 68700.00 223.15 6.33 0.0000 0.00000 238 | 230 69000.00 222.33 6.05 0.0000 0.00000 239 | 231 69300.00 221.50 5.79 0.0000 0.00000 240 | 232 69600.00 220.68 5.54 0.0000 0.00000 241 | 233 69900.00 219.86 5.30 0.0000 0.00000 242 | 234 70200.00 219.14 5.06 0.0000 0.00000 243 | 235 70500.00 218.47 4.83 0.0000 0.00000 244 | 236 70800.00 217.80 4.61 0.0000 0.00000 245 | 237 71100.00 217.12 4.40 0.0000 0.00000 246 | 238 71400.00 216.45 4.19 0.0000 0.00000 247 | 239 71700.00 215.78 4.00 0.0000 0.00000 248 | 240 72000.00 215.11 3.82 0.0000 0.00000 249 | 241 72300.00 214.44 3.64 0.0000 0.00000 250 | 242 72600.00 213.77 3.48 0.0000 0.00000 251 | 243 72900.00 213.10 3.32 0.0000 0.00000 252 | 244 73200.00 212.43 3.16 0.0000 0.00000 253 | 245 73500.00 211.75 3.02 0.0000 0.00000 254 | 246 73800.00 211.08 2.88 0.0000 0.00000 255 | 247 74100.00 210.41 2.75 0.0000 0.00000 256 | 248 74400.00 209.74 2.62 0.0000 0.00000 257 | 249 74700.00 209.07 2.50 0.0000 0.00000 258 | 250 75000.00 208.40 2.39 0.0000 0.00000 259 | 251 75300.00 207.81 2.27 0.0000 0.00000 260 | 252 75600.00 207.23 2.16 0.0000 0.00000 261 | 253 75900.00 206.64 2.06 0.0000 0.00000 262 | 254 76200.00 206.06 1.96 0.0000 0.00000 263 | 255 76500.00 205.47 1.87 0.0000 0.00000 264 | 256 76800.00 204.89 1.78 0.0000 0.00000 265 | 257 77100.00 204.30 1.69 0.0000 0.00000 266 | 258 77400.00 203.71 1.61 0.0000 0.00000 267 | 259 77700.00 203.13 1.53 0.0000 0.00000 268 | 260 78000.00 202.54 1.46 0.0000 0.00000 269 | 261 78300.00 201.96 1.39 0.0000 0.00000 270 | 262 78600.00 201.37 1.32 0.0000 0.00000 271 | 263 78900.00 200.79 1.26 0.0000 0.00000 272 | 264 79200.00 200.20 1.20 0.0000 0.00000 273 | 265 79500.00 199.62 1.14 0.0000 0.00000 274 | 266 79800.00 199.03 1.09 0.0000 0.00000 275 | 267 80100.00 198.44 1.03 0.0000 0.00000 276 | 268 80400.00 197.86 0.98 0.0000 0.00000 277 | 269 80700.00 197.27 0.93 0.0000 0.00000 278 | 270 81000.00 196.69 0.89 0.0000 0.00000 279 | 271 81300.00 196.11 0.84 0.0000 0.00000 280 | 272 81600.00 195.52 0.80 0.0000 0.00000 281 | 273 81900.00 194.94 0.76 0.0000 0.00000 282 | 274 82200.00 194.35 0.72 0.0000 0.00000 283 | 275 82500.00 193.77 0.68 0.0000 0.00000 284 | 276 82800.00 193.18 0.65 0.0000 0.00000 285 | 277 83100.00 192.60 0.62 0.0000 0.00000 286 | 278 83400.00 192.01 0.59 0.0000 0.00000 287 | 279 83700.00 191.43 0.56 0.0000 0.00000 288 | 280 84000.00 190.84 0.53 0.0000 0.00000 289 | 281 84300.00 190.26 0.50 0.0000 0.00000 290 | 282 84600.00 189.67 0.48 0.0000 0.00000 291 | 283 84900.00 189.09 0.45 0.0000 0.00000 292 | 284 85200.00 188.81 0.43 0.0000 0.00000 293 | 285 85500.00 188.69 0.41 0.0000 0.00000 294 | 286 85800.00 188.57 0.39 0.0000 0.00000 295 | 287 86100.00 188.45 0.37 0.0000 0.00000 296 | 288 86400.00 188.33 0.35 0.0000 0.00000 297 | 289 86700.00 188.21 0.33 0.0000 0.00000 298 | 290 87000.00 188.08 0.31 0.0000 0.00000 299 | 291 87300.00 187.96 0.30 0.0000 0.00000 300 | 292 87600.00 187.84 0.28 0.0000 0.00000 301 | 293 87900.00 187.72 0.27 0.0000 0.00000 302 | 294 88200.00 187.60 0.25 0.0000 0.00000 303 | 295 88500.00 187.48 0.24 0.0000 0.00000 304 | 296 88800.00 187.36 0.23 0.0000 0.00000 305 | 297 89100.00 187.23 0.22 0.0000 0.00000 306 | 298 89400.00 187.11 0.20 0.0000 0.00000 307 | 299 89700.00 186.99 0.19 0.0000 0.00000 308 | -------------------------------------------------------------------------------- /CO2 temperature analysis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Since the current concentrations $N$ of $CO_2$ in the atmosphere is so high, the direct\n", 8 | "dependence of the surface temperature $T$ on $N$ should be given approximately by\n", 9 | "$$\n", 10 | "T = T_0 + \\Delta T {\\log{N \\over N_0} \\over \\log 2}\\quad\\quad\\quad\\text{(1)}\n", 11 | "$$\n", 12 | "Here $T_0$ is a reference temperature, say the temperature in 1980 and $N_0$ is the corresponding\n", 13 | "atmospheric concentration of $CO_2$, say 340 ppm at Mauna Loa in 1980. From (1) we see\n", 14 | "that doubling the CO2 concentration (letting $N = 2N_0$) will increase T by the *temperature\n", 15 | "sensitivity* $\\Delta T$." 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": {}, 21 | "source": [ 22 | "Let's express $\\Delta T$:\n", 23 | "$$\n", 24 | "\\Delta T = (T - T_0) {\\log 2 \\over \\log (N/N_0)}\\quad\\quad\\quad\\text{(2)}\n", 25 | "$$" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "### $CO_2$ Data" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 1, 38 | "metadata": { 39 | "collapsed": false 40 | }, 41 | "outputs": [ 42 | { 43 | "name": "stdout", 44 | "output_type": "stream", 45 | "text": [ 46 | "Populating the interactive namespace from numpy and matplotlib\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "%pylab inline\n", 52 | "import urllib" 53 | ] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "metadata": {}, 58 | "source": [ 59 | "Let's fetch the raw data of $CO_2$ measurements at Mauna Loa from the `noaa.gov` website:" 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 2, 65 | "metadata": { 66 | "collapsed": false 67 | }, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/plain": [ 72 | "49915" 73 | ] 74 | }, 75 | "execution_count": 2, 76 | "metadata": {}, 77 | "output_type": "execute_result" 78 | } 79 | ], 80 | "source": [ 81 | "# Only execute this if you want to regenerate the downloaded file\n", 82 | "open(\"data/co2_mm_mlo.txt\", \"wb\").write(urllib.request.urlopen(\"ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt\").read())" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 3, 88 | "metadata": { 89 | "collapsed": false 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "D = loadtxt(\"data/co2_mm_mlo.txt\")\n", 94 | "years = D[:, 2]\n", 95 | "average = D[:, 3]\n", 96 | "interpolated = D[:, 4]\n", 97 | "trend = D[:, 5]" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "As explained in the file [co2_mm_mlo.txt](ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt), the `average` column are the raw data of $CO_2$ values averaged over a month, and some months are missing. The `trend` then subtracts \"seasonal cycle\" computed over a 7 year window. The missing values in `trend` are then linearly interpolated. Finally, the `average` column then contains the `trend` value plus average seasonal cycle (i.e. `average` and `interpolated` contain the same values except for missing months, which are \"intelligently\" interpolated). We should do this analysis ourselves in the notebook directly from the `average` data only, but for now let's reuse this analysis." 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 4, 110 | "metadata": { 111 | "collapsed": false 112 | }, 113 | "outputs": [ 114 | { 115 | "data": { 116 | "image/png": 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\n", 117 | "text/plain": [ 118 | "" 119 | ] 120 | }, 121 | "metadata": {}, 122 | "output_type": "display_data" 123 | } 124 | ], 125 | "source": [ 126 | "plot(years, interpolated, \"r-\", lw=1.5, label=\"monthly average\")\n", 127 | "plot(years, trend, \"k-\", label=\"trend\")\n", 128 | "xlabel(\"Year\")\n", 129 | "ylabel(\"$CO_2$ concentration [ppm]\")\n", 130 | "title(\"Atmospheric $CO_2$ concentrations at Mauna Loa\")\n", 131 | "legend(loc=\"upper left\");" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "Let's get numbers for $N$ (year 2010) and $N_0$ (year 1980) by reading it off the $y$-axis:" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 5, 144 | "metadata": { 145 | "collapsed": false 146 | }, 147 | "outputs": [ 148 | { 149 | "name": "stdout", 150 | "output_type": "stream", 151 | "text": [ 152 | "N0 = 338.09 ppm (year 1980.042)\n", 153 | "N = 388.41 ppm (year 2010.042)\n" 154 | ] 155 | } 156 | ], 157 | "source": [ 158 | "idx1980 = sum(years < 1980)\n", 159 | "idx2010 = sum(years < 2010)\n", 160 | "N0 = trend[idx1980]\n", 161 | "N = trend[idx2010]\n", 162 | "print(\"N0 = %.2f ppm (year %.3f)\" % (N0, years[idx1980]))\n", 163 | "print(\"N = %.2f ppm (year %.3f)\" % (N, years[idx2010]))" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "### Temperature changes" 171 | ] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": {}, 176 | "source": [ 177 | "Warming from Berkeley Earth in the last 30 years (see a separate notebook for this calculation):" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": 6, 183 | "metadata": { 184 | "collapsed": false 185 | }, 186 | "outputs": [], 187 | "source": [ 188 | "dTdt = 0.24995728742972512 # C / decade" 189 | ] 190 | }, 191 | { 192 | "cell_type": "markdown", 193 | "metadata": {}, 194 | "source": [ 195 | "Warming over the past 30 years using satellite measuremenets (see a separate notebook for this calculation):" 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 7, 201 | "metadata": { 202 | "collapsed": false 203 | }, 204 | "outputs": [], 205 | "source": [ 206 | "dTdt = 0.13764588789937693 # C / decade" 207 | ] 208 | }, 209 | { 210 | "cell_type": "markdown", 211 | "metadata": {}, 212 | "source": [ 213 | "We'll use the satellite measurements, as arguably they have less systematic errors.\n", 214 | "Temperature difference:" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 8, 220 | "metadata": { 221 | "collapsed": false 222 | }, 223 | "outputs": [ 224 | { 225 | "data": { 226 | "text/plain": [ 227 | "0.4129376636981308" 228 | ] 229 | }, 230 | "execution_count": 8, 231 | "metadata": {}, 232 | "output_type": "execute_result" 233 | } 234 | ], 235 | "source": [ 236 | "dT = dTdt * 3 # 3 decades\n", 237 | "dT" 238 | ] 239 | }, 240 | { 241 | "cell_type": "markdown", 242 | "metadata": {}, 243 | "source": [ 244 | "### Calculation of temperature sensitivity" 245 | ] 246 | }, 247 | { 248 | "cell_type": "markdown", 249 | "metadata": {}, 250 | "source": [ 251 | "From equation (2) we then directly calculate:" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 9, 257 | "metadata": { 258 | "collapsed": false 259 | }, 260 | "outputs": [ 261 | { 262 | "name": "stdout", 263 | "output_type": "stream", 264 | "text": [ 265 | "∆T = 2.0629039075597113 C\n" 266 | ] 267 | } 268 | ], 269 | "source": [ 270 | "from math import log\n", 271 | "deltaT = dT * log(2) / log(1.0*N/N0)\n", 272 | "print(\"∆T = \", deltaT, \"C\")" 273 | ] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "metadata": {}, 278 | "source": [ 279 | "This assumes that all warming (`dT` = $T-T_0$) has been caused by $CO_2$ in the past 30 years. If for example only $1/2$ of the warming was caused by $CO_2$, then $\\Delta T \\approx 1 C$. If on the other hand $CO_2$ stays longer in the atmosphere than other chemicals that might be temporarily cooling the atmosphere down by let's say $1/2$, then $\\Delta T \\approx 4 C$." 280 | ] 281 | } 282 | ], 283 | "metadata": { 284 | "kernelspec": { 285 | "display_name": "Python 3", 286 | "language": "python", 287 | "name": "python3" 288 | }, 289 | "language_info": { 290 | "codemirror_mode": { 291 | "name": "ipython", 292 | "version": 3 293 | }, 294 | "file_extension": ".py", 295 | "mimetype": "text/x-python", 296 | "name": "python", 297 | "nbconvert_exporter": "python", 298 | "pygments_lexer": "ipython3", 299 | "version": "3.6.4" 300 | } 301 | }, 302 | "nbformat": 4, 303 | "nbformat_minor": 2 304 | } 305 | --------------------------------------------------------------------------------