├── .gitignore ├── LICENSE ├── MANIFEST.in ├── README.md ├── nmc_met_diagnostic ├── __init__.py ├── dynamic.py ├── feature │ ├── __init__.py │ └── cyclone.py └── thermal.py ├── setup.py └── tests └── cyclone_test.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /MANIFEST.in: -------------------------------------------------------------------------------- 1 | include README.md 2 | include LICENSE 3 | recursive-exclude * __pycache__ 4 | recursive-exclude * *.pyc 5 | recursive-exclude * *.pyo 6 | recursive-exclude * *.orig 7 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 本程序库已经合并到[nmc_met_base](https://github.com/nmcdev/nmc_met_base)中去, 不再更新。 2 | 3 | 4 | # 气象诊断分析程序库 5 | 提供气象诊断分析程序，包括动力, 热力, 水汽和天气特征分析等。 6 | 7 | 8 | ## Dependencies 9 | Other required packages: 10 | 11 | - Numpy 12 | - Scipy 13 | - nmc_met_base, 请预先安装, 见https://github.com/nmcdev/nmc_met_base. 14 | 15 | ## Install 16 | Using the fellowing command to install packages: 17 | ``` 18 | pip install git+git://github.com/nmcdev/nmc_met_diagnostic.git --process-dependency-links 19 | ``` 20 | 21 | or download the package and install: 22 | ``` 23 | git clone --recursive https://github.com/nmcdev/nmc_met_diagnostic.git 24 | cd nmc_met_diagnostic 25 | python setup.py install 26 | ``` -------------------------------------------------------------------------------- /nmc_met_diagnostic/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | A collection of meteorological diagnostic and analysis functions. 3 | """ 4 | 5 | __author__ = "The R & D Center for Weather Forecasting Technology in NMC, CMA" 6 | __version__ = '0.1.0' 7 | -------------------------------------------------------------------------------- /nmc_met_diagnostic/dynamic.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2019 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Compute dynamic physical parameters on lon/lat grid. 8 | 9 | refer 10 | https://github.com/keltonhalbert/wrftools/blob/master/wrftools/variables/winds.py 11 | https://bitbucket.org/tmiyachi/pymet 12 | """ 13 | 14 | import numpy as np 15 | from nmc_met_base import arr, constants 16 | from nmc_met_base.grid import calc_dx_dy, dvardx, dvardy, d2vardx2, \ 17 | d2vardy2, dvardp, gradient_sphere, rot 18 | from nmc_met_diagnostic.thermal import pottemp, stability 19 | 20 | 21 | NA = np.newaxis 22 | a0 = constants.Re 23 | g = constants.g0 24 | PI = constants.pi 25 | d2r = PI/180. 26 | Rd = constants.rd 27 | 28 | 29 | def avort(uwind, vwind, lon, lat): 30 | """ 31 | Calculate absolute vorticity. 32 | refer to 33 | https://nbviewer.jupyter.org/url/fujita.valpo.edu/~kgoebber/NAM_vorticity.ipynb 34 | 35 | :param uwind: u direction wind. 36 | :param vwind: v direction wind. 37 | :param lon: grid longitude. 38 | :param lat: grid latitude. 39 | :return: relative vorticity. 40 | 41 | :Example: 42 | 43 | """ 44 | 45 | # grid space 46 | dx, dy = calc_dx_dy(lon, lat) 47 | 48 | # relative vorticity 49 | dvdx = np.gradient(vwind, dx, axis=1) 50 | dudy = np.gradient(uwind, dy, axis=0) 51 | cor = 2 * (7.292 * 10 ** (-5)) * np.sin(np.deg2rad(lat)) 52 | 53 | return dvdx - dudy + cor 54 | 55 | 56 | def absvrt(uwnd, vwnd, lon, lat, xdim, ydim, cyclic=True, sphere=True): 57 | """ 58 | Calculate absolute vorticity. 59 | 60 | :param uwnd: ndarray, u-component wind. 61 | :param vwnd: ndarray, v-component wind. 62 | :param lon: array_like, longitude. 63 | :param lat: array_like, latitude. 64 | :param xdim: the longitude dimension index 65 | :param ydim: the latitude dimension index 66 | :param cyclic: east-west boundary is cyclic 67 | :param sphere: sphere coordinate 68 | :return: 69 | """ 70 | 71 | u, v = np.ma.getdata(uwnd), np.ma.getdata(vwnd) 72 | mask = np.ma.getmask(uwnd) | np.ma.getmask(vwnd) 73 | ndim = u.ndim 74 | 75 | vor = rot(u, v, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere) 76 | f = arr.expand(constants.earth_f(lat), ndim, axis=ydim) 77 | out = f + vor 78 | 79 | out = np.ma.array(out, mask=mask) 80 | out = arr.mrollaxis(out, ydim, 0) 81 | out[0, ...] = np.ma.masked 82 | out[-1, ...] = np.ma.masked 83 | out = arr.mrollaxis(out, 0, ydim + 1) 84 | 85 | return out 86 | 87 | 88 | def ertelpv(uwnd, vwnd, temp, lon, lat, lev, xdim, ydim, zdim, 89 | cyclic=True, punit=100., sphere=True): 90 | """ 91 | Calculate Ertel potential vorticity. 92 | Hoskins, B.J., M.E. McIntyre and A.E. Robertson, 1985: 93 | On the use and significance of isentropic potential 94 | vorticity maps, `QJRMS`, 111, 877-946, 95 | 96 | 97 | :param uwnd: ndarray, u component wind [m/s]. 98 | :param vwnd: ndarray, v component wind [m/s]. 99 | :param temp: ndarray, temperature [K]. 100 | :param lon: array_like, longitude [degrees]. 101 | :param lat: array_like, latitude [degrees]. 102 | :param lev: array_like, pressure level [punit*Pa]. 103 | :param xdim: west-east axis 104 | :param ydim: south-north axis 105 | :param zdim: vertical axis 106 | :param cyclic: west-east cyclic boundary 107 | :param punit: pressure level unit 108 | :param sphere: sphere coordinates. 109 | :return: 110 | """ 111 | 112 | u, v, t = np.ma.getdata(uwnd), np.ma.getdata(vwnd), np.ma.getdata(temp) 113 | mask = np.ma.getmask(uwnd) | np.ma.getmask(vwnd) | np.ma.getmask(temp) 114 | ndim = u.ndim 115 | 116 | # potential temperature 117 | theta = pottemp(t, lev, zdim, punit=punit) 118 | 119 | # partial derivation 120 | dthdp = dvardp(theta, lev, zdim, punit=punit) 121 | dudp = dvardp(u, lev, zdim, punit=punit) 122 | dvdp = dvardp(v, lev, zdim, punit=punit) 123 | 124 | dthdx = dvardx(theta, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere) 125 | dthdy = dvardy(theta, lat, ydim, sphere=sphere) 126 | 127 | # absolute vorticity 128 | vor = rot(u, v, lon, lat, xdim, ydim, cyclic=cyclic, sphere=sphere) 129 | f = arr.expand(constants.earth_f(lat), ndim, axis=ydim) 130 | avor = f + vor 131 | 132 | out = -g * (avor*dthdp - (dthdx*dvdp-dthdy*dudp)) 133 | 134 | out = np.ma.array(out, mask=mask) 135 | out = arr.mrollaxis(out, ydim, 0) 136 | out[0, ...] = np.ma.masked 137 | out[-1, ...] = np.ma.masked 138 | out = arr.mrollaxis(out, 0, ydim+1) 139 | 140 | return out 141 | 142 | 143 | def vertical_vorticity_latlon(u, v, lats, lons, abs_opt=False): 144 | """ 145 | Calculate the vertical vorticity on a latitude/longitude grid. 146 | 147 | :param u: 2 dimensional u wind arrays, dimensioned by (lats,lons). 148 | :param v: 2 dimensional v wind arrays, dimensioned by (lats,lons). 149 | :param lats: latitude vector 150 | :param lons: longitude vector 151 | :param abs_opt: True to compute absolute vorticity, 152 | False for relative vorticity only 153 | :return: Two dimensional array of vertical vorticity. 154 | """ 155 | 156 | dudy, dudx = gradient_sphere(u, lats, lons) 157 | dvdy, dvdx = gradient_sphere(v, lats, lons) 158 | 159 | if abs_opt: 160 | # 2D latitude array 161 | glats = np.zeros_like(u).astype('f') 162 | for jj in range(0, len(lats)): 163 | glats[jj, :] = lats[jj] 164 | 165 | # Coriolis parameter 166 | f = 2 * 7.292e-05 * np.sin(np.deg2rad(glats)) 167 | else: 168 | f = 0. 169 | 170 | vert_vort = dvdx - dudy + f 171 | 172 | return vert_vort 173 | 174 | 175 | def epv_sphere(theta, u, v, levs, lats, lons): 176 | """ 177 | Computes the Ertel Potential Vorticity (PV) on a latitude/longitude grid. 178 | 179 | :param theta: 3D potential temperature array on isobaric levels 180 | :param u: 3D u components of the horizontal wind on isobaric levels 181 | :param v: 3D v components of the horizontal wind on isobaric levels 182 | :param levs: 1D pressure vectors 183 | :param lats: 1D latitude vectors 184 | :param lons: 1D longitude vectors 185 | :return: Ertel PV in potential vorticity units (PVU) 186 | """ 187 | 188 | iz, iy, ix = theta.shape 189 | 190 | dthdp, dthdy, dthdx = gradient_sphere(theta, levs, lats, lons) 191 | dudp, dudy, dudx = gradient_sphere(u, levs, lats, lons) 192 | dvdp, dvdy, dvdx = gradient_sphere(v, levs, lats, lons) 193 | 194 | abvort = np.zeros_like(theta).astype('f') 195 | for kk in range(0, iz): 196 | abvort[kk, :, :] = vertical_vorticity_latlon( 197 | u[kk, :, :].squeeze(), v[kk, :, :].squeeze(), 198 | lats, lons, abs_opt=True) 199 | 200 | epv = (-9.81 * (-dvdp * dthdx - dudp * dthdy + abvort * dthdp)) * 1.0e6 201 | return epv 202 | 203 | 204 | def tnflux2d(U, V, strm, lon, lat, xdim, ydim, cyclic=True, limit=100): 205 | """ 206 | Takaya & Nakamura (2001) 计算水平等压面上的波活动度. 207 | Takaya, K and H. Nakamura, 2001: A formulation of a phase-independent 208 | wave-activity flux for stationary and migratory quasigeostrophic eddies 209 | on a zonally varying basic flow, `JAS`, 58, 608-627. 210 | http://journals.ametsoc.org/doi/abs/10.1175/1520-0469%282001%29058%3C0608%3AAFOAPI%3E2.0.CO%3B2 211 | 212 | :param U: u component wind [m/s]. 213 | :param V: v component wind [m/s]. 214 | :param strm: stream function [m^2/s]. 215 | :param lon: longitude degree. 216 | :param lat: latitude degree. 217 | :param xdim: longitude dimension index. 218 | :param ydim: latitude dimension index. 219 | :param cyclic: east-west cyclic boundary. 220 | :param limit: 221 | :return: 222 | """ 223 | 224 | U, V = np.asarray(U), np.asarray(V) 225 | ndim = U.ndim 226 | 227 | dstrmdx = dvardx(strm, lon, lat, xdim, ydim, cyclic=cyclic) 228 | dstrmdy = dvardy(strm, lat, ydim) 229 | d2strmdx2 = d2vardx2(strm, lon, lat, xdim, ydim, cyclic=cyclic) 230 | d2strmdy2 = d2vardy2(strm, lat, ydim) 231 | d2strmdxdy = dvardy( 232 | dvardx(strm, lon, lat, xdim, ydim, cyclic=cyclic), 233 | lat, ydim) 234 | 235 | tnx = U * (dstrmdx ** 2 - strm * d2strmdx2) + \ 236 | V * (dstrmdx * dstrmdy - strm * d2strmdxdy) 237 | tny = U * (dstrmdx * dstrmdy - strm * d2strmdxdy) + \ 238 | V * (dstrmdy ** 2 - strm * d2strmdy2) 239 | 240 | tnx = 0.5 * tnx / np.abs(U + 1j * V) 241 | tny = 0.5 * tny / np.abs(U + 1j * V) 242 | 243 | tnxy = np.sqrt(tnx ** 2 + tny ** 2) 244 | tnx = np.ma.asarray(tnx) 245 | tny = np.ma.asarray(tny) 246 | tnx[tnxy > limit] = np.ma.masked 247 | tny[tnxy > limit] = np.ma.masked 248 | tnx[U < 0] = np.ma.masked 249 | tny[U < 0] = np.ma.masked 250 | 251 | return tnx, tny 252 | 253 | 254 | def tnflux3d(U, V, T, strm, lon, lat, lev, xdim, ydim, zdim, 255 | cyclic=True, limit=100, punit=100.): 256 | """ 257 | Takaya & Nakamura (2001) 计算等压面上的波活动度. 258 | Takaya, K and H. Nakamura, 2001: A formulation of a phase-independent 259 | wave-activity flux for stationary and migratory quasigeostrophic eddies 260 | on a zonally varying basic flow, `JAS`, 58, 608-627. 261 | http://journals.ametsoc.org/doi/abs/10.1175/1520-0469%282001%29058%3C0608%3AAFOAPI%3E2.0.CO%3B2 262 | 263 | :param U: u component wind [m/s]. 264 | :param V: v component wind [m/s]. 265 | :param T: climate temperature [K]. 266 | :param strm: stream function bias [m^2/s] 267 | :param lon: longitude degree. 268 | :param lat: latitude degree. 269 | :param lev: level pressure. 270 | :param xdim: longitude dimension index. 271 | :param ydim: latitude dimension index. 272 | :param zdim: level dimension index. 273 | :param cyclic: east-west cyclic boundary. 274 | :param limit: 275 | :param punit: level pressure unit. 276 | :return: east-west, south-north, vertical component. 277 | """ 278 | 279 | U, V, T = np.asarray(U), np.asarray(V), np.asarray(T) 280 | ndim = U.ndim 281 | S = stability(T, lev, zdim, punit=punit) 282 | f = arr.expand(constants.earth_f(lat), ndim, axis=ydim) 283 | 284 | dstrmdx = dvardx(strm, lon, lat, xdim, ydim, cyclic=cyclic) 285 | dstrmdy = dvardy(strm, lat, ydim) 286 | dstrmdp = dvardp(strm, lev, zdim, punit=punit) 287 | d2strmdx2 = d2vardx2(strm, lon, lat, xdim, ydim, cyclic=cyclic) 288 | d2strmdy2 = d2vardy2(strm, lat, ydim) 289 | d2strmdxdy = dvardy(dstrmdx, lat, ydim) 290 | d2strmdxdp = dvardx(dstrmdp, lon, lat, xdim, ydim, cyclic=True) 291 | d2strmdydp = dvardy(dstrmdp, lat, ydim) 292 | 293 | tnx = U * (dstrmdx ** 2 - strm * d2strmdx2) + \ 294 | V * (dstrmdx * dstrmdy - strm * d2strmdxdy) 295 | tny = U * (dstrmdx * dstrmdy - strm * d2strmdxdy) + \ 296 | V * (dstrmdy ** 2 - strm * d2strmdy2) 297 | tnz = f ** 2 / S ** 2 * ( 298 | U * (dstrmdx * dstrmdp - strm * d2strmdxdp) - 299 | V * (dstrmdy * dstrmdp - strm * d2strmdydp)) 300 | 301 | tnx = 0.5 * tnx / np.abs(U + 1j * V) 302 | tny = 0.5 * tny / np.abs(U + 1j * V) 303 | 304 | tnxy = np.sqrt(tnx ** 2 + tny ** 2) 305 | tnx = np.ma.asarray(tnx) 306 | tny = np.ma.asarray(tny) 307 | tnz = np.ma.asarray(tnz) 308 | tnx[(U < 0) | (tnxy > limit)] = np.ma.masked 309 | tny[(U < 0) | (tnxy > limit)] = np.ma.masked 310 | tnz[(U < 0) | (tnxy > limit)] = np.ma.masked 311 | 312 | return tnx, tny, tnz 313 | 314 | 315 | def w_to_omega(w, pres, tempk): 316 | """ 317 | Compute vertical velocity on isobaric surfaces 318 | 319 | :param w: Input vertical velocity (m s-1) 320 | :param pres: Input half level pressures (full field) in Pa 321 | :param tempk: Input temperature (K) 322 | :return: 323 | """ 324 | 325 | omeg = -((pres * g) / (Rd * tempk)) * w 326 | return omeg 327 | -------------------------------------------------------------------------------- /nmc_met_diagnostic/feature/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/nmcdev/nmc_met_diagnostic/e90b950dbba6775a101fb93898156962c393fc8f/nmc_met_diagnostic/feature/__init__.py -------------------------------------------------------------------------------- /nmc_met_diagnostic/feature/cyclone.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2019 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Cyclone identification and track methods. 8 | """ 9 | 10 | import numpy as np 11 | from nmc_met_base.geographical import haversine_np 12 | from nmc_met_base.math import extreme_2d 13 | 14 | 15 | def _elim_mult_centers(in_press, in_lon, in_lat, search_rad=800e3, type=-1): 16 | """ 17 | ; Given a vector of pressures, and corresponding vectors of lon. and lat. 18 | ; where those pressures are at, looks to see if any two points are "too" 19 | ; close to each other. If they are, then the one with the lower (or 20 | ; higher, as set by the Type keyword) pressure is retained. The 1-D 21 | ; vector returned is of the locations (in terms of subscripting of the 22 | ; original pressure vector) that have been retained. 23 | ; 24 | ; This function is typically used for eliminating multiple high or low 25 | ; centers that has been identified by an automated pressure center 26 | ; finding algorithm. 27 | 28 | :param in_press: Pressure (in hPa) at locations defined by in_lon and 29 | in_lat. Floating or double array of any dimension. 30 | Unchanged by procedure. 31 | :param in_lon: Longitude of points given by in_press (in decimal deg). 32 | Floating or double array. Same dimensions as in_press. 33 | :param in_lat: Latitude of points given by in_press (in decimal deg). 34 | Floating or double array. Same dimensions as in_press. 35 | :param search_rad: Radius defining the region from a point the procedure 36 | searches to try and determine whether a given location 37 | is too close to other locations. In meters. Not 38 | changed by function. This can either be a scalar 39 | (which is applied to all locations) or a vector of the 40 | same size as in_press that gives Search_Rad to use 41 | for each location. Default is 800e3 meters. 42 | :param type: Required. If set to 1, then the function retains the 43 | higher of the pressures; if set to -1, then the function 44 | retains the lower of the pressures. 45 | :return: Vector of the locations of retained locations, as 46 | described above. Created. 1-D integer vector of 47 | array indices, in terms of the input array in_press. 48 | If none of the pressures are "too close" to each other, 49 | out_loc will end up being just a vector of the indices 50 | of all the elements in in_press. 51 | """ 52 | 53 | # protect input 54 | press = in_press 55 | lon = in_lon 56 | lat = in_lat 57 | npress = press.size 58 | 59 | ''' 60 | ; --------------------- Find Multiple Center Situations ------------------- 61 | ; 62 | ; Method: All permutations of the values of press are tested pairwise 63 | ; against to see each other to see if they are less than Search_Rad apart. 64 | ; If so, it is assumed that they are not actually separate systems, and 65 | ; the value with the lowest (highest) value is retained as describing the 66 | ; true low (high) center. 67 | ; 68 | ; NB: If a case exists where the min. (or max.) of the points that are 69 | ; within Search_Rad of each other applies to more than one point, it is 70 | ; assumed that both are centers, and a warning message is printed out. 71 | ; This should be an extremely rare situation, since press is floating pt. 72 | ''' 73 | out_loc = np.array([], dtype=np.int64) 74 | for i in range(npress): 75 | dist_from_i = haversine_np( 76 | np.full(npress, lon[i]), np.full(npress, lat[i]), lon, lat) 77 | same_loc = np.flatnonzero(dist_from_i <= search_rad) 78 | 79 | if same_loc.size == 1: 80 | out_loc = np.append(out_loc, same_loc) 81 | 82 | if same_loc.size > 1: 83 | same_press = press[same_loc] 84 | if type > 0: 85 | keep_pts = np.argmax(same_press) 86 | else: 87 | keep_pts = np.argmin(same_press) 88 | out_loc = np.append(out_loc, same_loc[keep_pts]) 89 | 90 | # ---------------------------- Clean-Up and Output ------------------------ 91 | if out_loc.size == 0: 92 | out_loc = np.arange(npress) 93 | else: 94 | out_loc = np.unique(out_loc) 95 | 96 | return out_loc 97 | 98 | 99 | def loc(in_press, in_lon, in_lat, edge_distance=800e3, 100 | lr_periodic=False, tb_periodic=False, 101 | search_rad_max=1200e3, search_rad_min=400e3, 102 | search_rad_ndiv=3, slp_diff_test=2, limit=None, 103 | ref_point=None, relax=1.0): 104 | """ 105 | ; Given a lat.-lon. grid of sea level pressure, fctn. finds where the 106 | ; centers of cyclones are (using a form of the Serreze (1995) and Serreze 107 | ; et al. (1997) algorithms) and returns a vector of the locations of 108 | ; centers, in 1-D array index form. This function supports "pseudo- 109 | ; arbitrary" spacing: For the purposes of calculating local maximum, it 110 | ; is assumed that the grid is a 2-D grid where each internal point is 111 | ; surrounded by 8 points). The boundaries of the 2-D array are also 112 | ; assumed to be the boundaries of the domain. However, no other 113 | ; assumptions, including in terms of grid size and spacing, are made. 114 | ; 115 | ; If either your top/bottom or left/right boundaries are periodic, see 116 | ; keyword list discussion of Lr_Periodic and Tb_Periodic below. Note 117 | ; although these keywords are included, I have not tested whether 118 | ; specifying those keywords will properly detect cyclones at periodic 119 | ; boundaries; I have only tested whether the specification of those 120 | ; keywords will turn on or off the edge effect filter. 121 | http://www.johnny-lin.com/idl_code/cyclone_loc.pro 122 | 123 | - References: 124 | * Serreze, M. C. (1995), Climatological aspects of cyclone development 125 | and decay in the Arctic, Atmos.-Oc., v. 33, pp. 1-23; 126 | * Serreze, M. C., F. Carse, R. G. Barry, and J. C. Rogers (1997), 127 | Icelandic low cyclone activity: Climatological features, linkages 128 | with the NAO, and relationships with recent changes in the Northern 129 | Hemisphere circulation, J. Clim., v. 10, pp. 453-464. 130 | 131 | Notices: 132 | 1 参数的选择对于最后的结果非常重要, 最好将典型的气旋显示出来, 133 | 主观测量要识别气旋的大小, 获得参数的值. 134 | 2 search_rad_min和slp_diff_test的设置经验上更为重要一些. 135 | 3 典型气旋中心常有多个低极值点, 因此search_rad_min设置太小会造成 136 | 同一个气旋多个气旋被识别出来, search_rad_min最好能够覆盖 137 | 气旋的中心部位. 138 | 4 slp_diff_test要根据search_rad_min的距离来设置, 不能设置太大, 139 | 会造成很难满足条件而无法识别出气旋, 也不能设置太小而把太多的 140 | 弱气旋包含进来, 一般考虑0.25/100km. 141 | 5 search_rad_max最好包含气旋的最外围, 但其主要作用是保证有4以上的点 142 | 高于中心气压, 这个一般很好满足, 因此不太重要. 143 | 6 search_rad_ndiv就用默认的3就行, 一般第一个圆环就能满足条件. 144 | 145 | :param in_press: Sea level pressure (in hPa) at grid defined by in_lon and 146 | in_lat. 2-D floating or double array. 147 | :param in_lon: Longitude of grid given by in_press (in decimal deg), 148 | 1D array. 149 | :param in_lat: Latitude of grid given by in_press (in decimal deg), 150 | 1D array. 151 | :param edge_distance: Distance defining how from the edge is the "good" 152 | domain to be considered; if you're within this 153 | distance of the edge (and you're boundary is 154 | non-periodic), it's assumed that cyclone centers 155 | cannot be detected there. In meters. Not changed 156 | by function. Scalar. Default is 800 kilometers. 157 | :param lr_periodic: If LR_PERIODIC is true, the left and right (i.e. col. 158 | at rows 0 and NX_P-1) bound. are assumed periodic, 159 | and the edge effect for IDing cyclones (i.e. that 160 | cyclones found near the edge are not valid) is assumed 161 | not to apply. 162 | :param tb_periodic: If TB_PERIODIC is true, the top and bottom bound. 163 | (rows at col. 0 and NY_P-1) are assumed periodic. If 164 | neither are true (default), none of the bound. are 165 | assumed periodic. 166 | :param search_rad_max: Max. radius defining the region from a point the 167 | procedure searches to try and determine whether a 168 | given location is a low pressure center. In meters. 169 | Not changed by function. This can either be a scalar 170 | (which is applied to all locations) or a vector of 171 | the same size as in_press of Search_Rad_Max to use 172 | for each location. Default is 1200e3 meters. 173 | :param search_rad_min: Min. radius defining the region from a point the 174 | procedure searches to determine whether a given 175 | location is a low pressure center. In meters. Not 176 | changed by function. This can either be a scalar 177 | (which is applied to all locations) or a vector of 178 | the same size as in_press that gives Search_Rad_Min 179 | to use for each location. Default is 400e3 meters. 180 | This value is also used to test for multiple lows 181 | (see commenting below). 182 | :param search_rad_ndiv: Integer number of shells between Search_Rad_Min and 183 | Search_Rad_Max to search. Scalar. Default is 3. 184 | :param slp_diff_test: A low pressure center is identified if it is entirely 185 | surrounded by grid points in the region between 186 | Search_Rad_Min and Search_Rad_Max that are all higher 187 | in SLP than the point in question by a min. of 188 | Slp_Diff_Test. In hPa. Not changed by function. This 189 | can either be a scalar (which is applied to all 190 | locations) or a vector of the same size as in_press 191 | of slp_diff_test to use for each location. 192 | Default is 2 hPa. 193 | :param limit: give a region limit where cyclones can be identified, 194 | format is [lonmin, lonmax, latmin, latmax]. 195 | if None, do not think limit region. 196 | :param ref_point: if is not None, will return the nearest cyclone to the 197 | reference point. 198 | :param relax: value 0~1.0, the proportion of shell grid points which meet 199 | the pressure slp_diff_test. 200 | 201 | :return: [ncyclones, 3] array, each cyclone 202 | [cent_lon, cent_lat,cent_pressure] 203 | 204 | """ 205 | 206 | # protect input 207 | press = in_press.ravel() 208 | lons, lats = np.meshgrid(in_lon, in_lat) 209 | npress = press.size 210 | 211 | # 212 | # Start cycling through each point in Entire Domain 213 | tmp_loc = [] 214 | for i in range(npress): 215 | # check limit region 216 | if limit is not None: 217 | if (lons.ravel()[i] < limit[0]) or (lons.ravel()[i] > limit[1]) \ 218 | or (lats.ravel()[i] < limit[2]) or \ 219 | (lats.ravel()[i] > limit[3]): 220 | continue 221 | 222 | ''' 223 | ; ------ What Array Indices Surround Each Index for a Shell of Points - 224 | ; 225 | ; shell_loc_for_i is a vector of the subscripts of the points that 226 | ; are within the region defined by search_rad_min and search_rad_top of 227 | ; the element i, and are not i itself. 228 | ; 229 | ; For each point in the spatial domain, we search through a number of 230 | ; shells (where search_rad_top expands outwards by search_rad_ndiv 231 | ; steps until it reaches search_rad_max). This enables more 232 | ; flexibility in finding centers of various sizes. 233 | ''' 234 | 235 | # distance of each point from i 236 | dist_from_i = haversine_np( 237 | np.full(npress, lons.ravel()[i]), np.full(npress, lats.ravel()[i]), 238 | lons.ravel(), lats.ravel()) 239 | 240 | # make array of the lower limit of of the search shell 241 | incr = (search_rad_max - search_rad_min) / search_rad_ndiv 242 | search_rad_top = (np.arange(search_rad_ndiv) + 1.0) * incr + \ 243 | search_rad_min 244 | 245 | # Cycle through each search_rad division 246 | for ndiv in range(search_rad_ndiv): 247 | shell_loc_for_i = np.flatnonzero( 248 | (dist_from_i <= search_rad_top[ndiv]) & 249 | (dist_from_i >= search_rad_min)) 250 | npts_shell = shell_loc_for_i.size 251 | 252 | if npts_shell == 0: 253 | print("*** warning--domain may be too spread out ***") 254 | 255 | ''' 256 | ; --------------- Find Locations That Pass the Low Pressure Test -- 257 | ; 258 | ; Method: For each location, check that the pressure of all the 259 | ; points in the shell around i, defined by search_rad_top and 260 | ; search_rad_min, is slp_diff_test higher. If so, and the shell 261 | ; of points around that location is >= 4 (which is a test to help 262 | ; make sure the location isn't being examined on the basis of just 263 | ; a few points), then that location is labeled as passing the low 264 | ; pressure test. 265 | ; 266 | ; Note that since the shell is based upon distance which is based 267 | ; on lat/lon, this low pressure test automatically accommodates for 268 | ; periodic bound., if the bounds are periodic. For non-periodic 269 | ; bounds, some edge points may pass this test, and thus must be 270 | ; removed later on in the edge effects removal section. 271 | ''' 272 | if npts_shell > 0: 273 | slp_diff = press[shell_loc_for_i] - press[i] 274 | tmp = np.flatnonzero(slp_diff >= slp_diff_test) 275 | if (tmp.size >= npts_shell*relax) and (npts_shell >= 4): 276 | tmp_loc.append(i) 277 | break # pass the low pressure test 278 | 279 | ''' 280 | ; ----------------- Identify Low Pressure Centers Candidates -------------- 281 | ; 282 | ; Method: From the locations that pass the SLP difference test, we find 283 | ; which ones could be low pressure centers by finding the locations that 284 | ; are local minimums in SLP. Note low_loc values are in units of indices 285 | ; of the orig. pressure array. 286 | ''' 287 | if len(tmp_loc) == 0: 288 | return None 289 | 290 | tmp_loc = np.array(tmp_loc) 291 | test_slp = np.full(in_press.shape, 100000.0) 292 | test_slp.ravel()[tmp_loc] = press.ravel()[tmp_loc] 293 | 294 | # 会去掉几个相邻的低压中心候选点，找一个最低气压的低压中心. 295 | low_loc = extreme_2d(test_slp, -1, edge=True) 296 | 297 | ''' 298 | ; ----- Test For Multiple Systems In a Region Defined By Search_Rad_Min -- 299 | ; 300 | ; Method: If two low centers identified in low_loc are less than 301 | ; Search_Rad_Min apart, it is assumed that they are not actually 302 | ; separate systems, and the value with the lowest SLP value is 303 | ; retained as describing the true low center. 304 | ''' 305 | if low_loc is not None: 306 | test_slp_ll = test_slp.ravel()[low_loc] 307 | lon_ll = lons.ravel()[low_loc] 308 | lat_ll = lats.ravel()[low_loc] 309 | emc_loc = _elim_mult_centers( 310 | test_slp_ll, lon_ll, lat_ll, type=-1, search_rad=search_rad_min) 311 | out_loc = low_loc[emc_loc] 312 | else: 313 | return None 314 | 315 | ''' 316 | ; --------------------------- Eliminate Edge Points ----------------------- 317 | ; 318 | ; Method: Eliminate all out_loc candidate points that are a distance 319 | ; Edge_Distance away from the edge, for the boundaries that are non- 320 | ; periodic. 321 | ''' 322 | # Flag to elim. edge: default is on (=1) 323 | ielim_flag = True 324 | 325 | if not lr_periodic and not tb_periodic: 326 | edge_lon = np.concatenate( 327 | (lons[0, :], lons[-1, :], lons[:, 0], lons[:, -1])) 328 | edge_lat = np.concatenate( 329 | (lats[0, :], lats[-1, :], lats[:, 0], lats[:, -1])) 330 | elif lr_periodic and not tb_periodic: 331 | edge_lon = np.concatenate((lons[:, 0], lons[:, -1])) 332 | edge_lat = np.concatenate((lats[:, 0], lats[:, -1])) 333 | elif not lr_periodic and tb_periodic: 334 | edge_lon = np.concatenate((lons[0, :], lons[-1, :])) 335 | edge_lat = np.concatenate((lats[0, :], lats[-1, :])) 336 | elif lr_periodic and tb_periodic: 337 | # set flag to elim. edge to off 338 | ielim_flag = False 339 | else: 340 | print('error--bad periodic keywords') 341 | 342 | # Case elim. at least some edges 343 | if ielim_flag: 344 | for i, iloc in np.ndenumerate(out_loc): 345 | dist_from_ol_i = haversine_np( 346 | np.full(edge_lon.size, lons.ravel()[iloc]), 347 | np.full(edge_lat.size, lats.ravel()[iloc]), 348 | edge_lon, edge_lat) 349 | 350 | tmp = np.flatnonzero(dist_from_ol_i <= edge_distance) 351 | if tmp.size > 0: 352 | out_loc[i] = -1 353 | 354 | # keep only those points not near edge: 355 | good_pts = np.flatnonzero(out_loc >= 0) 356 | if good_pts.size > 0: 357 | out_loc = out_loc[good_pts] 358 | else: 359 | return None 360 | 361 | # clean up and sort 362 | cent_lon = lons.ravel()[out_loc] 363 | cent_lat = lats.ravel()[out_loc] 364 | cent_press = press[out_loc] 365 | sort_idx = np.argsort(cent_press) 366 | cent_press = cent_press[sort_idx] 367 | cent_lon = cent_lon[sort_idx] 368 | cent_lat = cent_lat[sort_idx] 369 | if ref_point is None: 370 | return np.stack((cent_lon, cent_lat, cent_press), axis=1) 371 | else: 372 | dist_from_refer = haversine_np( 373 | np.full(cent_press.size, ref_point[0]), 374 | np.full(cent_press.size, ref_point[1]), cent_lon, cent_lat) 375 | idx = np.argmin(dist_from_refer) 376 | return np.array( 377 | [cent_lon[idx], cent_lat[idx], cent_press[idx]]).reshape([1, 3]) 378 | -------------------------------------------------------------------------------- /nmc_met_diagnostic/thermal.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | # Copyright (c) 2019 NMC Developers. 4 | # Distributed under the terms of the GPL V3 License. 5 | 6 | """ 7 | Calculate thermal parameters. 8 | """ 9 | 10 | import numpy as np 11 | from nmc_met_base import arr, constants, grid 12 | 13 | Cp = constants.cp 14 | Cv = constants.cv 15 | Rd = constants.rd 16 | Rv = constants.rv 17 | RvRd = Rv / Rd 18 | g = constants.g0 19 | L = constants.Lv 20 | Lf = constants.Lf 21 | Talt = constants.Talt 22 | Tfrez = constants.Tfrez 23 | To = constants.T0 24 | Po = constants.P0 25 | Pr = constants.Pr 26 | lapsesta = constants.lapsesta 27 | kappa = constants.kappa 28 | epsil = constants.epsil 29 | pi = constants.pi 30 | pid = pi/180 31 | R_earth = constants.Re 32 | omeg_e = constants.omega 33 | eo = constants.eo 34 | missval = -9999 35 | eps = 2.2204e-16 36 | 37 | 38 | def pottemp(temp, lev, zdim, punit=100., p0=100000.): 39 | """ 40 | Calculate potential temperature. 41 | 42 | :param temp: array_like, temperature [K]. 43 | :param lev: array_like, level [punit*Pa]. 44 | :param zdim: vertical dimensions. 45 | :param punit: pressure level punit. 46 | :param p0: reference pressure. 47 | :return: ndarray. 48 | """ 49 | 50 | temp = np.asarray(temp) 51 | ndim = temp.ndim 52 | p = arr.expand(lev, ndim, axis=zdim) 53 | 54 | out = temp * ((p0/p/punit)**kappa) 55 | 56 | return out 57 | 58 | 59 | def thetae(thta, temp, qv): 60 | """ 61 | Compute equivalent potential temperature 62 | 63 | :param thta: Input potential temperature of column (K) 64 | :param temp: Input temperature (K) at LCL 65 | :param qv: Input mixing ratio of column (kg kg-1) 66 | :return: Output equivalent potential temperature (K) 67 | """ 68 | 69 | thout = thta * np.exp((L * qv) / (Cp * temp)) 70 | return thout 71 | 72 | 73 | def temp_to_theta(temp, pres, p0=100000.): 74 | """ 75 | Compute potential temperature. 76 | 77 | :param temp: Input temperature (K) 78 | :param pres: Input pressure (Pa) 79 | :param p0: reference pressure (Pa) 80 | :return: potential temperature (K) 81 | """ 82 | 83 | return temp * (p0 / pres) ** 0.286 84 | 85 | 86 | def theta_to_temp(theta, pres, p0=100000.): 87 | """ 88 | Compute temperature. 89 | 90 | :param theta: Input potential temperature (K) 91 | :param pres: Input pressure (Pa) 92 | :param p0: reference pressure (Pa) 93 | :return: Output temperature (K) 94 | """ 95 | 96 | return theta * (pres / p0) ** 0.286 97 | 98 | 99 | def td_to_mixrat(tdew, pres): 100 | """ 101 | Convert from dew point temperature to water vapor mixing ratio. 102 | 103 | :param tdew: Input dew point temperature (K) 104 | :param pres: Input pressure (Pa) 105 | :return: Output water vapor mixing ratio (kg/kg) 106 | """ 107 | 108 | pres = pres / 100 109 | mixrat = eo / (pres * RvRd) * np.exp((L / Rv) * ((1 / Tfrez) - (1 / tdew))) 110 | return mixrat 111 | 112 | 113 | def mixrat_to_td(qvap, pres): 114 | """ 115 | Convert from water vapor mixing ratio to dewpoint temperature. 116 | 117 | :param qvap: Input water vapor mixing ratio (kg/kg) 118 | :param pres: Input pressure (Pa) 119 | :return: Output dewpoint temperature (K) 120 | """ 121 | 122 | pres = pres / 100. 123 | evap = qvap * pres * RvRd 124 | tdew = 1 / ((1 / Tfrez) - (Rv / L) * np.log(evap / eo)) 125 | return tdew 126 | 127 | 128 | def spechum_to_td(spechum, pres): 129 | """ 130 | Convert from specific humidity to dewpoint temperature 131 | 132 | :param spechum: Input specific humidity in (kg/kg) 133 | :param pres: Input pressure (Pa) 134 | :return: Output dewpoint temperature (K) 135 | """ 136 | 137 | qvap = (spechum / (1 - spechum)) 138 | pres = pres / 100 139 | evap = qvap * pres * RvRd 140 | tdew = 1 / ((1 / Tfrez) - (Rv / L) * np.log(evap / eo)) 141 | return tdew 142 | 143 | 144 | def claus_clap(temp): 145 | """ 146 | Compute saturation vapor pressure 147 | 148 | :param temp: Input temperature (K) 149 | :return: Output satuation vapor pressure (Pa) 150 | """ 151 | 152 | esat = (eo * np.exp((L / Rv) * (1.0 / Tfrez - 1 / temp))) * 100. 153 | return esat 154 | 155 | 156 | def claus_clap_ice(temp): 157 | """ 158 | Compute saturation vapor pressure over ice 159 | 160 | :param temp: Input temperature (K) 161 | :return: Output satuation vapor pressure of ice (Pa) 162 | """ 163 | 164 | a = 273.16 / temp 165 | exponent = -9.09718 * (a - 1.) - 3.56654 * np.log10(a) + \ 166 | 0.876793 * (1. - 1. / a) + np.log10(6.1071) 167 | esi = 10 ** exponent 168 | esi = esi * 100 169 | return esi 170 | 171 | 172 | def sat_vap(temp): 173 | """ 174 | Compute saturation vapor pressure 175 | 176 | :param temp: Input temperature (K) 177 | :return: Output satuation vapor pressure (Pa) 178 | """ 179 | 180 | [iinds] = np.where(temp < 273.15) 181 | [linds] = np.where(temp >= 273.15) 182 | esat = np.zeros_like(temp).astype('f') 183 | 184 | nice = len(iinds) 185 | nliq = len(linds) 186 | 187 | tempc = temp - 273.15 188 | if nliq > 1: 189 | esat[linds] = 6.112 * np.exp(17.67 * tempc[linds] / ( 190 | tempc[linds] + 243.12)) * 100. 191 | else: 192 | if nliq > 0: 193 | esat = 6.112 * np.exp(17.67 * tempc / (tempc + 243.12)) * 100. 194 | if nice > 1: 195 | esat[iinds] = 6.112 * np.exp(22.46 * tempc[iinds] / ( 196 | tempc[iinds] + 272.62)) * 100. 197 | else: 198 | if nice > 0: 199 | esat = 6.112 * np.exp(22.46 * tempc / (tempc + 272.62)) * 100. 200 | return esat 201 | 202 | 203 | def moist_lapse(ws, temp): 204 | """ 205 | Compute moist adiabatic lapse rate 206 | 207 | :param ws: Input saturation mixing ratio (kg kg-1) 208 | :param temp: Input air temperature (K) 209 | :return: Output moist adiabatic lapse rate 210 | """ 211 | 212 | return (g / Cp) * ((1.0 + L * ws) / (Rd * temp)) / ( 213 | 1.0 + (ws * (L ** 2.0) / (Cp * Rv * temp ** 2.0))) 214 | 215 | 216 | def satur_mix_ratio(es, pres): 217 | """ 218 | Compute saturation mixing ratio 219 | 220 | :param es: Input saturation vapor pressure (Pa) 221 | :param pres: Input air pressure (Pa) 222 | :return: Output saturation mixing ratio 223 | """ 224 | 225 | ws = 0.622 * (es / (pres - es)) 226 | return ws 227 | 228 | 229 | def virtual_temp_from_mixr(tempk, mixr): 230 | """ 231 | Virtual Temperature 232 | 233 | :param tempk: Temperature (K) 234 | :param mixr: Mixing Ratio (kg/kg) 235 | :return: Virtual temperature (K) 236 | """ 237 | 238 | return tempk * (1.0 + 0.6 * mixr) 239 | 240 | 241 | def latentc(tempk): 242 | """ 243 | Latent heat of condensation (vapourisation) 244 | http://en.wikipedia.org/wiki/Latent_heat#Latent_heat_for_condensation_of_water 245 | 246 | :param tempk: Temperature (K) 247 | :return: L_w (J/kg) 248 | """ 249 | 250 | tempc = tempk - 273.15 251 | return 1000 * ( 252 | 2500.8 - 2.36 * tempc + 0.0016 * tempc ** 2 - 253 | 0.00006 * tempc ** 3) 254 | 255 | 256 | def gamma_w(tempk, pres, e=None): 257 | """ 258 | Function to calculate the moist adiabatic lapse rate (deg K/Pa) based 259 | on the temperature, pressure, and rh of the environment. 260 | 261 | :param tempk: Temperature (K) 262 | :param pres: Input pressure (Pa) 263 | :param e: Input saturation vapor pressure (Pa) 264 | :return: The moist adiabatic lapse rate (Dec K/Pa) 265 | """ 266 | 267 | es = sat_vap(tempk) 268 | ws = satur_mix_ratio(es, pres) 269 | 270 | if e is None: 271 | # assume saturated 272 | e = es 273 | 274 | w = satur_mix_ratio(e, pres) 275 | 276 | tempv = virtual_temp_from_mixr(tempk, w) 277 | latent = latentc(tempk) 278 | 279 | A = 1.0 + latent * ws / (Rd * tempk) 280 | B = 1.0 + epsil * latent * latent * ws / (Cp * Rd * tempk * tempk) 281 | Rho = pres / (Rd * tempv) 282 | gamma = (A / B) / (Cp * Rho) 283 | return gamma 284 | 285 | 286 | def dry_parcel_ascent(startpp, starttk, starttdewk, nsteps=101): 287 | """ 288 | Lift a parcel dry adiabatically from startp to LCL. 289 | 290 | :param startpp: Pressure of parcel to lift in Pa 291 | :param starttk: Temperature of parcel at startp in K 292 | :param starttdewk: Dewpoint temperature of parcel at startp in K 293 | :param nsteps: 294 | :return: presdry, tempdry, pressure (Pa) and temperature (K) along 295 | dry adiabatic ascent of parcel 296 | tempiso is in K 297 | T_lcl, P_lcl, Temperature and pressure at LCL 298 | """ 299 | 300 | assert starttdewk <= starttk 301 | 302 | startt = starttk - 273.15 303 | starttdew = starttdewk - 273.15 304 | startp = startpp / 100. 305 | 306 | if starttdew == startt: 307 | return np.array([startp]), np.array([startt]), np.array([starttdew]), 308 | 309 | Pres = np.logspace(np.log10(startp), np.log10(600), nsteps) 310 | 311 | # Lift the dry parcel 312 | T_dry = (starttk * (Pres / startp) ** (Rd / Cp)) - 273.15 313 | 314 | # Mixing ratio isopleth 315 | starte = sat_vap(starttdewk) 316 | startw = satur_mix_ratio(starte, startpp) 317 | ee = Pres * startw / (.622 + startw) 318 | T_iso = 243.5 / (17.67 / np.log(ee / 6.112) - 1.0) 319 | 320 | # Solve for the intersection of these lines (LCL). 321 | # interp requires the x argument (argument 2) 322 | # to be ascending in order! 323 | P_lcl = np.interp(0, T_iso - T_dry, Pres) 324 | T_lcl = np.interp(P_lcl, Pres[::-1], T_dry[::-1]) 325 | 326 | presdry = np.linspace(startp, P_lcl) 327 | tempdry = np.interp(presdry, Pres[::-1], T_dry[::-1]) 328 | tempiso = np.interp(presdry, Pres[::-1], T_iso[::-1]) 329 | 330 | return ( 331 | presdry * 100., tempdry + 273.15, 332 | tempiso + 273.15, T_lcl + 273.15, P_lcl * 100.) 333 | 334 | 335 | def moist_ascent(startpp, starttk, ptop=100, nsteps=501): 336 | """ 337 | Lift a parcel moist adiabatically from startp to endp. 338 | 339 | :param startpp: Pressure of parcel to lift in Pa 340 | :param starttk: Temperature of parcel at startp in K 341 | :param ptop: Top pressure of parcel to lift in Pa 342 | :param nsteps: 343 | :return: 344 | """ 345 | 346 | startp = startpp / 100. # convert to hPa 347 | startt = starttk - 273.15 # convert to deg C 348 | 349 | preswet = np.logspace(np.log10(startp), np.log10(ptop), nsteps) 350 | 351 | temp = startt 352 | tempwet = np.zeros(preswet.shape) 353 | tempwet[0] = startt 354 | for ii in range(preswet.shape[0] - 1): 355 | delp = preswet[ii] - preswet[ii + 1] 356 | temp = temp - 100. * delp * gamma_w( 357 | temp + 273.15, (preswet[ii] - delp / 2) * 100.) 358 | tempwet[ii + 1] = temp 359 | 360 | return preswet * 100., tempwet + 273.15 361 | 362 | 363 | def stability(temp, lev, zdim, punit=100.): 364 | """ 365 | P level coordinates stability (Brunt-Vaisala). 366 | 367 | :param temp: array_like, temperature. 368 | :param lev: array_like, pressure level. 369 | :param zdim: vertical dimension axis. 370 | :param punit: pressure unit. 371 | :return: ndarray. 372 | """ 373 | 374 | temp = np.asarray(temp) 375 | ndim = temp.ndim 376 | p = arr.expand(lev, ndim, axis=zdim) * punit 377 | theta = pottemp(temp, lev, zdim, punit=punit) 378 | alpha = Rd * temp / p 379 | N = -alpha * grid.dvardp(np.log(theta), lev, zdim, punit=punit) 380 | 381 | return N 382 | 383 | 384 | def atmosphere(alt): 385 | """python-standard-atmosphere 386 | Python package for creating pressure and temperature profiles of 387 | the standard atmosphere for use with geophysical models. This 388 | package will only calcualate good values up to 86km. 389 | https://github.com/pcase13/python-standard-atmosphere/blob/master/standard.py 390 | Arguments: 391 | alt {scalar} -- altitude, hPa 392 | 393 | Returns: 394 | scalar -- standard-atmosphere 395 | """ 396 | 397 | # Constants 398 | REARTH = 6369.0 # radius of earth 399 | GMR = 34.163195 # hydrostatic constant 400 | NTAB = 8 # number of entries in defining tables 401 | 402 | # Define defining tables 403 | htab = [0.0, 11.0, 20.0, 32.0, 47.0, 51.0, 71.0, 84.852] 404 | ttab = [288.15, 216.65, 216.65, 228.65, 270.65, 270.65, 214.65, 186.946] 405 | ptab = [ 406 | 1.0, 2.233611E-1, 5.403295E-2, 8.5666784E-3, 1.0945601E-3, 407 | 6.6063531E-4, 3.9046834E-5, 3.68501E-6] 408 | gtab = [-6.5, 0.0, 1.0, 2.8, 0.0, -2.8, -2.0, 0.0] 409 | 410 | # Calculate 411 | h = alt*REARTH/(alt+REARTH) # convert to geopotential alt 412 | i = 1 413 | j = NTAB 414 | 415 | while(j > i+1): 416 | k = int((i+j)/2) # integer division 417 | if(h < htab[k]): 418 | j = k 419 | else: 420 | i = k 421 | print(i) 422 | tgrad = gtab[i] 423 | tbase = ttab[i] 424 | deltah = h-htab[i] 425 | tlocal = tbase + tgrad * deltah 426 | theta = tlocal/ttab[0] 427 | 428 | if(tgrad == 0.0): 429 | delta = ptab[i] * np.exp(-1*GMR*deltah/tbase) 430 | else: 431 | delta = ptab[i] * (tbase/tlocal)**(GMR/tgrad) 432 | 433 | sigma = delta/theta 434 | return sigma, delta, theta 435 | 436 | 437 | def get_standard_atmosphere_1d(z): 438 | NZ = z.shape[0] 439 | p0 = 1.013250e5 440 | t0 = 288.15 441 | p = np.zeros(z.shape) 442 | t = np.zeros(z.shape) 443 | 444 | for i in np.arange(NZ): 445 | sigma, delta, theta = atmosphere(z[i]/1000.) # convert to km 446 | p[i] = p0 * delta 447 | t[i] = t0 * theta 448 | return p, t 449 | 450 | 451 | def get_standard_atmosphere_2d(z): 452 | NZ = z.shape[1] 453 | NY = z.shape[0] 454 | p0 = 1.013250e5 455 | t0 = 288.15 456 | p = np.zeros(z.shape) 457 | t = np.zeros(z.shape) 458 | 459 | for i in np.arange(NY): 460 | for j in np.arange(NZ): 461 | sigma, delta, theta = atmosphere(z[i, j]/1000.) # convert to km 462 | p[i, j] = p0 * delta 463 | t[i, j] = t0 * theta 464 | return p, t 465 | 466 | 467 | def get_standard_atmosphere_3d(z): 468 | NZ = z.shape[2] 469 | NX = z.shape[0] 470 | NY = z.shape[1] 471 | p0 = 1.013250e5 472 | t0 = 288.15 473 | p = np.zeros(z.shape) 474 | t = np.zeros(z.shape) 475 | 476 | for i in np.arange(NX): 477 | for j in np.arange(NY): 478 | for k in np.arange(NZ): 479 | # convert to km 480 | sigma, delta, theta = atmosphere(z[i, j, k]/1000.) 481 | p[i, j, k] = p0 * delta 482 | t[i, j, k] = t0 * theta 483 | return p, t 484 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | from os import path 4 | from setuptools import find_packages, setup 5 | from codecs import open 6 | 7 | 8 | name = "nmc_met_diagnostic" 9 | author = __import__(name).__author__ 10 | version = __import__(name).__version__ 11 | 12 | here = path.abspath(path.dirname(__file__)) 13 | 14 | # Get the long description from the README file 15 | with open(path.join(here, 'README.md'), encoding='utf-8') as f: 16 | long_description = f.read() 17 | 18 | setup( 19 | name=name, 20 | 21 | version=version, 22 | 23 | description=("A collection of meteorological" 24 | "diagnostic and analysis functions."), 25 | long_description=long_description, 26 | 27 | # author 28 | author=author, 29 | author_email='kan.dai@foxmail.com', 30 | 31 | # LICENSE 32 | license='GPL3', 33 | 34 | classifiers=[ 35 | 'Development Status :: 3 - Alpha', 36 | 'Intended Audience :: Developers', 37 | 'Programming Language :: Python :: 3', 38 | ], 39 | 40 | packages=find_packages(exclude=['docs', 'tests', 'build', 'dist']), 41 | include_package_data=True, 42 | exclude_package_data={'': ['.gitignore', '*.pyc', '*.pyo']}, 43 | 44 | install_requires=['numpy>=1.12.1', 45 | 'scipy>=0.19.0', 46 | 'nmc_met_base'], 47 | dependency_links=[ 48 | 'git+https://github.com/nmcdev/nmc_met_base.git@master#egg=nmc_met_base', 49 | ] 50 | ) 51 | 52 | # development mode (DOS command): 53 | # python setup.py develop 54 | # python setup.py develop --uninstall 55 | 56 | # build mode： 57 | # python setup.py build --build-base=D:/test/python/build 58 | 59 | # distribution mode: 60 | # python setup.py sdist # create source tar.gz file in /dist 61 | # python setup.py bdist_wheel # create wheel binary in /dist 62 | -------------------------------------------------------------------------------- /tests/cyclone_test.py: -------------------------------------------------------------------------------- 1 | # _*_ coding: utf-8 _*_ 2 | 3 | """ 4 | Cyclones identification test. 5 | """ 6 | 7 | import os 8 | import datetime as dt 9 | import numpy as np 10 | import matplotlib.pyplot as plt 11 | import cartopy.crs as ccrs 12 | from netCDF4 import Dataset 13 | from nmc_met_graphics.draw_synoptic_analysis import draw_850_wind 14 | from nmc_met_diagnostic.feature import cyclone_loc 15 | 16 | 17 | data_dir = "H:/case_20160719/data/raw/tigge/ecmf/nc" 18 | ana_time = dt.datetime(2016, 7, 20, 0) 19 | 20 | # read pressure level data 21 | filename = os.path.join(data_dir, "ecmf_fc_pl_" + ana_time.strftime('%Y%m%d%H') + ".nc") 22 | fio = Dataset(filename, mode='r') 23 | lon = fio.variables['longitude'][:] 24 | lat = fio.variables['latitude'][:] 25 | levs = fio.variables['level'][:] 26 | id_lev = np.where(levs == 850) 27 | u = np.squeeze((fio.variables['u'][:])[0, id_lev, :, :]) 28 | v = np.squeeze((fio.variables['v'][:])[0, id_lev, :, :]) 29 | fio.close() 30 | 31 | # read mean sea level pressure 32 | filename = os.path.join(data_dir, "ecmf_fc_sfc_" + ana_time.strftime('%Y%m%d%H') + ".nc") 33 | fio = Dataset(filename, mode='r') 34 | msl = np.squeeze((fio.variables['msl'][:])[0, :, :]) / 100. 35 | fio.close() 36 | 37 | # identify cyclone 38 | low_loc = cyclone_loc(msl, lon, lat, edge_distance=600e3, 39 | search_rad_max=300e3, search_rad_min=150e3, 40 | search_rad_ndiv=3, slp_diff_test=0.5, limit=[110, 125, 28, 42]) 41 | 42 | # set figure 43 | plotcrs = ccrs.PlateCarree(central_longitude=110.) 44 | fig = plt.figure(figsize=(6, 6.8)) 45 | ax = plt.axes(projection=plotcrs) 46 | right_title = "Analysis: {}".format(ana_time.strftime('%Y-%m-%d %H:00')) 47 | cf, bb = draw_850_wind(ax, lon, lat, u, v, mslp=[lon, lat, msl], 48 | map_extent=[102, 122, 23, 43], left_title="", right_title=right_title) 49 | bb.length = 0.4 50 | 51 | # add cyclone center 52 | if low_loc is not None: 53 | ax.annotate("{:6.1f}".format(low_loc[0, 2]), xy=(low_loc[0, 0]-0.5, low_loc[0, 1]-0.5), 54 | xycoords=ccrs.PlateCarree()._as_mpl_transform(ax), ha='right', va='top', 55 | bbox=dict(boxstyle='round,pad=0.5', fc='yellow')) 56 | ax.scatter(low_loc[0, 0], low_loc[0, 1], edgecolors="k", facecolors="white", 57 | linewidth=2, s=100, transform=ccrs.PlateCarree()) 58 | 59 | fig.subplots_adjust(bottom=0.15) 60 | cax = fig.add_axes([0.15, 0.1, 0.7, 0.02]) 61 | cb = plt.colorbar(cf, cax=cax, orientation='horizontal', extendrect=True) 62 | cb.set_label('850hPa wind speed [m/s]', size='large', fontsize=18) 63 | cb.ax.tick_params(labelsize=16) 64 | 65 | plt.show() 66 | 67 | --------------------------------------------------------------------------------