├── LICENSE ├── MEIC 污染源清单向WRF-Chem 模式网格插值分配工具操作手册.pdf ├── README.md ├── int_dis.py ├── meic2wrf_GUI.py ├── meic2wrf_noGUI.py ├── namelist.input └── wrfinput_d01 /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. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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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 | -------------------------------------------------------------------------------- /MEIC 污染源清单向WRF-Chem 模式网格插值分配工具操作手册.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jinfan0931/meic2wrf/edeaa7f75667bc22bdc0694e32cbcbb6d6972c2f/MEIC 污染源清单向WRF-Chem 模式网格插值分配工具操作手册.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # meic2wrf 2 | Interpolating & distributing MEIC 0.25*0.25 emission inventory onto WRF-Chem grids 3 | Origin: https://github.com/jinfan0931/meic2wrf 4 | 5 | # Additional function: 6 | * A no-GUI script for command line user 7 | * Add `ll_area_new` for more accurent meic grid area(using [geojson-area](https://github.com/scisco/area)) and `meic2wrf_interp` for linear interpolation instead of nearest interpolation, especially for wrf with a high spatial resolution 8 | * Example namelist and corresponding wrfinput file 9 | 10 | 11 | # update 20210324 12 | * Delete repeated codes in GUI version 13 | * Add support for new meic data(as meic file names have changed recently) -------------------------------------------------------------------------------- /int_dis.py: -------------------------------------------------------------------------------- 1 | ############################################### meic2wrf ######################################################## 2 | # Authors: Fan Jin; Zhou Yong-long; Zhang Lei; Xu Xuan-ye; Jiang Pei-ya; Li Zhuo # 3 | # Institution: Chengdu AeriDice Environment Technology Co., Ltd.; Chengdu University of Information & Technology; # 4 | # Chinese Academy of Meteorological Sciences # 5 | # E-mail: jin.fan@outlook.com # 6 | # Environment: Python 3.7.7; pynio 1.5.5 # 7 | ################################################################################################################### 8 | # June 8, 2020, added ll_area_new(more accurent meic grid area), meic2wrf_interp(linear interpolation instead of 9 | # nearest) (by Hao Lyu) 10 | 11 | ###################################### 12 | # 1. area of lat_lon grids # 13 | ###################################### 14 | import numpy as np 15 | 16 | def ll_area(lat,res): #input : lat : np.array((200, 320)) 17 | Re=6371.392 18 | X=Re*np.cos(lat*(np.pi/180))*(np.pi/180)*res #np.array((200, 320)) 19 | Y=Re*(np.pi/180)*res #float 20 | return X*Y 21 | 22 | def ll_area_new(lat,res): 23 | from area import area 24 | startlon=0 25 | return_area = np.zeros_like(lat) 26 | isize,jsize = return_area.shape 27 | for i in range(isize): 28 | for j in range(jsize): 29 | obj = {'type':'Polygon','coordinates':[[[startlon,lat[i,j]-0.125],[startlon,lat[i,j]+0.125],[startlon+0.25,lat[i,j]+0.125],[startlon+0.25,lat[i,j]-0.125]]]} 30 | return_area[i,j] = area(obj)/1000.0/1000.0 31 | return return_area 32 | ################################### 33 | # 2. 2D interpolation func. # 34 | # ----dx----|--cdx--- # 35 | # --------1---------- # 36 | ################################### 37 | 38 | def meic2wrf(lon_inp,lat_inp,lon,lat,emis,):#lon/lat_inp: model; lon/lat: meic; emis: meic emis 39 | 40 | #coordinations of meic grids origin 41 | ox=lat[0,0] 42 | oy=lon[0,0] 43 | 44 | def inp(ix, iy, dx, dy, cdx, cdy): #put small function under meic2wrf function, or variables in small functions are global. 45 | #area_ratio 46 | return emis[ix,iy]*cdx*cdy+emis[ix,iy+1]*cdx*dy+emis[ix+1,iy+1]*dx*dy+emis[ix+1,iy]*dx*cdy 47 | 48 | def std_p(p,o): #standardize point p 49 | p = (p-o)*4 50 | dp = p - int(p) 51 | cdp = 1 - dp 52 | ip = int(p) #get index of the nearest big grid relates to the p point 53 | return dp, cdp, ip 54 | 55 | def inp_p(px, py): 56 | dx, cdx, ix = std_p(px,ox) 57 | dy, cdy, iy = std_p(py,oy) 58 | return inp(ix, iy, dx, dy, cdx, cdy) 59 | 60 | emis_inp=np.zeros(lon_inp.shape, dtype='float32') 61 | y_cnt =0 62 | for (row_lat, row_lon) in zip(lat_inp, lon_inp): #2D meic coordinates to 1D 63 | x_cnt=0 64 | for (pnt_lat, pnt_lon) in zip(row_lat, row_lon): #1D to point 65 | emis_inp[y_cnt,x_cnt] = inp_p(pnt_lat, pnt_lon) #assign meic emission 66 | x_cnt += 1 67 | y_cnt +=1 68 | return emis_inp 69 | 70 | def meic2wrf_interp(lon_inp,lat_inp,lon,lat,emis,interp_method = 'bilinear'):#lon/lat_inp: model; lon/lat: meic; emis: meic emis 71 | import xesmf as xe 72 | grid_out = {'lon': lon_inp,'lat': lat_inp} 73 | grid_in = {'lon': lon,'lat': lat} 74 | regridder = xe.Regridder(grid_in, grid_out, interp_method,reuse_weights=True) 75 | emis_inp = regridder(emis) 76 | return emis_inp 77 | ########################################### 78 | # 3. vertical & time distribution func. # 79 | ########################################### 80 | 81 | def sec2zt(sec,zfac,tfac): 82 | c=[sec*i*j for i in tfac for j in zfac] 83 | d=[np.array(c[i:i+len(zfac)]) for i in np.arange(0,len(c),len(zfac))] 84 | return np.array(d) 85 | 86 | ################################### -------------------------------------------------------------------------------- /meic2wrf_GUI.py: -------------------------------------------------------------------------------- 1 | ############################################### meic2wrf ######################################################## 2 | # Authors: Fan Jin; Zhou Yong-long; Zhang Lei; Xu Xuan-ye; Jiang Pei-ya; Li Zhuo # 3 | # Institution: Chengdu AeriDice Environment Technology Co., Ltd.; Chengdu University of Information & Technology; # 4 | # Chinese Academy of Meteorological Sciences # 5 | # E-mail: jin.fan@outlook.com # 6 | # Environment: Python 3.7.7; pynio 1.5.5 # 7 | ################################################################################################################### 8 | 9 | import fnmatch 10 | import glob 11 | import os 12 | import shutil 13 | import tkinter as tk 14 | from tkinter import filedialog, messagebox 15 | 16 | import Nio 17 | import numpy as np 18 | 19 | from int_dis import * 20 | 21 | root=tk.Tk() 22 | root.title('MEIC污染源清单向WRF-Chem模式网格插值分配程序') 23 | 24 | lbl=tk.Label(root, text='1. 整合MEIC清单中各污染物不同部门的排放数据') 25 | lbl.grid(row=0,column=0,sticky=tk.W) 26 | 27 | lbl=tk.Label(root, text='MEIC清单数据所在文件夹路径:') 28 | lbl.grid(row=1,column=0,sticky=tk.E) 29 | 30 | ent_dir = tk.Entry(root, width=65) 31 | ent_dir.grid(row=1,column=1) 32 | 33 | def bws_meic(): 34 | dirname = filedialog.askdirectory() 35 | ent_dir.delete(0,tk.END) 36 | ent_dir.insert(0, dirname) 37 | 38 | def merge_meic_dept(): 39 | if os.path.exists(ent_dir.get()+'/merged/'): 40 | shutil.rmtree(ent_dir.get()+'/merged/') 41 | os.makedirs(ent_dir.get()+'/merged/') 42 | print("++++++ent_dir++++++",ent_dir.get()) 43 | for i,j in zip(['*BC*','*CO[!2]*','*CO2*','*NH3*','*NOx*','*[!V]OC*','*PM2.5*','*PMcoarse*','*ALD*',\ 44 | '*CSL*','*ETH*','*GLY*','*HC3*','*HC5*','*HC8*','*HCHO*',\ 45 | '*ISO*','*KET*','*MACR*','*MGLY*','*MVK*','*NR*','*NVOL*',\ 46 | '*OL2*','*OLI*','*OLT*','*ORA1*','*ORA2*','*TOL*','*XYL*','*SO2*','*VOC*',], ['BC','CO','CO2','NH3','NOx','OC','PM2.5','PMcoarse','ALD',\ 47 | 'CSL','ETH','GLY','HC3','HC5','HC8','HCHO',\ 48 | 'ISO','KET','MACR','MGLY','MVK','NR','NVOL',\ 49 | 'OL2','OLI','OLT','ORA1','ORA2','TOL','XYL','SO2','VOC',]): 50 | 51 | # new:2016_1_agriculture_BC.nc 52 | # old:2016_01__agriculture__BC.nc 53 | 54 | try: 55 | fn_act = glob.glob(ent_dir.get()+'/*_agr*_' +j+".nc" )[0] 56 | fn_idt = glob.glob(ent_dir.get()+'/*_ind*_' +j+".nc" )[0] 57 | fn_pwr = glob.glob(ent_dir.get()+'/*_pow*_' +j+".nc" )[0] 58 | fn_rdt = glob.glob(ent_dir.get()+'/*_res*_' +j+".nc" )[0] 59 | fn_tpt = glob.glob(ent_dir.get()+'/*_tra*_' +j+".nc" )[0] 60 | except: 61 | fn_act = glob.glob(ent_dir.get()+"/*_agr*_PM25.nc" )[0] # 新旧文件一个是pm2.5一个是pm25 62 | fn_idt = glob.glob(ent_dir.get()+"/*_ind*_PM25.nc" )[0] 63 | fn_pwr = glob.glob(ent_dir.get()+"/*_pow*_PM25.nc" )[0] 64 | fn_rdt = glob.glob(ent_dir.get()+"/*_res*_PM25.nc" )[0] 65 | fn_tpt = glob.glob(ent_dir.get()+"/*_tra*_PM25.nc" )[0] 66 | 67 | 68 | f_act=Nio.open_file(fn_act) 69 | f_idt=Nio.open_file(fn_idt) 70 | f_pwr=Nio.open_file(fn_pwr) 71 | f_rdt=Nio.open_file(fn_rdt) 72 | f_tpt=Nio.open_file(fn_tpt) 73 | 74 | act=f_act.variables['z'][:].reshape((200, 320),)[::-1] 75 | act=np.where(act>0.0,act*1,0.0) 76 | idt=f_idt.variables['z'][:].reshape((200, 320),)[::-1] 77 | idt=np.where(idt>0.0,idt*1,0.0) 78 | pwr=f_pwr.variables['z'][:].reshape((200, 320),)[::-1] 79 | pwr=np.where(pwr>0.0,pwr*1,0.0) 80 | rdt=f_rdt.variables['z'][:].reshape((200, 320),)[::-1] 81 | rdt=np.where(rdt>0.0,rdt*1,0.0) 82 | tpt=f_tpt.variables['z'][:].reshape((200, 320),)[::-1] 83 | tpt=np.where(tpt>0.0,tpt*1,0.0) 84 | 85 | lon=np.arange(70.125,150,0.25,dtype=np.float32) 86 | lat=np.arange(10.125,60,0.25,dtype=np.float32) 87 | lon,lat=np.meshgrid(lon,lat) 88 | 89 | f=Nio.open_file(ent_dir.get()+'/merged/'+j+'.nc','c') 90 | f.create_dimension('lon',320) 91 | f.create_dimension('lat',200) 92 | 93 | for var,val in zip(['act','idt','pwr','rdt','tpt','lon','lat'],[act,idt,pwr,rdt,tpt,lon,lat]): 94 | f.create_variable(var,'f',('lat','lon',)) 95 | f.variables[var][:] = val 96 | 97 | f.close() 98 | 99 | 100 | b_btn = tk.Button(root, text='浏览', command=bws_meic, width=15) 101 | b_btn.grid(row=1,column=2) 102 | 103 | r_btn = tk.Button(root, text='运行', command=merge_meic_dept, width=15) 104 | r_btn.grid(row=1,column=3) 105 | 106 | 107 | lbl=tk.Label(root, text='2. 向WRF-Chem模式网格插值分配') 108 | lbl.grid(row=2,column=0,sticky=tk.W) 109 | 110 | lbl=tk.Label(root, text='wrfinput文件路径:') 111 | lbl.grid(row=3,column=0,sticky=tk.E) 112 | 113 | ent_inp = tk.Entry(root, width=65) 114 | ent_inp.grid(row=3,column=1) 115 | 116 | def bws_wrfipt(): 117 | wrfiptname = filedialog.askopenfilename() 118 | ent_inp.delete(0,tk.END) 119 | ent_inp.insert(0, wrfiptname) 120 | 121 | b_btn = tk.Button(root, text='浏览', command=bws_wrfipt, width=15) 122 | b_btn.grid(row=3,column=2)#padx=10,pady=10 123 | 124 | agr_z_d=[1.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000] 125 | ind_z_d=[0.602,0.346,0.052,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000] 126 | pow_z_d=[0.034,0.140,0.349,0.227,0.167,0.059,0.024,0.000,0.000,0.000,0.000] 127 | res_z_d=[0.900,0.100,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000] 128 | tra_z_d=[0.950,0.050,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000,0.000] 129 | 130 | sec_z_d=[agr_z_d,ind_z_d,pow_z_d,res_z_d,tra_z_d,] 131 | 132 | agr_t_d=[1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 133 | 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000] 134 | ind_t_d=[1.080, 1.632, 1.632, 1.632, 1.632, 1.632, 1.632, 1.632, 1.632, 1.080, 0.936, 0.792, 0.648, 0.504, 135 | 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.648, 0.792, 0.936] 136 | pow_t_d=[1.104, 1.200, 1.212, 1.200, 1.224, 1.236, 1.236, 1.224, 1.176, 1.104, 1.116, 1.128, 1.080, 1.008, 137 | 0.948, 0.720, 0.840, 0.708, 0.576, 0.600, 0.732, 0.804, 0.852, 0.972] 138 | res_t_d=[1.224, 1.584, 2.736, 2.520, 0.984, 0.696, 0.552, 0.984, 1.680, 2.328, 1.488, 0.840, 0.264, 0.360, 139 | 0.336, 0.240, 0.336, 0.312, 0.408, 0.216, 0.408, 1.080, 1.176, 1.248] 140 | tra_t_d=[1.536, 1.440, 1.392, 1.272, 1.212, 1.368, 1.380, 1.416, 1.428, 1.524, 1.380, 1.128, 1.068, 1.008, 141 | 0.864, 0.648, 0.480, 0.348, 0.252, 0.216, 0.192, 0.312, 0.720, 1.416] 142 | 143 | sec_t_d=[agr_t_d,ind_t_d,pow_t_d,res_t_d,tra_t_d,] 144 | 145 | state=tk.StringVar() 146 | state.set('disable') 147 | 148 | def disable(): 149 | ent_agr_z.config(state=state.get()) 150 | ent_ind_z.config(state=state.get()) 151 | ent_pow_z.config(state=state.get()) 152 | ent_res_z.config(state=state.get()) 153 | ent_tra_z.config(state=state.get()) 154 | ent_agr_t.config(state=state.get()) 155 | ent_ind_t.config(state=state.get()) 156 | ent_pow_t.config(state=state.get()) 157 | ent_res_t.config(state=state.get()) 158 | ent_tra_t.config(state=state.get()) 159 | 160 | def activate(): 161 | ent_agr_z.config(state=state.get()) 162 | ent_ind_z.config(state=state.get()) 163 | ent_pow_z.config(state=state.get()) 164 | ent_res_z.config(state=state.get()) 165 | ent_tra_z.config(state=state.get()) 166 | ent_agr_t.config(state=state.get()) 167 | ent_ind_t.config(state=state.get()) 168 | ent_pow_t.config(state=state.get()) 169 | ent_res_t.config(state=state.get()) 170 | ent_tra_t.config(state=state.get()) 171 | 172 | rdb_default = tk.Radiobutton(root, text='使用默认分配因子', variable=state, value='disable', command=disable) 173 | rdb_default.grid(row=4,column=1,sticky=tk.W) 174 | rdb_custom = tk.Radiobutton(root, text='自定义分配因子', variable=state, value='normal', command=activate) 175 | rdb_custom.grid(row=4,column=1,sticky=tk.E) 176 | 177 | lbl=tk.Label(root, text='农业垂直排放因子:') 178 | lbl.grid(row=5,column=0,sticky=tk.E) 179 | 180 | ent_agr_z=tk.Entry(root, state='disable',width=65) 181 | ent_agr_z.grid(row=5,column=1,) 182 | 183 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 184 | lbl.grid(row=5,column=2) 185 | 186 | lbl=tk.Label(root, text='工业垂直排放因子:') 187 | lbl.grid(row=6,column=0,sticky=tk.E) 188 | 189 | ent_ind_z=tk.Entry(root, state='disable',width=65) 190 | ent_ind_z.grid(row=6,column=1,) 191 | 192 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 193 | lbl.grid(row=6,column=2) 194 | 195 | lbl=tk.Label(root, text='电力垂直排放因子:') 196 | lbl.grid(row=7,column=0,sticky=tk.E) 197 | 198 | ent_pow_z=tk.Entry(root, state='disable',width=65) 199 | ent_pow_z.grid(row=7,column=1,) 200 | 201 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 202 | lbl.grid(row=7,column=2) 203 | 204 | lbl=tk.Label(root, text='民用垂直排放因子:') 205 | lbl.grid(row=8,column=0,sticky=tk.E) 206 | 207 | ent_res_z=tk.Entry(root, state='disable',width=65) 208 | ent_res_z.grid(row=8,column=1,) 209 | 210 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 211 | lbl.grid(row=8,column=2) 212 | 213 | lbl=tk.Label(root, text='交通垂直排放因子:') 214 | lbl.grid(row=9,column=0,sticky=tk.E) 215 | 216 | ent_tra_z=tk.Entry(root, state='disable',width=65) 217 | ent_tra_z.grid(row=9,column=1,) 218 | 219 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 220 | lbl.grid(row=9,column=2) 221 | 222 | lbl=tk.Label(root, text='农业逐小时排放因子:') 223 | lbl.grid(row=10,column=0,sticky=tk.E) 224 | 225 | ent_agr_t=tk.Entry(root, state='disable',width=65) 226 | ent_agr_t.grid(row=10,column=1,) 227 | 228 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 229 | lbl.grid(row=10,column=2) 230 | 231 | lbl=tk.Label(root, text='工业逐小时排放因子:') 232 | lbl.grid(row=11,column=0,sticky=tk.E) 233 | 234 | ent_ind_t=tk.Entry(root, state='disable',width=65) 235 | ent_ind_t.grid(row=11,column=1,) 236 | 237 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 238 | lbl.grid(row=11,column=2) 239 | 240 | lbl=tk.Label(root, text='电力逐小时排放因子:') 241 | lbl.grid(row=12,column=0,sticky=tk.E) 242 | 243 | ent_pow_t=tk.Entry(root, state='disable',width=65) 244 | ent_pow_t.grid(row=12,column=1) 245 | 246 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 247 | lbl.grid(row=12,column=2) 248 | 249 | lbl=tk.Label(root, text='民用逐小时排放因子:') 250 | lbl.grid(row=13,column=0,sticky=tk.E) 251 | 252 | ent_res_t=tk.Entry(root, state='disable',width=65) 253 | ent_res_t.grid(row=13,column=1) 254 | 255 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 256 | lbl.grid(row=13,column=2) 257 | 258 | lbl=tk.Label(root, text='交通逐小时排放因子:') 259 | lbl.grid(row=14,column=0,sticky=tk.E) 260 | 261 | ent_tra_t=tk.Entry(root, state='disable',width=65) 262 | ent_tra_t.grid(row=14,column=1) 263 | 264 | lbl=tk.Label(root, text='输入格式: 0.34 0.56 ... ') 265 | lbl.grid(row=14,column=2) 266 | 267 | def itp_dis(): 268 | if state.get() == 'normal': 269 | if fnmatch.fnmatch(ent_agr_z.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_ind_z.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_pow_z.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_res_z.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_tra_z.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_agr_t.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_ind_t.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_pow_t.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_res_t.get().replace(' ', '').replace('.',''), '*[!0-9]*') or fnmatch.fnmatch(ent_tra_t.get().replace(' ', '').replace('.',''), '*[!0-9]*'): 270 | tk.messagebox.showerror(title='error',message='input format must be 0.23 0.34 ...') 271 | elif not len(ent_agr_z.get().strip().split(' '))==len(ent_ind_z.get().strip().split(' '))==len(ent_pow_z.get().strip().split(' '))==len(ent_res_z.get().strip().split(' '))==len(ent_tra_z.get().strip().split(' ')): 272 | tk.messagebox.showerror(title='error',message='number of z factor must be equal') 273 | elif not len(ent_agr_t.get().strip().split(' '))==len(ent_ind_t.get().strip().split(' '))==len(ent_pow_t.get().strip().split(' '))==len(ent_res_t.get().strip().split(' '))==len(ent_tra_t.get().strip().split(' '))==24: 274 | tk.messagebox.showerror(title='error',message='number of t factor must be 24') 275 | else: 276 | agr_z=[float(i) for i in ent_agr_z.get().strip().split(' ')] 277 | ind_z=[float(i) for i in ent_ind_z.get().strip().split(' ')] 278 | pow_z=[float(i) for i in ent_pow_z.get().strip().split(' ')] 279 | res_z=[float(i) for i in ent_res_z.get().strip().split(' ')] 280 | tra_z=[float(i) for i in ent_tra_z.get().strip().split(' ')] 281 | sec_z=[agr_z,ind_z,pow_z,res_z,tra_z,] 282 | agr_t=[float(i) for i in ent_agr_t.get().strip().split(' ')] 283 | ind_t=[float(i) for i in ent_ind_t.get().strip().split(' ')] 284 | pow_t=[float(i) for i in ent_pow_t.get().strip().split(' ')] 285 | res_t=[float(i) for i in ent_res_t.get().strip().split(' ')] 286 | tra_t=[float(i) for i in ent_tra_t.get().strip().split(' ')] 287 | sec_t=[agr_t,ind_t,pow_t,res_t,tra_t,] 288 | 289 | f_inp=Nio.open_file(ent_inp.get(),format='nc') 290 | lon_inp=f_inp.variables['XLONG'][0,:] 291 | lat_inp=f_inp.variables['XLAT'][0,:] 292 | time_inp=f_inp.variables['Times'][:][0] 293 | time_inp=''.join([i.decode('utf-8') for i in time_inp]).split('_')[0] 294 | f_inp.close() 295 | 296 | #put all the distributed meic species into meic_spec_emis: 297 | 298 | meic_spec_emis=[] 299 | #inorganic gas: ton/(grid.month) to mole/(km2.h) 300 | for spec,M in zip(['CO','CO2','NH3','NOx','SO2',],[28,44,17,46,64]): 301 | f_post=Nio.open_file(ent_dir.get()+'/merged/'+spec+'.nc') 302 | lon=f_post.variables['lon'][:] 303 | lat=f_post.variables['lat'][:] 304 | section=[(f_post.variables[sec][:,:]*1e6)/(ll_area(lat,0.25)*30*24*M) for sec in ['act','idt','pwr','rdt','tpt',]] 305 | f_post.close() 306 | sections=[meic2wrf(lon_inp,lat_inp,lon,lat,emis,) for emis in section] 307 | c=[sec2zt(i,j,k) for i,j,k in zip(sections,sec_z,sec_t)] 308 | c=sum(c) 309 | meic_spec_emis.append(c) 310 | #organic gas: million_mole/(grid.month) to mole/(km2.h) 311 | for spec in ['ALD','CSL','ETH','GLY','HC3','HC5','HC8','HCHO','ISO','KET','MACR','MGLY','MVK','NR','NVOL', 312 | 'OL2','OLI','OLT','ORA1','ORA2','TOL','XYL',]: 313 | f_post=Nio.open_file(ent_dir.get()+'/merged/'+spec+'.nc') 314 | #lon=f_post.variables['lon'][:] 315 | #lat=f_post.variables['lat'][:] 316 | section=[(f_post.variables[sec][:,:]*1e6)/(ll_area(lat,0.25)*30*24) for sec in ['act','idt','pwr','rdt','tpt',]] 317 | f_post.close() 318 | sections=[meic2wrf(lon_inp,lat_inp,lon,lat,emis,) for emis in section] 319 | c=[sec2zt(i,j,k) for i,j,k in zip(sections,sec_z,sec_t)] 320 | c=sum(c) 321 | meic_spec_emis.append(c) 322 | #aerosol: ton/(grid.month) to ug/(m2.s) 323 | for spec in ['BC','OC','PM2.5','PMcoarse',]: 324 | f_post=Nio.open_file(ent_dir.get()+'/merged/'+spec+'.nc') 325 | #lon=f_post.variables['lon'][:] 326 | #lat=f_post.variables['lat'][:] 327 | section=[(f_post.variables[sec][:,:]*1e6)/(ll_area(lat,0.25)*30*24*3600) for sec in ['act','idt','pwr','rdt','tpt',]] 328 | f_post.close() 329 | sections=[meic2wrf(lon_inp,lat_inp,lon,lat,emis,) for emis in section] 330 | c=[sec2zt(i,j,k) for i,j,k in zip(sections,sec_z,sec_t)] 331 | c=sum(c) 332 | meic_spec_emis.append(c) 333 | 334 | #meic emission to RADM2 chemistry scheme: 335 | 336 | wrf_spec_emis=[np.zeros(meic_spec_emis[0][:].shape, dtype='float32')]*31 337 | 338 | wrf_spec_emis[0]=meic_spec_emis[0] #wrf: CO 339 | wrf_spec_emis[1]=meic_spec_emis[2] #wrf: NH3 340 | wrf_spec_emis[2]=meic_spec_emis[3]*0.9 #wrf: NO 341 | wrf_spec_emis[3]=meic_spec_emis[3]*0.1 #wrf: NO2 342 | wrf_spec_emis[4]=meic_spec_emis[4]*0.9 #wrf: SO2 343 | wrf_spec_emis[5]=meic_spec_emis[5] #wrf: ALD 344 | wrf_spec_emis[6]=meic_spec_emis[6] #wrf: CSL 345 | wrf_spec_emis[7]=meic_spec_emis[7] #wrf: ETH 346 | wrf_spec_emis[8]=meic_spec_emis[9] #wrf: HC3 347 | wrf_spec_emis[9]=meic_spec_emis[10] #wrf: HC5 348 | wrf_spec_emis[10]=meic_spec_emis[11] #wrf: HC8 349 | wrf_spec_emis[11]=meic_spec_emis[12] #wrf: HCHO 350 | wrf_spec_emis[12]=meic_spec_emis[13] #wrf: ISO 351 | wrf_spec_emis[13]=meic_spec_emis[14] #wrf: KET 352 | wrf_spec_emis[14]=meic_spec_emis[20]*1.1 #wrf: OL2 353 | wrf_spec_emis[15]=meic_spec_emis[21]*1.1 #wrf: OLI 354 | wrf_spec_emis[16]=meic_spec_emis[22]*1.1 #wrf: OLT 355 | wrf_spec_emis[17]=meic_spec_emis[24] #wrf: ORA2 356 | wrf_spec_emis[18]=meic_spec_emis[25]*1.1 #wrf: TOL 357 | wrf_spec_emis[19]=meic_spec_emis[26]*1.1 #wrf: XYL 358 | wrf_spec_emis[20]=meic_spec_emis[27]*0.2 #wrf: ECi 359 | wrf_spec_emis[21]=meic_spec_emis[27]*0.8 #wrf: ECj 360 | wrf_spec_emis[22]=meic_spec_emis[28]*0.2 #wrf: ORGi 361 | wrf_spec_emis[23]=meic_spec_emis[28]*0.8 #wrf: ORGj 362 | wrf_spec_emis[24]=meic_spec_emis[29]-meic_spec_emis[28]-meic_spec_emis[27]*0.2 #wrf: PM25i 363 | wrf_spec_emis[25]=meic_spec_emis[29]-meic_spec_emis[28]-meic_spec_emis[27]*0.8 #wrf: PM25j 364 | wrf_spec_emis[26]=meic_spec_emis[30]*0.8 #wrf: PM10 365 | wrf_spec_emis[27]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: SO4i 366 | wrf_spec_emis[28]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: SO4j 367 | wrf_spec_emis[29]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: NO3i 368 | wrf_spec_emis[30]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: NO3j 369 | 370 | #generate wrfchemi_00z_d01 anthropogenic emission data for wrf-chem model run: 371 | if os.path.exists(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]): 372 | os.remove(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]) 373 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1],'c',format='nc') 374 | 375 | f_chem.create_dimension('Time',None) 376 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 377 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 378 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 379 | f_chem.create_dimension('DateStrLen',19) 380 | 381 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 382 | for i,time in enumerate([time_inp+'_00:00:00',time_inp+'_01:00:00',time_inp+'_02:00:00',time_inp+'_03:00:00',time_inp+'_04:00:00', 383 | time_inp+'_05:00:00',time_inp+'_06:00:00',time_inp+'_07:00:00',time_inp+'_08:00:00',time_inp+'_09:00:00',time_inp+'_10:00:00', 384 | time_inp+'_11:00:00',]): 385 | f_chem.variables['Times'][i]=list(time) #split the string to char 386 | 387 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 388 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 389 | f_chem.variables[LL][:]=ll 390 | 391 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 392 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 393 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 394 | 395 | for gas in radm_gas: 396 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 397 | f_chem.variables[gas].FieldType = np.int16(104) 398 | f_chem.variables[gas].MemoryOrder = 'XYZ' 399 | f_chem.variables[gas].description = 'EMISSIONS' 400 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 401 | f_chem.variables[gas].stagger = 'Z' 402 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 403 | for aerosol in radm_aerosol: 404 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 405 | f_chem.variables[aerosol].FieldType = np.int16(104) 406 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 407 | f_chem.variables[aerosol].description = 'EMISSIONS' 408 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 409 | f_chem.variables[aerosol].stagger = 'Z' 410 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 411 | 412 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 413 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 414 | 'E_SO4J','E_NO3I','E_NO3J',] 415 | 416 | for i,spec in enumerate(radm_spec): 417 | f_chem.variables[spec][:] = wrf_spec_emis[i][0:12,:,:,:] #dimension need to be matched with the variable defination 418 | 419 | f_chem.close() 420 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]) 421 | else: 422 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1],'c',format='nc') 423 | f_chem.create_dimension('Time',None) 424 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 425 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 426 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 427 | f_chem.create_dimension('DateStrLen',19) 428 | 429 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 430 | for i,time in enumerate([time_inp+'_00:00:00',time_inp+'_01:00:00',time_inp+'_02:00:00',time_inp+'_03:00:00',time_inp+'_04:00:00', 431 | time_inp+'_05:00:00',time_inp+'_06:00:00',time_inp+'_07:00:00',time_inp+'_08:00:00',time_inp+'_09:00:00',time_inp+'_10:00:00', 432 | time_inp+'_11:00:00',]): 433 | f_chem.variables['Times'][i]=list(time) #split the string to char 434 | 435 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 436 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 437 | f_chem.variables[LL][:]=ll 438 | 439 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 440 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 441 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 442 | 443 | for gas in radm_gas: 444 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 445 | f_chem.variables[gas].FieldType = np.int16(104) 446 | f_chem.variables[gas].MemoryOrder = 'XYZ' 447 | f_chem.variables[gas].description = 'EMISSIONS' 448 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 449 | f_chem.variables[gas].stagger = 'Z' 450 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 451 | for aerosol in radm_aerosol: 452 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 453 | f_chem.variables[aerosol].FieldType = np.int16(104) 454 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 455 | f_chem.variables[aerosol].description = 'EMISSIONS' 456 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 457 | f_chem.variables[aerosol].stagger = 'Z' 458 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 459 | 460 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 461 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 462 | 'E_SO4J','E_NO3I','E_NO3J',] 463 | 464 | for i,spec in enumerate(radm_spec): 465 | f_chem.variables[spec][:] = wrf_spec_emis[i][0:12,:,:,:] #dimension need to be matched with the variable defination 466 | 467 | f_chem.close() 468 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]) 469 | 470 | #generate wrfchemi_12z_d01 anthropogenic emission data for wrf-chem model run: 471 | if os.path.exists(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]): 472 | os.remove(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]) 473 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1],'c',format='nc') 474 | 475 | f_chem.create_dimension('Time',None) 476 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 477 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 478 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 479 | f_chem.create_dimension('DateStrLen',19) 480 | 481 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 482 | for i,time in enumerate([time_inp+'_12:00:00',time_inp+'_13:00:00',time_inp+'_14:00:00',time_inp+'_15:00:00',time_inp+'_16:00:00', 483 | time_inp+'_17:00:00',time_inp+'_18:00:00',time_inp+'_19:00:00',time_inp+'_20:00:00',time_inp+'_21:00:00',time_inp+'_22:00:00', 484 | time_inp+'_23:00:00',]): 485 | f_chem.variables['Times'][i]=list(time) 486 | 487 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 488 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 489 | f_chem.variables[LL][:]=ll 490 | 491 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 492 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 493 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 494 | 495 | for gas in radm_gas: 496 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 497 | f_chem.variables[gas].FieldType = np.int16(104) 498 | f_chem.variables[gas].MemoryOrder = 'XYZ' 499 | f_chem.variables[gas].description = 'EMISSIONS' 500 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 501 | f_chem.variables[gas].stagger = 'Z' 502 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 503 | for aerosol in radm_aerosol: 504 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 505 | f_chem.variables[aerosol].FieldType = np.int16(104) 506 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 507 | f_chem.variables[aerosol].description = 'EMISSIONS' 508 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 509 | f_chem.variables[aerosol].stagger = 'Z' 510 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 511 | 512 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 513 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 514 | 'E_SO4J','E_NO3I','E_NO3J',] 515 | 516 | for i,spec in enumerate(radm_spec): 517 | f_chem.variables[spec][:] = wrf_spec_emis[i][12:24,:,:,:] #dimension need to be matched with the variable defination 518 | 519 | f_chem.close() 520 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]) 521 | else: 522 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1],'c',format='nc') 523 | f_chem.create_dimension('Time',None) 524 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 525 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 526 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 527 | f_chem.create_dimension('DateStrLen',19) 528 | 529 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 530 | for i,time in enumerate([time_inp+'_12:00:00',time_inp+'_13:00:00',time_inp+'_14:00:00',time_inp+'_15:00:00',time_inp+'_16:00:00', 531 | time_inp+'_17:00:00',time_inp+'_18:00:00',time_inp+'_19:00:00',time_inp+'_20:00:00',time_inp+'_21:00:00',time_inp+'_22:00:00', 532 | time_inp+'_23:00:00',]): 533 | f_chem.variables['Times'][i]=list(time) 534 | 535 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 536 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 537 | f_chem.variables[LL][:]=ll 538 | 539 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 540 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 541 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 542 | 543 | for gas in radm_gas: 544 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 545 | f_chem.variables[gas].FieldType = np.int16(104) 546 | f_chem.variables[gas].MemoryOrder = 'XYZ' 547 | f_chem.variables[gas].description = 'EMISSIONS' 548 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 549 | f_chem.variables[gas].stagger = 'Z' 550 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 551 | for aerosol in radm_aerosol: 552 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 553 | f_chem.variables[aerosol].FieldType = np.int16(104) 554 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 555 | f_chem.variables[aerosol].description = 'EMISSIONS' 556 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 557 | f_chem.variables[aerosol].stagger = 'Z' 558 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 559 | 560 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 561 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 562 | 'E_SO4J','E_NO3I','E_NO3J',] 563 | 564 | for i,spec in enumerate(radm_spec): 565 | f_chem.variables[spec][:] = wrf_spec_emis[i][12:24,:,:,:] #dimension need to be matched with the variable defination 566 | 567 | f_chem.close() 568 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]) 569 | 570 | elif state.get() =='disable': 571 | f_inp=Nio.open_file(ent_inp.get(),format='nc') 572 | lon_inp=f_inp.variables['XLONG'][0,:] 573 | lat_inp=f_inp.variables['XLAT'][0,:] 574 | time_inp=f_inp.variables['Times'][:][0] 575 | time_inp=''.join([i.decode('utf-8') for i in time_inp]).split('_')[0] 576 | f_inp.close() 577 | 578 | #put all the distributed meic species into meic_spec_emis: 579 | meic_spec_emis=[] 580 | #inorganic gas: ton/(grid.month) to mole/(km2.h) 581 | for spec,M in zip(['CO','CO2','NH3','NOx','SO2',],[28,44,17,46,64]): 582 | f_post=Nio.open_file(ent_dir.get()+'/merged/'+spec+'.nc') 583 | lon=f_post.variables['lon'][:] 584 | lat=f_post.variables['lat'][:] 585 | section=[(f_post.variables[sec][:,:]*1e6)/(ll_area(lat,0.25)*30*24*M) for sec in ['act','idt','pwr','rdt','tpt',]] 586 | f_post.close() 587 | sections=[meic2wrf(lon_inp,lat_inp,lon,lat,emis,) for emis in section] 588 | c=[sec2zt(i,j,k) for i,j,k in zip(sections,sec_z_d,sec_t_d)] 589 | c=sum(c) 590 | meic_spec_emis.append(c) 591 | #organic gas: million_mole/(grid.month) to mole/(km2.h) 592 | for spec in ['ALD','CSL','ETH','GLY','HC3','HC5','HC8','HCHO','ISO','KET','MACR','MGLY','MVK','NR','NVOL', 593 | 'OL2','OLI','OLT','ORA1','ORA2','TOL','XYL',]: 594 | f_post=Nio.open_file(ent_dir.get()+'/merged/'+spec+'.nc') 595 | #lon=f_post.variables['lon'][:] 596 | #lat=f_post.variables['lat'][:] 597 | section=[(f_post.variables[sec][:,:]*1e6)/(ll_area(lat,0.25)*30*24) for sec in ['act','idt','pwr','rdt','tpt',]] 598 | f_post.close() 599 | sections=[meic2wrf(lon_inp,lat_inp,lon,lat,emis,) for emis in section] 600 | c=[sec2zt(i,j,k) for i,j,k in zip(sections,sec_z_d,sec_t_d)] 601 | c=sum(c) 602 | meic_spec_emis.append(c) 603 | #aerosol: ton/(grid.month) to ug/(m2.s) 604 | for spec in ['BC','OC','PM2.5','PMcoarse',]: 605 | f_post=Nio.open_file(ent_dir.get()+'/merged/'+spec+'.nc') 606 | #lon=f_post.variables['lon'][:] 607 | #lat=f_post.variables['lat'][:] 608 | section=[(f_post.variables[sec][:,:]*1e6)/(ll_area(lat,0.25)*30*24*3600) for sec in ['act','idt','pwr','rdt','tpt',]] 609 | f_post.close() 610 | sections=[meic2wrf(lon_inp,lat_inp,lon,lat,emis,) for emis in section] 611 | c=[sec2zt(i,j,k) for i,j,k in zip(sections,sec_z_d,sec_t_d)] 612 | c=sum(c) 613 | meic_spec_emis.append(c) 614 | 615 | #meic emission to RADM2 chemistry scheme: 616 | 617 | wrf_spec_emis=[np.zeros(meic_spec_emis[0][:].shape, dtype='float32')]*31 618 | 619 | wrf_spec_emis[0]=meic_spec_emis[0] #wrf: CO 620 | wrf_spec_emis[1]=meic_spec_emis[2] #wrf: NH3 621 | wrf_spec_emis[2]=meic_spec_emis[3]*0.9 #wrf: NO 622 | wrf_spec_emis[3]=meic_spec_emis[3]*0.1 #wrf: NO2 623 | wrf_spec_emis[4]=meic_spec_emis[4]*0.9 #wrf: SO2 624 | wrf_spec_emis[5]=meic_spec_emis[5] #wrf: ALD 625 | wrf_spec_emis[6]=meic_spec_emis[6] #wrf: CSL 626 | wrf_spec_emis[7]=meic_spec_emis[7] #wrf: ETH 627 | wrf_spec_emis[8]=meic_spec_emis[9] #wrf: HC3 628 | wrf_spec_emis[9]=meic_spec_emis[10] #wrf: HC5 629 | wrf_spec_emis[10]=meic_spec_emis[11] #wrf: HC8 630 | wrf_spec_emis[11]=meic_spec_emis[12] #wrf: HCHO 631 | wrf_spec_emis[12]=meic_spec_emis[13] #wrf: ISO 632 | wrf_spec_emis[13]=meic_spec_emis[14] #wrf: KET 633 | wrf_spec_emis[14]=meic_spec_emis[20]*1.1 #wrf: OL2 634 | wrf_spec_emis[15]=meic_spec_emis[21]*1.1 #wrf: OLI 635 | wrf_spec_emis[16]=meic_spec_emis[22]*1.1 #wrf: OLT 636 | wrf_spec_emis[17]=meic_spec_emis[24] #wrf: ORA2 637 | wrf_spec_emis[18]=meic_spec_emis[25]*1.1 #wrf: TOL 638 | wrf_spec_emis[19]=meic_spec_emis[26]*1.1 #wrf: XYL 639 | wrf_spec_emis[20]=meic_spec_emis[27]*0.2 #wrf: ECi 640 | wrf_spec_emis[21]=meic_spec_emis[27]*0.8 #wrf: ECj 641 | wrf_spec_emis[22]=meic_spec_emis[28]*0.2 #wrf: ORGi 642 | wrf_spec_emis[23]=meic_spec_emis[28]*0.8 #wrf: ORGj 643 | wrf_spec_emis[24]=meic_spec_emis[29]-meic_spec_emis[28]-meic_spec_emis[27]*0.2 #wrf: PM25i 644 | wrf_spec_emis[25]=meic_spec_emis[29]-meic_spec_emis[28]-meic_spec_emis[27]*0.8 #wrf: PM25j 645 | wrf_spec_emis[26]=meic_spec_emis[30]*0.8 #wrf: PM10 646 | wrf_spec_emis[27]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: SO4i 647 | wrf_spec_emis[28]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: SO4j 648 | wrf_spec_emis[29]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: NO3i 649 | wrf_spec_emis[30]=np.zeros(meic_spec_emis[0][:].shape, dtype='float32') #wrf: NO3j 650 | 651 | #generate wrfchemi_00z_d01 anthropogenic emission data for wrf-chem model run: 652 | if os.path.exists(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]): 653 | os.remove(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]) 654 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1],'c',format='nc') 655 | 656 | f_chem.create_dimension('Time',None) 657 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 658 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 659 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 660 | f_chem.create_dimension('DateStrLen',19) 661 | 662 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 663 | for i,time in enumerate([time_inp+'_00:00:00',time_inp+'_01:00:00',time_inp+'_02:00:00',time_inp+'_03:00:00',time_inp+'_04:00:00', 664 | time_inp+'_05:00:00',time_inp+'_06:00:00',time_inp+'_07:00:00',time_inp+'_08:00:00',time_inp+'_09:00:00',time_inp+'_10:00:00', 665 | time_inp+'_11:00:00',]): 666 | f_chem.variables['Times'][i]=list(time) #split the string to char 667 | 668 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 669 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 670 | f_chem.variables[LL][:]=ll 671 | 672 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 673 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 674 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 675 | 676 | for gas in radm_gas: 677 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 678 | f_chem.variables[gas].FieldType = np.int16(104) 679 | f_chem.variables[gas].MemoryOrder = 'XYZ' 680 | f_chem.variables[gas].description = 'EMISSIONS' 681 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 682 | f_chem.variables[gas].stagger = 'Z' 683 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 684 | for aerosol in radm_aerosol: 685 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 686 | f_chem.variables[aerosol].FieldType = np.int16(104) 687 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 688 | f_chem.variables[aerosol].description = 'EMISSIONS' 689 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 690 | f_chem.variables[aerosol].stagger = 'Z' 691 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 692 | 693 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 694 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 695 | 'E_SO4J','E_NO3I','E_NO3J',] 696 | 697 | for i,spec in enumerate(radm_spec): 698 | f_chem.variables[spec][:] = wrf_spec_emis[i][0:12,:,:,:] #dimension need to be matched with the variable defination 699 | 700 | f_chem.close() 701 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]) 702 | else: 703 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1],'c',format='nc') 704 | f_chem.create_dimension('Time',None) 705 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 706 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 707 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 708 | f_chem.create_dimension('DateStrLen',19) 709 | 710 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 711 | for i,time in enumerate([time_inp+'_00:00:00',time_inp+'_01:00:00',time_inp+'_02:00:00',time_inp+'_03:00:00',time_inp+'_04:00:00', 712 | time_inp+'_05:00:00',time_inp+'_06:00:00',time_inp+'_07:00:00',time_inp+'_08:00:00',time_inp+'_09:00:00',time_inp+'_10:00:00', 713 | time_inp+'_11:00:00',]): 714 | f_chem.variables['Times'][i]=list(time) #split the string to char 715 | 716 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 717 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 718 | f_chem.variables[LL][:]=ll 719 | 720 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 721 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 722 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 723 | 724 | for gas in radm_gas: 725 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 726 | f_chem.variables[gas].FieldType = np.int16(104) 727 | f_chem.variables[gas].MemoryOrder = 'XYZ' 728 | f_chem.variables[gas].description = 'EMISSIONS' 729 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 730 | f_chem.variables[gas].stagger = 'Z' 731 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 732 | for aerosol in radm_aerosol: 733 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 734 | f_chem.variables[aerosol].FieldType = np.int16(104) 735 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 736 | f_chem.variables[aerosol].description = 'EMISSIONS' 737 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 738 | f_chem.variables[aerosol].stagger = 'Z' 739 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 740 | 741 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 742 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 743 | 'E_SO4J','E_NO3I','E_NO3J',] 744 | 745 | for i,spec in enumerate(radm_spec): 746 | f_chem.variables[spec][:] = wrf_spec_emis[i][0:12,:,:,:] #dimension need to be matched with the variable defination 747 | 748 | f_chem.close() 749 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_00z_'+ent_inp.get().split('_')[-1]) 750 | 751 | #generate wrfchemi_12z_d01 anthropogenic emission data for wrf-chem model run: 752 | if os.path.exists(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]): 753 | os.remove(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]) 754 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1],'c',format='nc') 755 | f_chem.create_dimension('Time',None) 756 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 757 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 758 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 759 | f_chem.create_dimension('DateStrLen',19) 760 | 761 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 762 | for i,time in enumerate([time_inp+'_12:00:00',time_inp+'_13:00:00',time_inp+'_14:00:00',time_inp+'_15:00:00',time_inp+'_16:00:00', 763 | time_inp+'_17:00:00',time_inp+'_18:00:00',time_inp+'_19:00:00',time_inp+'_20:00:00',time_inp+'_21:00:00',time_inp+'_22:00:00', 764 | time_inp+'_23:00:00',]): 765 | f_chem.variables['Times'][i]=list(time) 766 | 767 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 768 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 769 | f_chem.variables[LL][:]=ll 770 | 771 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 772 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 773 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 774 | 775 | for gas in radm_gas: 776 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 777 | f_chem.variables[gas].FieldType = np.int16(104) 778 | f_chem.variables[gas].MemoryOrder = 'XYZ' 779 | f_chem.variables[gas].description = 'EMISSIONS' 780 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 781 | f_chem.variables[gas].stagger = 'Z' 782 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 783 | for aerosol in radm_aerosol: 784 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 785 | f_chem.variables[aerosol].FieldType = np.int16(104) 786 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 787 | f_chem.variables[aerosol].description = 'EMISSIONS' 788 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 789 | f_chem.variables[aerosol].stagger = 'Z' 790 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 791 | 792 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 793 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 794 | 'E_SO4J','E_NO3I','E_NO3J',] 795 | 796 | for i,spec in enumerate(radm_spec): 797 | f_chem.variables[spec][:] = wrf_spec_emis[i][12:24,:,:,:] #dimension need to be matched with the variable defination 798 | 799 | f_chem.close() 800 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]) 801 | else: 802 | f_chem=Nio.open_file(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1],'c',format='nc') 803 | 804 | f_chem.create_dimension('Time',None) 805 | f_chem.create_dimension('emissions_zdim',wrf_spec_emis[0].shape[1]) 806 | f_chem.create_dimension('south_north',wrf_spec_emis[0].shape[2]) 807 | f_chem.create_dimension('west_east',wrf_spec_emis[0].shape[3]) 808 | f_chem.create_dimension('DateStrLen',19) 809 | 810 | f_chem.create_variable('Times','S1',('Time','DateStrLen'),) 811 | for i,time in enumerate([time_inp+'_12:00:00',time_inp+'_13:00:00',time_inp+'_14:00:00',time_inp+'_15:00:00',time_inp+'_16:00:00', 812 | time_inp+'_17:00:00',time_inp+'_18:00:00',time_inp+'_19:00:00',time_inp+'_20:00:00',time_inp+'_21:00:00',time_inp+'_22:00:00', 813 | time_inp+'_23:00:00',]): 814 | f_chem.variables['Times'][i]=list(time) 815 | 816 | for ll, LL in zip([lon_inp, lat_inp],['XLONG', 'XLAT']): 817 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 818 | f_chem.variables[LL][:]=ll 819 | 820 | radm_gas=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 821 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL',] 822 | radm_aerosol=['E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I','E_SO4J','E_NO3I','E_NO3J',] 823 | 824 | for gas in radm_gas: 825 | f_chem.create_variable(gas,'f',('Time','emissions_zdim','south_north','west_east',)) 826 | f_chem.variables[gas].FieldType = np.int16(104) 827 | f_chem.variables[gas].MemoryOrder = 'XYZ' 828 | f_chem.variables[gas].description = 'EMISSIONS' 829 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 830 | f_chem.variables[gas].stagger = 'Z' 831 | #f_chem.variables[gas].ordinates = 'XLONG XLAT' 832 | for aerosol in radm_aerosol: 833 | f_chem.create_variable(aerosol,'f',('Time','emissions_zdim','south_north','west_east',)) 834 | f_chem.variables[aerosol].FieldType = np.int16(104) 835 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 836 | f_chem.variables[aerosol].description = 'EMISSIONS' 837 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 838 | f_chem.variables[aerosol].stagger = 'Z' 839 | #f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 840 | 841 | radm_spec=['E_CO','E_NH3','E_NO','E_NO2','E_SO2','E_ALD','E_CSL','E_ETH','E_HC3','E_HC5','E_HC8','E_HCHO','E_ISO','E_KET', 842 | 'E_OL2','E_OLI','E_OLT','E_ORA2','E_TOL','E_XYL','E_ECI','E_ECJ','E_ORGI','E_ORGJ','E_PM25I','E_PM25J','E_PM_10','E_SO4I', 843 | 'E_SO4J','E_NO3I','E_NO3J',] 844 | 845 | for i,spec in enumerate(radm_spec): 846 | f_chem.variables[spec][:] = wrf_spec_emis[i][12:24,:,:,:] #dimension need to be matched with the variable defination 847 | 848 | f_chem.close() 849 | os.rename(ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]+'.nc', ent_dir.get()+'/merged/'+'wrfchemi_12z_'+ent_inp.get().split('_')[-1]) 850 | 851 | btn = tk.Button(root, text='运行', command=itp_dis, width=15) 852 | btn.grid(row=15, column=3) 853 | 854 | tk.mainloop() 855 | -------------------------------------------------------------------------------- /meic2wrf_noGUI.py: -------------------------------------------------------------------------------- 1 | ############################################### meic2wrf ######################################################## 2 | # Authors: Fan Jin; Zhou Yong-long; Zhang Lei; Xu Xuan-ye; Jiang Pei-ya; Li Zhuo # 3 | # Institution: Chengdu AeriDice Environment Technology Co., Ltd.; Chengdu University of Information & Technology; # 4 | # Chinese Academy of Meteorological Sciences # 5 | # E-mail: jin.fan@outlook.com # 6 | # Environment: Python 3.7.7; pynio 1.5.5 # 7 | ################################################################################################################### 8 | # June 8, 2020, added noGUI script (by Hao Lyu) 9 | # Environment added: area(https://github.com/scisco/area) 10 | 11 | import fnmatch 12 | import glob 13 | import os 14 | import shutil 15 | 16 | import Nio 17 | import numpy as np 18 | 19 | from int_dis import * 20 | 21 | meic2wrf = meic2wrf#_interp #用线性插值替代最邻近插值 22 | ll_area = ll_area #更高精度计算每块meic网格面积 23 | # 排放源高度分布 24 | agr_z_d = [1.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000] 25 | ind_z_d = [0.602, 0.346, 0.052, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000] 26 | pow_z_d = [0.034, 0.140, 0.349, 0.227, 0.167, 0.059, 0.024, 0.000, 0.000, 0.000, 0.000] 27 | res_z_d = [0.900, 0.100, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000] 28 | tra_z_d = [0.950, 0.050, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000] 29 | sec_z_d = [agr_z_d, ind_z_d, pow_z_d, res_z_d, tra_z_d, ] 30 | # 排放源时间分布 31 | agr_t_d=[1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 32 | 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000] 33 | ind_t_d=[1.080, 1.632, 1.632, 1.632, 1.632, 1.632, 1.632, 1.632, 1.632, 1.080, 0.936, 0.792, 0.648, 0.504, 34 | 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.504, 0.648, 0.792, 0.936] 35 | pow_t_d=[1.104, 1.200, 1.212, 1.200, 1.224, 1.236, 1.236, 1.224, 1.176, 1.104, 1.116, 1.128, 1.080, 1.008, 36 | 0.948, 0.720, 0.840, 0.708, 0.576, 0.600, 0.732, 0.804, 0.852, 0.972] 37 | res_t_d=[1.224, 1.584, 2.736, 2.520, 0.984, 0.696, 0.552, 0.984, 1.680, 2.328, 1.488, 0.840, 0.264, 0.360, 38 | 0.336, 0.240, 0.336, 0.312, 0.408, 0.216, 0.408, 1.080, 1.176, 1.248] 39 | tra_t_d=[1.536, 1.440, 1.392, 1.272, 1.212, 1.368, 1.380, 1.416, 1.428, 1.524, 1.380, 1.128, 1.068, 1.008, 40 | 0.864, 0.648, 0.480, 0.348, 0.252, 0.216, 0.192, 0.312, 0.720, 1.416] 41 | sec_t_d = [agr_t_d, ind_t_d, pow_t_d, res_t_d, tra_t_d, ] 42 | 43 | def merge_meic_dept(ent_dir): # 生成merge文件夹，预处理排放源文件 44 | if os.path.exists(ent_dir+'/merged/'): 45 | shutil.rmtree(ent_dir+'/merged/') 46 | os.makedirs(ent_dir+'/merged/') 47 | for i, j in zip(['*BC*', '*CO[!2]*', '*CO2*', '*NH3*', '*NOx*', '*[!V]OC*', '*PM2.5*', '*PMcoarse*', '*ALD*', 48 | '*CSL*', '*ETH*', '*GLY*', '*HC3*', '*HC5*', '*HC8*', '*HCHO*', 49 | '*ISO*', '*KET*', '*MACR*', '*MGLY*', '*MVK*', '*NR*', '*NVOL*', 50 | '*OL2*', '*OLI*', '*OLT*', '*ORA1*', '*ORA2*', '*TOL*', '*XYL*', '*SO2*', '*VOC*', ], 51 | ['BC', 'CO', 'CO2', 'NH3', 'NOx', 'OC', 'PM2.5', 'PMcoarse', 'ALD', 52 | 'CSL', 'ETH', 'GLY', 'HC3', 'HC5', 'HC8', 'HCHO', 53 | 'ISO', 'KET', 'MACR', 'MGLY', 'MVK', 'NR','NVOL', 54 | 'OL2', 'OLI', 'OLT', 'ORA1', 'ORA2', 'TOL', 'XYL', 'SO2', 'VOC', ]): 55 | # new:2016_1_agriculture_BC.nc 56 | # old:2016_01__agriculture__BC.nc 57 | try: 58 | fn_act = glob.glob(ent_dir+'/*_agr*_' +j+".nc" )[0] 59 | fn_idt = glob.glob(ent_dir+'/*_ind*_' +j+".nc" )[0] 60 | fn_pwr = glob.glob(ent_dir+'/*_pow*_' +j+".nc" )[0] 61 | fn_rdt = glob.glob(ent_dir+'/*_res*_' +j+".nc" )[0] 62 | fn_tpt = glob.glob(ent_dir+'/*_tra*_' +j+".nc" )[0] 63 | except: 64 | fn_act = glob.glob(ent_dir+"/*_agr*_PM25.nc" )[0] # 新旧文件一个是pm2.5一个是pm25 65 | fn_idt = glob.glob(ent_dir+"/*_ind*_PM25.nc" )[0] 66 | fn_pwr = glob.glob(ent_dir+"/*_pow*_PM25.nc" )[0] 67 | fn_rdt = glob.glob(ent_dir+"/*_res*_PM25.nc" )[0] 68 | fn_tpt = glob.glob(ent_dir+"/*_tra*_PM25.nc" )[0] 69 | 70 | f_act = Nio.open_file(fn_act) 71 | f_idt = Nio.open_file(fn_idt) 72 | f_pwr = Nio.open_file(fn_pwr) 73 | f_rdt = Nio.open_file(fn_rdt) 74 | f_tpt = Nio.open_file(fn_tpt) 75 | 76 | act = f_act.variables['z'][:].reshape((200, 320),)[::-1] 77 | act = np.where(act > 0.0, act*1, 0.0) 78 | idt = f_idt.variables['z'][:].reshape((200, 320),)[::-1] 79 | idt = np.where(idt > 0.0, idt*1, 0.0) 80 | pwr = f_pwr.variables['z'][:].reshape((200, 320),)[::-1] 81 | pwr = np.where(pwr > 0.0, pwr*1, 0.0) 82 | rdt = f_rdt.variables['z'][:].reshape((200, 320),)[::-1] 83 | rdt = np.where(rdt > 0.0, rdt*1, 0.0) 84 | tpt = f_tpt.variables['z'][:].reshape((200, 320),)[::-1] 85 | tpt = np.where(tpt > 0.0, tpt*1, 0.0) 86 | 87 | lon = np.arange(70.125, 150, 0.25, dtype=np.float32) 88 | lat = np.arange(10.125, 60, 0.25, dtype=np.float32) 89 | lon, lat = np.meshgrid(lon, lat) 90 | 91 | f = Nio.open_file(ent_dir+'/merged/'+j+'.nc', 'c') 92 | f.create_dimension('lon', 320) 93 | f.create_dimension('lat', 200) 94 | for var, val in zip(['act', 'idt', 'pwr', 'rdt', 'tpt', 'lon', 'lat'], [act, idt, pwr, rdt, tpt, lon, lat]): 95 | f.create_variable(var, 'f', ('lat', 'lon',)) 96 | f.variables[var][:] = val 97 | f.close() 98 | 99 | def itp_dis(ent_inp,ent_dir,save_dir): 100 | f_inp = Nio.open_file(ent_inp, format='nc') 101 | lon_inp = f_inp.variables['XLONG'][0, :] 102 | lat_inp = f_inp.variables['XLAT'][0, :] 103 | time_inp = f_inp.variables['Times'][:][0] 104 | time_inp = ''.join([i.decode('utf-8') for i in time_inp]).split('_')[0] 105 | f_inp.close() 106 | # put all the distributed meic species into meic_spec_emis: 107 | meic_spec_emis = [] 108 | # inorganic gas: ton/(grid.month) to mole/(km2.h) 109 | for spec, M in zip(['CO', 'CO2', 'NH3', 'NOx', 'SO2', ], [28, 44, 17, 46, 64]): 110 | f_post = Nio.open_file(ent_dir+'/merged/'+spec+'.nc') 111 | lon = f_post.variables['lon'][:] 112 | lat = f_post.variables['lat'][:] 113 | section = [(f_post.variables[sec][:, :]*1e6)/(ll_area(lat, 0.25)*30*24*M) 114 | for sec in ['act', 'idt', 'pwr', 'rdt', 'tpt', ]] 115 | f_post.close() 116 | sections = [meic2wrf(lon_inp, lat_inp, lon, lat, emis,) 117 | for emis in section] 118 | c = [sec2zt(i, j, k) for i, j, k in zip(sections, sec_z_d, sec_t_d)] 119 | c = sum(c) 120 | meic_spec_emis.append(c) 121 | # organic gas: million_mole/(grid.month) to mole/(km2.h) 122 | for spec in ['ALD', 'CSL', 'ETH', 'GLY', 'HC3', 'HC5', 'HC8', 'HCHO', 'ISO', 'KET', 'MACR', 'MGLY', 'MVK', 'NR', 'NVOL', 123 | 'OL2', 'OLI', 'OLT', 'ORA1', 'ORA2', 'TOL', 'XYL', ]: 124 | f_post = Nio.open_file(ent_dir+'/merged/'+spec+'.nc') 125 | section = [(f_post.variables[sec][:, :]*1e6)/(ll_area(lat, 0.25)*30*24) 126 | for sec in ['act', 'idt', 'pwr', 'rdt', 'tpt', ]] 127 | f_post.close() 128 | sections = [meic2wrf(lon_inp, lat_inp, lon, lat, emis,) 129 | for emis in section] 130 | c = [sec2zt(i, j, k) for i, j, k in zip(sections, sec_z_d, sec_t_d)] 131 | c = sum(c) 132 | meic_spec_emis.append(c) 133 | # aerosol: ton/(grid.month) to ug/(m2.s) 134 | for spec in ['BC', 'OC', 'PM2.5', 'PMcoarse', ]: 135 | f_post = Nio.open_file(ent_dir+'/merged/'+spec+'.nc') 136 | # lon=f_post.variables['lon'][:] 137 | # lat=f_post.variables['lat'][:] 138 | section = [(f_post.variables[sec][:, :]*1e6)/(ll_area(lat, 0.25)*30*24*3600) 139 | for sec in ['act', 'idt', 'pwr', 'rdt', 'tpt', ]] 140 | f_post.close() 141 | sections = [meic2wrf(lon_inp, lat_inp, lon, lat, emis,) 142 | for emis in section] 143 | c = [sec2zt(i, j, k) for i, j, k in zip(sections, sec_z_d, sec_t_d)] 144 | c = sum(c) 145 | meic_spec_emis.append(c) 146 | 147 | # meic emission to RADM2 chemistry scheme: 148 | 149 | wrf_spec_emis = [ 150 | np.zeros(meic_spec_emis[0][:].shape, dtype='float32')]*31 151 | 152 | wrf_spec_emis[0] = meic_spec_emis[0] # wrf: CO 153 | wrf_spec_emis[1] = meic_spec_emis[2] # wrf: NH3 154 | wrf_spec_emis[2] = meic_spec_emis[3]*0.9 # wrf: NO 155 | wrf_spec_emis[3] = meic_spec_emis[3]*0.1 # wrf: NO2 156 | wrf_spec_emis[4] = meic_spec_emis[4]*0.9 # wrf: SO2 157 | wrf_spec_emis[5] = meic_spec_emis[5] # wrf: ALD 158 | wrf_spec_emis[6] = meic_spec_emis[6] # wrf: CSL 159 | wrf_spec_emis[7] = meic_spec_emis[7] # wrf: ETH 160 | wrf_spec_emis[8] = meic_spec_emis[9] # wrf: HC3 161 | wrf_spec_emis[9] = meic_spec_emis[10] # wrf: HC5 162 | wrf_spec_emis[10] = meic_spec_emis[11] # wrf: HC8 163 | wrf_spec_emis[11] = meic_spec_emis[12] # wrf: HCHO 164 | wrf_spec_emis[12] = meic_spec_emis[13] # wrf: ISO 165 | wrf_spec_emis[13] = meic_spec_emis[14] # wrf: KET 166 | wrf_spec_emis[14] = meic_spec_emis[20]*1.1 # wrf: OL2 167 | wrf_spec_emis[15] = meic_spec_emis[21]*1.1 # wrf: OLI 168 | wrf_spec_emis[16] = meic_spec_emis[22]*1.1 # wrf: OLT 169 | wrf_spec_emis[17] = meic_spec_emis[24] # wrf: ORA2 170 | wrf_spec_emis[18] = meic_spec_emis[25]*1.1 # wrf: TOL 171 | wrf_spec_emis[19] = meic_spec_emis[26]*1.1 # wrf: XYL 172 | wrf_spec_emis[20] = meic_spec_emis[27]*0.2 # wrf: ECi 173 | wrf_spec_emis[21] = meic_spec_emis[27]*0.8 # wrf: ECj 174 | wrf_spec_emis[22] = meic_spec_emis[28]*0.2 # wrf: ORGi 175 | wrf_spec_emis[23] = meic_spec_emis[28]*0.8 # wrf: ORGj 176 | wrf_spec_emis[24] = meic_spec_emis[29] - \ 177 | meic_spec_emis[28]-meic_spec_emis[27]*0.2 # wrf: PM25i 178 | wrf_spec_emis[25] = meic_spec_emis[29] - \ 179 | meic_spec_emis[28]-meic_spec_emis[27]*0.8 # wrf: PM25j 180 | wrf_spec_emis[26] = meic_spec_emis[30]*0.8 # wrf: PM10 181 | wrf_spec_emis[27] = np.zeros( 182 | meic_spec_emis[0][:].shape, dtype='float32') # wrf: SO4i 183 | wrf_spec_emis[28] = np.zeros( 184 | meic_spec_emis[0][:].shape, dtype='float32') # wrf: SO4j 185 | wrf_spec_emis[29] = np.zeros( 186 | meic_spec_emis[0][:].shape, dtype='float32') # wrf: NO3i 187 | wrf_spec_emis[30] = np.zeros( 188 | meic_spec_emis[0][:].shape, dtype='float32') # wrf: NO3j 189 | 190 | #生成00和12两个时次 191 | for ihour in [0,12]: 192 | # generate wrfchemi_00z_d01 anthropogenic emission data for wrf-chem model run: 193 | if os.path.exists(ent_dir+'/merged/'+'wrfchemi_'+str(ihour).zfill(2)+'z_'+ent_inp.split('_')[-1]): 194 | os.remove(ent_dir+'/merged/'+'wrfchemi_'+str(ihour).zfill(2)+'z_' + ent_inp.split('_')[-1]) 195 | f_chem = Nio.open_file(ent_dir+'/merged/'+'wrfchemi_'+str(ihour).zfill(2)+'z_'+ent_inp.split('_')[-1], 'c', format='nc') 196 | f_chem.create_dimension('Time', None) 197 | f_chem.create_dimension('emissions_zdim', wrf_spec_emis[0].shape[1]) 198 | f_chem.create_dimension('south_north', wrf_spec_emis[0].shape[2]) 199 | f_chem.create_dimension('west_east', wrf_spec_emis[0].shape[3]) 200 | f_chem.create_dimension('DateStrLen', 19) 201 | 202 | f_chem.create_variable('Times', 'S1', ('Time', 'DateStrLen'),) 203 | for i, time in enumerate([time_inp+'_'+str(ii).zfill(2)+':00:00' for ii in range(ihour,ihour+12)]): 204 | f_chem.variables['Times'][i] = list(time) # split the string to char 205 | for ll, LL in zip([lon_inp, lat_inp], ['XLONG', 'XLAT']): 206 | f_chem.create_variable(LL, 'f', ('south_north', 'west_east',),) 207 | f_chem.variables[LL][:] = ll 208 | 209 | radm_gas = ['E_CO', 'E_NH3', 'E_NO', 'E_NO2', 'E_SO2', 'E_ALD', 'E_CSL', 'E_ETH', 'E_HC3', 'E_HC5', 'E_HC8', 'E_HCHO', 'E_ISO', 'E_KET', 210 | 'E_OL2', 'E_OLI', 'E_OLT', 'E_ORA2', 'E_TOL', 'E_XYL', ] 211 | radm_aerosol = ['E_ECI', 'E_ECJ', 'E_ORGI', 'E_ORGJ', 'E_PM25I', 212 | 'E_PM25J', 'E_PM_10', 'E_SO4I', 'E_SO4J', 'E_NO3I', 'E_NO3J', ] 213 | 214 | for gas in radm_gas: 215 | f_chem.create_variable(gas, 'f', ('Time', 'emissions_zdim', 'south_north', 'west_east',)) 216 | f_chem.variables[gas].FieldType = np.int16(104) 217 | f_chem.variables[gas].MemoryOrder = 'XYZ' 218 | f_chem.variables[gas].description = 'EMISSIONS' 219 | f_chem.variables[gas].units = 'mol km^-2 hr^-1' 220 | f_chem.variables[gas].stagger = 'Z' 221 | # f_chem.variables[gas].ordinates = 'XLONG XLAT' 222 | for aerosol in radm_aerosol: 223 | f_chem.create_variable(aerosol, 'f', ('Time', 'emissions_zdim', 'south_north', 'west_east',)) 224 | f_chem.variables[aerosol].FieldType = np.int16(104) 225 | f_chem.variables[aerosol].MemoryOrder = 'XYZ' 226 | f_chem.variables[aerosol].description = 'EMISSIONS' 227 | f_chem.variables[aerosol].units = 'ug/m3 m/s' 228 | f_chem.variables[aerosol].stagger = 'Z' 229 | # f_chem.variables[aerosol].ordinates = 'XLONG XLAT' 230 | 231 | radm_spec = ['E_CO', 'E_NH3', 'E_NO', 'E_NO2', 'E_SO2', 'E_ALD', 'E_CSL', 'E_ETH', 'E_HC3', 'E_HC5', 'E_HC8', 'E_HCHO', 'E_ISO', 'E_KET', 232 | 'E_OL2', 'E_OLI', 'E_OLT', 'E_ORA2', 'E_TOL', 'E_XYL', 'E_ECI', 'E_ECJ', 'E_ORGI', 'E_ORGJ', 'E_PM25I', 'E_PM25J', 'E_PM_10', 'E_SO4I', 233 | 'E_SO4J', 'E_NO3I', 'E_NO3J', ] 234 | 235 | for i, spec in enumerate(radm_spec): 236 | # dimension need to be matched with the variable defination 237 | #f_chem.variables[spec][:] = wrf_spec_emis[i][ihour:ihour+12, :, :, :] 238 | chem_spec_input=wrf_spec_emis[i][ihour:ihour+12, :, :, :] 239 | chem_spec_input_new = chem_spec_input.astype(np.float32) 240 | f_chem.variables[spec].assign_value(chem_spec_input_new) 241 | f_chem.close() 242 | #rename,delete suffix .nc 243 | if not os.path.exists(save_dir): os.makedirs(save_dir) 244 | shutil.move(ent_dir+'/merged/'+'wrfchemi_'+str(ihour).zfill(2)+'z_'+ent_inp.split('_')[-1]+'.nc', 245 | save_dir+'/'+'wrfchemi_'+str(ihour).zfill(2)+'z_'+ent_inp.split('_')[-1]) 246 | 247 | 248 | if __name__ == '__main__': 249 | ent_dir = "/home/em/桌面/test/201601/" #请设置为自己的排放源数据所在目录的路径 250 | ent_inp = "/home/em/桌面/test/meic2wrf/wrfinput_d01" #请设置为自己的wrfinput文件所在路径 251 | save_dir = "/home/em/桌面/test/meic2wrf/ssssaaaa" #请设置生成的wrfchemi文件保存目录路径 252 | merge_meic_dept(ent_dir) 253 | itp_dis(ent_inp,ent_dir,save_dir) 254 | -------------------------------------------------------------------------------- /namelist.input: -------------------------------------------------------------------------------- 1 | &time_control 2 | run_days = 0, 3 | run_hours = 0, 4 | run_minutes = 0, 5 | run_seconds = 0, 6 | start_year = 2020, 7 | start_month = 06, 8 | start_day = 01, 9 | start_hour = 12, 10 | start_minute = 00, 11 | start_second = 00, 12 | end_year = 2020, 13 | end_month = 07, 14 | end_day = 01, 15 | end_hour = 12, 16 | end_minute = 00, 17 | end_second = 00, 18 | interval_seconds = 21600, 19 | input_from_file = .true., 20 | history_interval = 180, 21 | frames_per_outfile = 24, 22 | restart = .false., 23 | restart_interval = 5000, 24 | io_form_history = 2, 25 | io_form_restart = 2, 26 | io_form_input = 2, 27 | io_form_boundary = 2, 28 | debug_level = 0, 29 | / 30 | 31 | &domains 32 | time_step = 54, 33 | time_step_fract_num = 0, 34 | time_step_fract_den = 1, 35 | max_dom = 1, 36 | s_we = 1, 37 | e_we = 120, 38 | s_sn = 1, 39 | e_sn = 100, 40 | s_vert = 1, 41 | e_vert = 35, 42 | p_top_requested = 5000, 43 | num_metgrid_levels = 38, 44 | dx = 9000, 45 | dy = 9000, 46 | grid_id = 1, 47 | parent_id = 1, 48 | i_parent_start = 1, 49 | j_parent_start = 1, 50 | parent_grid_ratio = 1, 51 | parent_time_step_ratio = 1, 52 | feedback = 1, 53 | smooth_option = 0, 54 | / 55 | 56 | &physics 57 | physics_suite = 'CONUS' 58 | mp_physics = 10, 59 | cu_physics = 3, 60 | ra_lw_physics = 4, 61 | ra_sw_physics = 4, 62 | bl_pbl_physics = -1, 63 | sf_sfclay_physics = -1, 64 | sf_surface_physics = -1, 65 | radt = 30, 66 | bldt = 0, 67 | cudt = 5, 68 | icloud = 1, 69 | num_land_cat = 21, 70 | sf_urban_physics = 0, 71 | 72 | progn = 1, 1, 1, 73 | naer = 1e9, 1e9, 1e9, 74 | mp_zero_out = 2, 75 | mp_zero_out_thresh = 1.e-8, 76 | cu_diag = 1, 77 | cugd_avedx = 1, 78 | isfflx = 1, 79 | ifsnow = 1, 80 | surface_input_source = 1, 81 | num_soil_layers = 4, 82 | maxiens = 1, 83 | maxens = 3, 84 | maxens2 = 3, 85 | maxens3 = 16, 86 | ensdim = 144, 87 | sst_update = 0, 88 | cu_rad_feedback = .true., .true., .true., 89 | / 90 | 91 | &fdda 92 | / 93 | 94 | &dynamics 95 | w_damping = 0, 96 | diff_opt = 1, 97 | km_opt = 4, 98 | diff_6th_opt = 0, 0, 0, 99 | diff_6th_factor = 0.12, 0.12, 0.12, 100 | base_temp = 290., 101 | damp_opt = 0, 102 | zdamp = 5000., 5000., 5000., 103 | dampcoef = 0.01, 0.01, 0.01, 104 | khdif = 0, 0, 0, 105 | kvdif = 0, 0, 0, 106 | non_hydrostatic = .true., .true., .true., 107 | tke_adv_opt = 1, 1, 1, 108 | moist_adv_opt = 2, 2, 2, 109 | scalar_adv_opt = 2, 2, 2, 110 | chem_adv_opt = 2, 2, 2, 111 | / 112 | 113 | &bdy_control 114 | spec_bdy_width = 5, 115 | spec_zone = 1, 116 | relax_zone = 4, 117 | specified = .true., .false., .false., 118 | nested = .false., .true., .true., 119 | / 120 | 121 | &grib2 122 | / 123 | 124 | &chem 125 | kemit = 11, 126 | chem_opt = 10, 10, 10, 127 | bioemdt = 6., 2., 0.666666666, 128 | photdt = 30, 30, 30, 129 | chemdt = 6., 2., 0.666666666, 130 | io_style_emissions = 1, 131 | emiss_inpt_opt = 101, 101, 101, 132 | emiss_opt = 4, 4, 4, 133 | chem_in_opt = 0, 0, 0, 134 | phot_opt = 2, 2, 2, 135 | gas_drydep_opt = 1, 1, 1, 136 | aer_drydep_opt = 1, 1, 1, 137 | bio_emiss_opt = 3, 3, 3, 138 | dust_opt = 3, 139 | dmsemis_opt = 0, 140 | seas_opt = 2, 141 | gas_bc_opt = 1, 1, 1, 142 | gas_ic_opt = 1, 1, 1, 143 | aer_bc_opt = 1, 1, 1, 144 | aer_ic_opt = 1, 1, 1, 145 | gaschem_onoff = 1, 1, 1, 146 | aerchem_onoff = 1, 1, 1, 147 | wetscav_onoff = 1, 1, 1, 148 | cldchem_onoff = 1, 1, 1, 149 | vertmix_onoff = 1, 1, 1, 150 | chem_conv_tr = 1, 151 | biomass_burn_opt = 0, 0, 0, 152 | plumerisefire_frq = 30, 30, 30, 153 | aer_ra_feedback = 1, 1, 1, 154 | have_bcs_chem = .true., .false., .false., 155 | aer_op_opt = 1, 156 | opt_pars_out = 1, 157 | ne_area = 300, 158 | / 159 | 160 | &namelist_quilt 161 | nio_tasks_per_group = 0, 162 | nio_groups = 1, 163 | / 164 | -------------------------------------------------------------------------------- /wrfinput_d01: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jinfan0931/meic2wrf/edeaa7f75667bc22bdc0694e32cbcbb6d6972c2f/wrfinput_d01 --------------------------------------------------------------------------------