-
Notifications
You must be signed in to change notification settings - Fork 11
/
combine_emissions.py
141 lines (119 loc) · 5.47 KB
/
combine_emissions.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 31 09:43:24 2019
Script for merging TNO and NAEI emission files
;
; This is based on Doug Lowes NCL script to do the same thing
; but with TNO/NAEI whereas we will use EDGAR/NAEI
; - treatment of the emission variables will be hardcoded,
; so that we can check for existence of NAEI emissions.
;
; Rules for merging:
; 1) EDGAR emissions will only be taken where there are no
; NAEI emissions (this will be based on a total summation of
; NAEI emissions, rather than done on a species by species basis).
; 2) A multiplying factor of 1.6 is applied to OC_DOM, OC_TRA, and OC_25_10
; emissions (to roughly account for the Carbon to "everything" mass ratio for OM)
; 4) OIN 2.5 PM species will be either the difference between
; E_PM25 and the sum of BC_1 + EC_1_25 + OC_DOM + OC_TRA
; or 10% of the E_PM25 mass (whichever is smaller)
; (we'll make sure that all emissions are >0)
; 5) OIN 10 PM species will be the difference between
; E_PM_10 and the sum of OC_25_10 + EC_25_10
; (we'll make sure that all emissions are >0)
@author: ee15amg
"""
import numpy as np
from netCDF4 import Dataset
from netCDF4 import num2date, date2num
#import time
#from scipy.io.netcdf import netcdf_file
#import matplotlib.pyplot as plt
# define input and output files
filename_edgar = ('wrfchemi_edgar_00z_d01')
filename_naei = ('wrfchemi_naei_00z_d01')
filename_combined = ('wrfchemi_00z_d01')
# open the files for processing
F_NAEI = Dataset(filename_naei,"r")
F_EDGAR = Dataset(filename_edgar,"r")
F_OUT = Dataset(filename_combined, "w")
#F_OUT = netcdf_file(filename_combined, "w")
# full list of variables (for calculating the total NAEI emissions)
var_full = (['E_CH4','E_ECI','E_ECJ','E_CO','E_C2H2','E_NH3','E_NO',
'E_NO2','E_ORGI','E_ORGJ','E_PM_10','E_PM25I','E_PM25J',
'E_SO2','E_BIGALK','E_BIGENE','E_C2H4','E_C2H5OH','E_C2H6',
'E_CH2O','E_CH3CHO','E_CH3COCH3','E_CH3OH','E_MEK','E_TOLUENE',
'E_C3H6','E_C3H8','E_BENZENE','E_XYLENE'])
# list of variables for which we filter TNO emissions to avoid clashs with
# NAEI emissions
var_filter = (['E_CH4','E_ECI','E_ECJ','E_CO','E_C2H2','E_NH3','E_NO',
'E_NO2','E_ORGI','E_ORGJ','E_PM_10','E_PM25I','E_PM25J',
'E_SO2','E_BIGALK','E_BIGENE','E_C2H4','E_C2H5OH','E_C2H6',
'E_CH2O','E_CH3CHO','E_CH3COCH3','E_CH3OH','E_MEK','E_TOLUENE',
'E_C3H6','E_C3H8','E_BENZENE','E_XYLENE'])
#create output netcdf following same layout as input files
#;;;;;;;;;;;;;; operational section of script ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
# loop through variables to pull out for making full NAEI summation
# (units don't matter here - it's a purely binary check)
var_tot = np.zeros([12,1,139,139])
for i_var in range(len(var_full)):
var_temp = np.asarray(F_NAEI[var_full[i_var]])
var_tot[:,:,:,:]=(var_tot[:,:,:,:] + var_temp[:,:,:,:])
# create data mask to apply to TNO data
# initialise mask to correct dimensions
naei_empty = var_tot[:,:,:,:]*0.0
# set mask to zero where there's NAEI data - and 1.0 where there isn't
mask= np.where(var_tot==0)
naei_empty[mask] = naei_empty[mask]+1.0
#check mask works
#print np.max(var_tot[mask])
## create NETCDF file to put data into
##create Dimensions
#
#get number of hours from netcdf data
lenDateStr = len(np.asarray(F_NAEI['Times'])[0,:])
n_lons = np.size(np.asarray(F_NAEI['XLONG'])[0,:])
n_lats = np.size(np.asarray(F_NAEI['XLAT'])[0,:])
n_emis = np.size(np.asarray(F_NAEI['E_CO'])[0,:,0,0])
n_times = np.size(np.asarray(F_NAEI['Times'])[:,0])
# copy all global attributes from old file to new one
# also copy old dimensions from old to new netcdf
F_OUT.setncatts(F_EDGAR.__dict__)
for name, dimension in F_EDGAR.dimensions.items():
F_OUT.createDimension(
name, (len(dimension) if not dimension.isunlimited() else None))
# add dimensions into netcdf
#Time = F_OUT.createDimension("Time",n_times)
#emissions_zdim_stag = F_OUT.createDimension("emissions_zdim_stag",n_emis)
#south_north = F_OUT.createDimension("south_north",n_lats)
#west_east = F_OUT.createDimension("west_east",n_lons)
#DateStrLen = F_OUT.createDimension("DateStrLen",lenDateStr)
#create Variables
Times = F_OUT.createVariable("Times","S1",("Time","DateStrLen")),
XLONG = F_OUT.createVariable("XLONG","f4",("south_north","west_east")),
XLAT = F_OUT.createVariable("XLAT","f4",("south_north","west_east"))
for i_var in range(np.size(var_full)):
var_filter[i_var] = F_OUT.createVariable((var_filter[i_var]),"f4",
("Time","emissions_zdim_stag","south_north","west_east"))
# fill basic variables manually
lat = np.asarray(F_NAEI['XLAT'])
lon = np.asarray(F_NAEI['XLONG'])
datetime = np.asarray(F_NAEI['Times'])
XLAT = lat
XLONG = lon
Times = datetime
# then loop through chem species to add them into file
#loop through the variables to be combined (in a straightforward manner)
for i_var in range(len(var_filter)):
# load data
var_edgar = np.asarray(F_EDGAR[var_full[i_var]])
var_naei= np.asarray(F_NAEI[var_full[i_var]])
# merge data files - applying the filter
# where naei data exists we wont input edgar vars
#(multiplying them by zero takes care of this)
var_naei_new = var_naei + naei_empty*var_edgar
# save data
#print var_full[i_var]
var_filter[i_var][:,:,:,:] = var_naei_new[:,:,:,:]
F_OUT.close()