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tracer_open.py
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tracer_open.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Open files related to GEOSChem Rn–Pb–Be tracer simulations
Module opens GEOSChem (and Replay) model simulations and MERRA-2 assimilated
data and parses the desired horizontal and vertical extent, time period, and
variables.
Revision History
26022020 -- initial version created
27022020 -- function 'open_merra2_inst3_3d_asm_Nv_specifieddomain' added
"""
def open_geoschem(years, months, simulation, collection, varname, latmin,
latmax, lngmin, lngmax, pmin, pmax, operation=None):
"""function opens daily mean GEOSChem output for tracer/specie of interest
and extracts columned diagnostics for the latitudes, longitudes, and levels
of interest. A sum, average, standard deviation is performed over the
column, if desired.
Parameters
----------
years : list
Year or range of years in measuring period
months : list
Three letter abbreviations (lowercase) for months in measuring period
simulation : str
GEOS-Chem simulation; should be the same as the run directory
(e.g., merra2_2x25_RnPbBe_co50, merra2_2x25_tropchem)
collection : str
GEOS-Chem history collection (e.g., SpeciesConc, AerosolMass)
varname : str
Variable name (e.g., SpeciesConc_TRAC50_40_50, SpeciesConc_O3)
latmin : float
Latitude (degrees north) of bottom edge of bounding box for focus
region. For this parameter and others defining the bounding box,
function finds the closest index to bounding box edges
latmax : float
Latitude (degrees north) of upper edge of bounding box for focus region
lngmin : float
Longitude (degrees east, 0-360) of left edge of bounding box for focus
region
lngmax : float
Longitude (degrees east, 0-360) of right edge of bounding box for focus
region
pmin : float
The upper pressure level of interest, units of hPa
pmax : float
The lower pressure level of interest, units of hPa
operation : str, optional
Operation (mean, sum, std) to apply to the tracer diagnostic column
(the default is None, which implies no operation will be applied to
the column axis).
Returns
-------
var : numpy.ndarray
GEOSChem tracer diagnostics for the region of interest, units of dry
mixing ratio (mol mol-1), [time, lat, lng] or [time, lev, lat, lng]
lat : numpy.ndarray
GEOSChem latitude coordinates, units of degrees north, [lat,]
lng : numpy.ndarray
GEOSChem longitude coordinates, units of degrees east, [lat,]
lev : numpy.ndarray
GEOSChem pressure level coordinates corresponding to the top edge of
the model layer, units of hPa, [lev,]
"""
import time
start_time = time.time()
print('# # # # # # # # # # # # # # # # # # # # # # # # # #\n'+
'Loading GEOSChem %s...' %varname)
import numpy as np
from netCDF4 import Dataset
import sys
sys.path.append('/Users/ghkerr/phd/utils/')
from geo_idx import geo_idx
# Convert month abbrevations to integers
months_int = []
for m in months:
months_int.append(str(time.strptime(m,'%b').tm_mon).zfill(2))
# List will filled with monthly GEOSChem output for variable of interest
var = []
# Loop through years, months of interest
for year in years:
PATH_GEOSCHEM='/Users/ghkerr/phd/tracer/data/'+\
'%s/%d/'%(simulation,year)
for month in months_int:
infile = Dataset(PATH_GEOSCHEM+'GEOSChem.%s.%d%s.nc4'
%(collection,year,month),'r')
# On first iteration, extract dimensions and find indicies
# corresponding to (closest to) desired domain
if (year==years[0]) and (month==months_int[0]):
lat = infile.variables['lat'][:]
lng = infile.variables['lon'][:]
lev = infile.variables['lev'][:]*1000. # Convert to hPa
# Convert longitude from (-180-180) to (0-360)
lng = lng%360
# Shift grid such that it spans (0-360) rather than (180-360,
# 0-180)
lng = np.roll(lng,int(lng.shape[0]/2))
latmin = geo_idx(latmin,lat)
latmax = geo_idx(latmax,lat)
lngmin = geo_idx(lngmin,lng)
lngmax = geo_idx(lngmax,lng)
pmax = (np.abs(lev-pmax)).argmin()
pmin = (np.abs(lev-pmin)).argmin()
lnglen = lng.shape[0]
# Restrict coordinates over focus region
lat = lat[latmin:latmax+1]
lng = lng[lngmin:lngmax+1]
# Although this function indexes the GEOSChem output using the
# 'lev' dimension, it will return the 'ilev' dimension instead.
# ilev represents the hybrid level at interfaces, whereas lev
# represents the hybrid level at midpoints. MERRA-2 dimensions
# represent the model layer top edge and are therefore the
# most directly comparable to ilev.
# i.e.,
# GEOSCHem ilev GEOSChem lev
# --- 940 hPa --
# --- 947.5 hPa --
# --- 955 hPa --
# --- 962.5 hPa --
# --- 970 hPa --
# --- 977.5 hPa --
# --- 985 hPa --
# --- 992.5 hPa --
# -- 1000 hPa --
# So if you have the MERRA-2 model layer corresponding to
# a model top edge at 985 hPa, you'd want to find the the
# value of lev closest to this (lev[0]) and then take the
# ilev[0+1] to find tthe top edge.
lev = infile.variables['ilev'][:]
lev = np.round(lev[pmax+1:pmin+1+1]*1000.)
# Extract variable for the month
var_month = infile.variables[varname][:]
# Roll grid similar to longitude grid
var_month = np.roll(var_month, int(var_month.shape[-1]/2),
axis=np.where(np.array(var_month.shape)==lnglen)[0][0])
# Extract output for horizontal and verticle domain of interest;
# for this to work, dimensions must be (time, lev, lat, lng)
var_month = var_month[:, pmax:pmin+1, latmin:latmax+1,
lngmin:lngmax+1]
if operation == 'mean':
var_month = np.nanmean(var_month, axis=1)
var.append(var_month)
elif operation == 'sum':
var_month = np.nansum(var_month, axis=1)
var.append(var_month)
elif operation == 'std':
var_month = np.nanstd(var_month, axis=1)
var.append(var_month)
elif operation == None:
var.append(var_month)
# Stack
var = np.vstack(var)
print('%s for %d-%d loaded in %.2f seconds!' %(varname, years[0],
years[-1], (time.time()-start_time)))
return var.data, lat.data, lng.data, lev.data
def open_merra2_inst3_3d_asm_Nv_specifieddomain(years, months, varname, lngmin,
latmax, lngmax, latmin, pmin, pmax, operation=None):
"""function opens daily mean MERRA-2 inst3_3d_asm_Nv fields (3d, 3-Hourly,
Instantaneous, Model-Level, Assimilation, Assimilated Meteorological Fields
V5.12.4) for the specified months and years. The variable of interest
is extracted for the region and pressure level(s) of interest. If
specified, function can also compute the column-averaged value.
Parameters
----------
years : list
Year or range of years in measuring period, [years,]
months : list
Three letter abbreviations (lowercase) for months in measuring period
varname : str
Variable of interest reanalysis Options include T (air temperature),
q (specific humidity), U (eastward wind), or V (northward wind)
lngmin : float
Longitude coordinate of the left side (minimum) of the bounding box
containing the focus region, units of degrees east
latmax : float
Latitude coordinate of the top side (maximum) of the bounding box
containing the focus region, units of degrees north
lngmax : float
Longitude coordinate of the right side (maximum) of the bounding box
containing the focus region, units of degrees east
latmin : float
Latitude coordinate of the bottom side (minimum) of the bounding box
containing the focus region, units of degrees north
pmin : float
The upper pressure level of interest, units of hPa
pmax : float
The lower pressure level of interest, units of hPa
operation : str, optional
Operation (mean, sum, std) to apply to the tracer diagnostic column
(the default is None, which implies no operation will be applied to
the column axis).
Returns
-------
var : numpy.ndarray
Model output for specified variable, units of ppbv, if operation =
None then shape is [time, lat, lng] if else the shape is [time, lev,
lat, lng]. Note the 0th index of the level dimension corresponds to
the pressure level closest to the surface (i.e., closest to pmax)
lat : numpy.ndarray
Model latitude coordinates, units of degrees north, [lat,]
lng : numpy.ndarray
Model numpy.ndarray coordinates, units of degrees east, [lng,]
lev : numpy.ndarray
Model pressure levels corresponding to the top edge of the layer, units
of hPa, [lev]
"""
import time
print('# # # # # # # # # # # # # # # # # # # # # # # # # #\n'+
'Loading %s from MERRA-2 simulation...' %varname)
start_time = time.time()
import numpy as np
from netCDF4 import Dataset
import sys
sys.path.append('/Users/ghkerr/phd/utils/')
from geo_idx import geo_idx
# Define pressures; for whatever reason the value of the pressues didn't
# download with the MERRA-2 model-level data, so I manually copy and
# pasted them from here from pg. 10 of
# https://gmao.gsfc.nasa.gov/pubs/docs/Bosilovich785.pdf
# Within this dataset, the pressure corresponding to the vertical level
# are nominal for a 1000 hPa surface pressure and refer to the top edge of
# the layer. Note that the bottom level layer has a nominal thickness of 15 hPa.
presslev = {43 : 208.152, 44 : 244.875, 45 : 288.083, 46 : 337.500,
47 : 375.000, 48 : 412.500, 49 : 450.000, 50 : 487.500, 51 : 525.000,
52 : 562.500, 53 : 600.000, 54 : 637.500, 55 : 675.000, 56 : 700.000,
57 : 725.000, 58 : 750.000, 59 : 775.000, 60 : 800.000, 61 : 820.000,
62 : 835.000, 63 : 850.000, 64 : 865.000, 65 : 880.000, 66 : 895.000,
67 : 910.000, 68 : 925.000, 69 : 940.000, 70 : 955.000, 71 : 970.000,
72 : 985.000}
# Convert month abbrevations to integers
months_int = []
for m in months:
months_int.append(str(time.strptime(m,'%b').tm_mon).zfill(2))
# List will filled with monthly GEOSChem output for variable of interest
var = []
# Loop through years, months of interest
for year in years:
PATH_MERRA='/Users/ghkerr/phd/meteorology/data/inst3_3d_asm_Nv/'+\
'%d/'%(year)
for month in months_int:
infile = Dataset(PATH_MERRA+'MERRA2_300.inst3_3d_asm_Nv.%d%s.nc'
%(year,month),'r')
# On first iteration, extract dimensions and find indicies
# corresponding to (closest to) desired domain
if (year==years[0]) and (month==months_int[0]):
lat = infile.variables['lat'][:]
lng = infile.variables['lon'][:]
# Convert longitude from (-180-180) to (0-360)
lng = lng%360
# Shift grid such that it spans (0-360) rather than (180-360,
# 0-180)
lng = np.roll(lng,int(lng.shape[0]/2))
# Kludgey
if (lng.data[0] > 359.5) & (lng.data[0] <= 360.):
lng[0] = 0.
latmin = geo_idx(latmin,lat)
latmax = geo_idx(latmax,lat)
lngmin = geo_idx(lngmin,lng)
lngmax = geo_idx(lngmax,lng)
lev = np.fromiter(presslev.values(), dtype=float)
pmax = (np.abs(lev-pmax)).argmin()
pmin = (np.abs(lev-pmin)).argmin()
lnglen = lng.shape[0]
# Restrict coordinates over focus region
lat = lat[latmin:latmax+1]
lng = lng[lngmin:lngmax+1]
lev = np.flip(lev[pmin:pmax+1], axis=0)
# Extract variable for the month
var_month = infile.variables[varname][:]
# Roll grid similar to longitude grid
var_month = np.roll(var_month, int(var_month.shape[-1]/2),
axis=np.where(np.array(var_month.shape)==lnglen)[0][0])
# Extract output for horizontal and verticle domain of interest;
# for this to work, dimensions must be (time, lev, lat, lng)
var_month = var_month[:, pmin:pmax+1, latmin:latmax+1,
lngmin:lngmax+1]
# Flip level dimension such that the lowest layer corresponds to
# the pressure level closest to the ground (closest to pmax)
var_month = np.flip(var_month, axis=1)
if operation == 'mean':
var_month = np.nanmean(var_month, axis=1)
var.append(var_month)
elif operation == 'sum':
var_month = np.nansum(var_month, axis=1)
var.append(var_month)
elif operation == 'std':
var_month = np.nanstd(var_month, axis=1)
var.append(var_month)
elif operation == None:
var.append(var_month)
# Stack
var = np.vstack(var)
print('%s for %d-%d loaded in %.2f seconds!' %(varname, years[0],
years[-1], (time.time()-start_time)))
return var, lat, lng, lev