def historical_sum_swe(lat_index,lon_index): ''' this function uses the Livneh historical dataset (from 1950-2005) to check whether the historical sum of SWE in the input grid cell is greater than or equal to 10 mm ''' import os import numpy as np direc = '/raid9/gergel/agg_snowpack/goodleap/SWE' filename = 'livneh_april1swe.nc' from snowpack_functions import unpack_netcdf_file_var lats,lons,swe,datess = unpack_netcdf_file_var(direc,filename,"swe") historical_sum_swe = 0 for year in np.arange(len(swe)): historical_sum_swe += swe[year,lat_index,lon_index] mean_swe = historical_sum_swe/len(swe) if mean_swe >= 10: return True else: return False
def historical_sum_swe(lat_index, lon_index):
'''
this function uses the Livneh historical dataset (from 1950-2005) to check whether the historical sum of SWE
in the input grid cell is greater than or equal to 10 mm
'''
import os
import numpy as np
direc = '/raid9/gergel/agg_snowpack/goodleap/SWE'
filename = 'livneh_april1swe.nc'
from snowpack_functions import unpack_netcdf_file_var
lats, lons, swe, datess = unpack_netcdf_file_var(direc, filename, "swe")
historical_sum_swe = 0
for year in np.arange(len(swe)):
historical_sum_swe += swe[year, lat_index, lon_index]
mean_swe = historical_sum_swe / len(swe)
if mean_swe >= 10:
return True
else:
return False
#!/bin/python import numpy as np import sys import os import math from snowpack_functions import import_gridcell_elevation,get_elev_for_lat_lon,lat_lon_adjust,unpack_netcdf_swe_month,mask_latlon,unpack_netcdf_file_var,historical_sum_swe import pandas as pd ## get command line arguments args = sys.argv[1:] basin = args[0] scenario = args[1] chunk = args[2] direc = '/raid9/gergel/agg_snowpack/goodleap/%s' %basin file_swe = 'SWE_ensavg_%s_%s.nc' %(scenario,basin) lats,lons,swe,datess = unpack_netcdf_file_var(direc,file_swe,"swe") soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt' elev_corr_info = import_gridcell_elevation(soil_file) ## step 1: load data sources ## PET: all three: NatVeg, Short, Tall ## AET: evaporation plus transpiration ## compare pet and aet from VIC (then see about computing aet using pm reference et) pet_agg = list() aet_agg = list() lats_inc = list() lons_inc = list() coms = list() elevs = list()
def swe_elevation_mean(lats,lons,swe,datess,elev_corr_info,basin,scenario):
import numpy as np
import math
from snowpack_functions import unpack_netcdf_file_var,get_elev_for_lat_lon,mask_latlon,lat_lon_adjust
## get historical SWE for model so that historical mean can be determined for mask
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' %basin
file_hist = 'SWE_ensavg_%s_%s.nc' %("historical",basin)
file = 'SWE_ensavg_%s_%s.nc' %(scenario,basin)
lats_hist,lons_hist,swe_hist,datess_hist = unpack_netcdf_file_var(direc,file_hist,"swe")
## list for latitude and longitude values included in the historical mean mask
lats_lons_inc_in_mask = list()
## initialize lists for storing grid cell elevations
if (scenario == "historical"):
swe_inc = list()
elev_inc = list()
else:
swe_1 = list()
elev_1 = list()
swe_2 = list()
elev_2 = list()
swe_3 = list()
elev_3 = list()
count = 0
################# loop through array and get latitude, longitude and swe values for each #############
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
### isnan will convert masked values to nan's, so this statement will evaluate to false if
### the value exists (and thus is not masked)
historical_mean_swe = 0
## calculate historical mean SWE for grid cell
if (math.isnan(swe[0,j,k]) == False):
if_in_box = mask_latlon(lats[j],lons[k],basin)
adjust_mask = lat_lon_adjust(lats[j],lons[k],basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year,j,k]
historical_mean_swe = historical_sum_swe/len(swe_hist)
if (historical_mean_swe >= 10):
count += 1
## get elevation of grid cell
elevation_individual_gridcell = get_elev_for_lat_lon(elev_corr_info,lats[j],lons[k])
## add swe and elevation values to lists depending on scenario
if (scenario == "historical"):
sum_swe = 0
for year in np.arange(len(swe)): ## calculate mean swe
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(swe)
mean_swe = mean_swe*0.001 # convert mm to meters
swe_inc.append(mean_swe)
elev_inc.append(elevation_individual_gridcell)
else:
sum_swe = 0
for year in np.arange(4,34):
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(np.arange(4,33))
mean_swe = mean_swe*0.001 # convert mm to meters
swe_1.append(mean_swe)
elev_1.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(34,64):
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(np.arange(4,33))
mean_swe = mean_swe*0.001 # convert mm to meters
swe_2.append(mean_swe)
elev_2.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(64,94):
sum_swe += swe[year,j,k]
mean_swe = sum_swe/len(np.arange(4,33))
mean_swe = mean_swe*0.001 # convert mm to meters
swe_3.append(mean_swe)
elev_3.append(elevation_individual_gridcell)
print(count)
##### save arrays to files for a multimodel average (and for spatial plots with lats and lons)
## define path based on scenario
filearrayname = '/raid9/gergel/agg_snowpack/elevations/ensavg_%s_%s.npz' %(basin,scenario)
if (scenario == "historical"):
np.savez(filearrayname,swe=np.asarray(swe_inc),elevations=np.asarray(elev_inc))
return (swe_inc,elev_inc)
else:
np.savez(filearrayname,swe1=np.asarray(swe_1),swe2=np.asarray(swe_2),swe3=np.asarray(swe_3),elev1=np.asarray(elev_1),elev2=np.asarray(elev_2),elev3=np.asarray(elev_3))
return (swe_1,swe_2,swe_3,elev_1,elev_2,elev_3)
return (swe_1,swe_2,swe_3,elev_1,elev_2,elev_3)
################################################ BEGIN LOESS ANALYSIS #########################################
import numpy as np
import sys
from snowpack_functions import unpack_netcdf_file_var, import_gridcell_elevation
## get command line arguments
args = sys.argv[1:]
basin = args[0]
scenario = args[1]
soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt'
elev_corr_info = import_gridcell_elevation(soil_file)
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' %basin
file = 'SWE_ensavg_%s_%s.nc' %(scenario,basin)
lats, lons, swe, datess = unpack_netcdf_file_var(direc,file,"swe")
if (scenario == "historical"):
swe_inc, elev_inc = swe_elevation_mean(lats,lons,swe,datess,elev_corr_info,basin,scenario)
else:
swe1,swe2,swe3,elev1,elev2,elev3 = swe_elevation_mean(lats,lons,swe,datess,elev_corr_info,basin,scenario)
##################### plot ############################
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import statsmodels.api as sm
plt.figure(figsize=(16,4))
lw=2.0
al=0.7
if (scenario == "historical"):
def swe_elevation_mean(lats, lons, swe, datess, elev_corr_info, basin,
scenario):
import numpy as np
import math
from snowpack_functions import unpack_netcdf_file_var, get_elev_for_lat_lon, mask_latlon, lat_lon_adjust
## get historical SWE for model so that historical mean can be determined for mask
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' % basin
file_hist = 'SWE_ensavg_%s_%s.nc' % ("historical", basin)
file = 'SWE_ensavg_%s_%s.nc' % (scenario, basin)
lats_hist, lons_hist, swe_hist, datess_hist = unpack_netcdf_file_var(
direc, file_hist, "swe")
## list for latitude and longitude values included in the historical mean mask
lats_lons_inc_in_mask = list()
## initialize lists for storing grid cell elevations
if (scenario == "historical"):
swe_inc = list()
elev_inc = list()
else:
swe_1 = list()
elev_1 = list()
swe_2 = list()
elev_2 = list()
swe_3 = list()
elev_3 = list()
count = 0
################# loop through array and get latitude, longitude and swe values for each #############
for j in np.arange(len(lats)): ### loop over latitude
for k in np.arange(len(lons)): ### loop over longitude
### don't calculate area for missing value elements
### isnan will convert masked values to nan's, so this statement will evaluate to false if
### the value exists (and thus is not masked)
historical_mean_swe = 0
## calculate historical mean SWE for grid cell
if (math.isnan(swe[0, j, k]) == False):
if_in_box = mask_latlon(lats[j], lons[k], basin)
adjust_mask = lat_lon_adjust(lats[j], lons[k], basin)
if if_in_box and adjust_mask:
historical_sum_swe = 0
for year in np.arange(len(swe_hist)):
historical_sum_swe += swe_hist[year, j, k]
historical_mean_swe = historical_sum_swe / len(swe_hist)
if (historical_mean_swe >= 10):
count += 1
## get elevation of grid cell
elevation_individual_gridcell = get_elev_for_lat_lon(
elev_corr_info, lats[j], lons[k])
## add swe and elevation values to lists depending on scenario
if (scenario == "historical"):
sum_swe = 0
for year in np.arange(
len(swe)): ## calculate mean swe
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(swe)
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_inc.append(mean_swe)
elev_inc.append(elevation_individual_gridcell)
else:
sum_swe = 0
for year in np.arange(4, 34):
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(np.arange(4, 33))
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_1.append(mean_swe)
elev_1.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(34, 64):
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(np.arange(4, 33))
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_2.append(mean_swe)
elev_2.append(elevation_individual_gridcell)
sum_swe = 0
for year in np.arange(64, 94):
sum_swe += swe[year, j, k]
mean_swe = sum_swe / len(np.arange(4, 33))
mean_swe = mean_swe * 0.001 # convert mm to meters
swe_3.append(mean_swe)
elev_3.append(elevation_individual_gridcell)
print(count)
##### save arrays to files for a multimodel average (and for spatial plots with lats and lons)
## define path based on scenario
filearrayname = '/raid9/gergel/agg_snowpack/elevations/ensavg_%s_%s.npz' % (
basin, scenario)
if (scenario == "historical"):
np.savez(filearrayname,
swe=np.asarray(swe_inc),
elevations=np.asarray(elev_inc))
return (swe_inc, elev_inc)
else:
np.savez(filearrayname,
swe1=np.asarray(swe_1),
swe2=np.asarray(swe_2),
swe3=np.asarray(swe_3),
elev1=np.asarray(elev_1),
elev2=np.asarray(elev_2),
elev3=np.asarray(elev_3))
return (swe_1, swe_2, swe_3, elev_1, elev_2, elev_3)
return (swe_1, swe_2, swe_3, elev_1, elev_2, elev_3)
################################################ BEGIN LOESS ANALYSIS #########################################
import numpy as np
import sys
from snowpack_functions import unpack_netcdf_file_var, import_gridcell_elevation
## get command line arguments
args = sys.argv[1:]
basin = args[0]
scenario = args[1]
soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt'
elev_corr_info = import_gridcell_elevation(soil_file)
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' % basin
file = 'SWE_ensavg_%s_%s.nc' % (scenario, basin)
lats, lons, swe, datess = unpack_netcdf_file_var(direc, file, "swe")
if (scenario == "historical"):
swe_inc, elev_inc = swe_elevation_mean(lats, lons, swe, datess,
elev_corr_info, basin, scenario)
else:
swe1, swe2, swe3, elev1, elev2, elev3 = swe_elevation_mean(
lats, lons, swe, datess, elev_corr_info, basin, scenario)
##################### plot ############################
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import statsmodels.api as sm
plt.figure(figsize=(16, 4))
lw = 2.0
# filename_hist = 'vic_TotalSoilMoist_%s_%s_%s_%s_WUSA_monthly_monmean.nc' % (model,modnum,"historical","1950_2005")
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' % basin
if (variable == "TotalSoilMoist"):
filename_hist = '%s__%s.monmean.TotalSoilMoist.%s_%s_summer.nc' % (
model, "historical", "1950_2005", basin)
filename = '%s__%s.monmean.TotalSoilMoist.%s_%s_summer.nc' % (
model, scenario, years, basin)
else:
filename_hist = '%s__%s.%s.%s.%s_%s_%s.nc' % (
model, "historical", monthcalc, variable, "1950_2005", basin, season)
filename = '%s__%s.%s.%s.%s_%s_%s.nc' % (model, scenario, monthcalc,
variable, years, basin, season)
## load data
if (variable == "TotalSoilMoist"):
lats, lons, sm_hist, datess_hist = unpack_netcdf_file_var(
direc, filename_hist, "TotalSoilMoist")
lats, lons, sm, datess = unpack_netcdf_file_var(direc, filename,
"TotalSoilMoist")
else:
print(os.path.join(direc, filename_hist))
print(os.path.join(direc, filename))
lats, lons, sm_hist, datess_hist = unpack_netcdf_file_var(
direc, filename_hist, variable)
lats, lons, sm, datess = unpack_netcdf_file_var(direc, filename, variable)
## load historical mean SWE file and get mask
direc = '/raid9/gergel/agg_snowpack/goodleap/SWE'
filename = 'histmeanmask.nc' ## True where hist mean swe > 10 mm
filename_lowlands = 'histmeanmask_lowlands.nc' ## True where hist mean swe < 10 mm
lats_swe, lons_swe, hist_swe, datess_swe = unpack_netcdf_file_var(
direc, filename, "swe")
#if (basin == "missouri") or (basin == "coastalsouth") or (basin == "lower_colorado") or ("basin == nwinterior") or ("basin == great_basin"):
# direc = '/raid9/gergel/agg_snowpack/is_summ/%s_%s_%s_monthly.rec' % (model,modnum,scenario)
# hist_direc = '/raid9/gergel/agg_snowpack/is_summ/%s_%s_%s_monthly.rec' % (model,modnum,"historical")
# filename = 'vic_TotalSoilMoist_%s_%s_%s_%s_WUSA_monthly_monmean.nc' % (model,modnum,scenario,years)
# filename_hist = 'vic_TotalSoilMoist_%s_%s_%s_%s_WUSA_monthly_monmean.nc' % (model,modnum,"historical","1950_2005")
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' %basin
if (variable == "TotalSoilMoist"):
filename_hist = '%s__%s.monmean.TotalSoilMoist.%s_%s_summer.nc' % (model,"historical","1950_2005",basin)
filename = '%s__%s.monmean.TotalSoilMoist.%s_%s_summer.nc' % (model,scenario,years,basin)
else:
filename_hist = '%s__%s.%s.%s.%s_%s_%s.nc' % (model,"historical",monthcalc,variable,"1950_2005",basin,season)
filename = '%s__%s.%s.%s.%s_%s_%s.nc' % (model,scenario,monthcalc,variable,years,basin,season)
## load data
if (variable == "TotalSoilMoist"):
lats,lons,sm_hist,datess_hist = unpack_netcdf_file_var(direc,filename_hist,"TotalSoilMoist")
lats,lons,sm,datess = unpack_netcdf_file_var(direc,filename,"TotalSoilMoist")
else:
print(os.path.join(direc,filename_hist))
print(os.path.join(direc,filename))
lats,lons,sm_hist,datess_hist = unpack_netcdf_file_var(direc,filename_hist,variable)
lats,lons,sm,datess = unpack_netcdf_file_var(direc,filename,variable)
## load historical mean SWE file and get mask
direc = '/raid9/gergel/agg_snowpack/goodleap/SWE'
filename = 'histmeanmask.nc' ## True where hist mean swe > 10 mm
filename_lowlands = 'histmeanmask_lowlands.nc' ## True where hist mean swe < 10 mm
lats_swe,lons_swe,hist_swe,datess_swe = unpack_netcdf_file_var(direc,filename,"swe")
hist_swe_mod = create_mask_mtn_ranges(hist_swe,lats_swe,lons_swe)
lats_swe,lons_swe,hist_noswe,datess_swe = unpack_netcdf_file_var(direc,filename_lowlands,"swe")
file_tasmin = 'tasmin_ensavg_%s_%s.nc' %(scenario,basin)
file_pr = 'pr_ensavg_%s_%s.nc' %(scenario,basin)
else:
if (scenario == "historical"):
file_swe = '%s__%s.monday1.SWE.1950_2005_%s.nc' %(model,scenario,basin)
file_tasmax = '%s__%s.monmean.tasmax.1950_2005_%s.nc' %(model,scenario,basin)
file_tasmin = '%s__%s.monmean.tasmin.1950_2005_%s.nc' %(model,scenario,basin)
file_pr = '%s__%s.monsum.pr.1950_2005_%s.nc' %(model,scenario,basin)
else:
file_swe = '%s__%s.monday1.SWE.2006_2099_%s.nc' %(model,scenario,basin)
file_tasmax = '%s__%s.monmean.tasmax.2006_2099_%s.nc' %(model,scenario,basin)
file_tasmin = '%s__%s.monmean.tasmin.2006_2099_%s.nc' %(model,scenario,basin)
file_pr = '%s__%s.monsum.pr.2006_2099_%s.nc' %(model,scenario,basin)
print(direc)
print(file_swe)
lats, lons, swe, datess_swe = unpack_netcdf_file_var(direc,file_swe,"swe")
lats, lons, temp_max, datess_temp = unpack_netcdf_file_var(direc,file_tasmax,"air_temp_max")
lats, lons, temp_min, datess_temp = unpack_netcdf_file_var(direc,file_tasmin,"air_temp_min")
lats, lons, precip, datess_precip = unpack_netcdf_file_var(direc,file_pr,"pr")
## adjust data for hydro years
precip = precip[3:-2,:,:]
swe = swe[1:,:,:]
temp_max = temp_max[3:-2,:,:]
temp_min = temp_min[3:-2,:,:]
datess_swe = datess_swe[1:]
datess_precip = datess_precip[3:-2]
## get historical SWE data to determine which lats/lons to include in analysis
lats, lons, swe_hist, datess_swe_hist = unpack_netcdf_gen("SWE", basin, "historical")
#!/bin/python import numpy as np import sys import os import math from snowpack_functions import import_gridcell_elevation, get_elev_for_lat_lon, lat_lon_adjust, unpack_netcdf_swe_month, mask_latlon, unpack_netcdf_file_var, historical_sum_swe import pandas as pd ## get command line arguments args = sys.argv[1:] basin = args[0] scenario = args[1] chunk = args[2] direc = '/raid9/gergel/agg_snowpack/goodleap/%s' % basin file_swe = 'SWE_ensavg_%s_%s.nc' % (scenario, basin) lats, lons, swe, datess = unpack_netcdf_file_var(direc, file_swe, "swe") soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt' elev_corr_info = import_gridcell_elevation(soil_file) ## step 1: load data sources ## PET: all three: NatVeg, Short, Tall ## AET: evaporation plus transpiration ## compare pet and aet from VIC (then see about computing aet using pm reference et) pet_agg = list() aet_agg = list() lats_inc = list() lons_inc = list() coms = list() elevs = list()
if (type == "ensavg"):
scenario = args[2]
else:
model = args[2]
scenario = args[3]
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' %basin
if (type == "ensavg"):
file_swe = 'SWE_ensavg_%s_%s.nc' %(scenario,basin)
else:
if (scenario == "historical"):
file_swe = '%s__%s.monday1.SWE.1950_2005_%s.nc' %(model,scenario,basin)
else:
file_swe = '%s__%s.monday1.SWE.2006_2099_%s.nc' %(model,scenario,basin)
lats,lons,swe,datess_swe = unpack_netcdf_file_var(direc,file_swe,"swe")
resol=0.0625
swe_yearly_agg = np.ndarray(shape = (len(swe),1), dtype=float)
sm_yearly_agg_mar = np.ndarray(shape = (len(swe),1),dtype=float)
sm_yearly_agg_aug = np.ndarray(shape = (len(swe),1),dtype=float)
## get historical data for soil moisture to be done
if (type == "ensavg"):
## historical file names
file_sm_mar_hist = 'TotalSoilMoist_ensavg_%s_%s_march.nc' %("historical",basin)
file_sm_aug_hist = 'TotalSoilMoist_ensavg_%s_%s_august.nc' %("historical",basin)
## other filenames
file_sm_mar = 'TotalSoilMoist_ensavg_%s_%s_march.nc' %(scenario,basin)
file_sm_aug = 'TotalSoilMoist_ensavg_%s_%s_august.nc' %(scenario,basin)
file_tasmax = '%s__%s.monmean.tasmax.1950_2005_%s.nc' % (
model, scenario, basin)
file_tasmin = '%s__%s.monmean.tasmin.1950_2005_%s.nc' % (
model, scenario, basin)
file_pr = '%s__%s.monsum.pr.1950_2005_%s.nc' % (model, scenario, basin)
else:
file_swe = '%s__%s.monday1.SWE.2006_2099_%s.nc' % (model, scenario,
basin)
file_tasmax = '%s__%s.monmean.tasmax.2006_2099_%s.nc' % (
model, scenario, basin)
file_tasmin = '%s__%s.monmean.tasmin.2006_2099_%s.nc' % (
model, scenario, basin)
file_pr = '%s__%s.monsum.pr.2006_2099_%s.nc' % (model, scenario, basin)
print(direc)
print(file_swe)
lats, lons, swe, datess_swe = unpack_netcdf_file_var(direc, file_swe, "swe")
lats, lons, temp_max, datess_temp = unpack_netcdf_file_var(
direc, file_tasmax, "air_temp_max")
lats, lons, temp_min, datess_temp = unpack_netcdf_file_var(
direc, file_tasmin, "air_temp_min")
lats, lons, precip, datess_precip = unpack_netcdf_file_var(
direc, file_pr, "pr")
## adjust data for hydro years
precip = precip[3:-2, :, :]
swe = swe[1:, :, :]
temp_max = temp_max[3:-2, :, :]
temp_min = temp_min[3:-2, :, :]
datess_swe = datess_swe[1:]
datess_precip = datess_precip[3:-2]
else:
model = args[2]
scenario = args[3]
direc = '/raid9/gergel/agg_snowpack/goodleap/%s' % basin
if (type == "ensavg"):
file_swe = 'SWE_ensavg_%s_%s.nc' % (scenario, basin)
else:
if (scenario == "historical"):
file_swe = '%s__%s.monday1.SWE.1950_2005_%s.nc' % (model, scenario,
basin)
else:
file_swe = '%s__%s.monday1.SWE.2006_2099_%s.nc' % (model, scenario,
basin)
lats, lons, swe, datess_swe = unpack_netcdf_file_var(direc, file_swe, "swe")
resol = 0.0625
swe_yearly_agg = np.ndarray(shape=(len(swe), 1), dtype=float)
sm_yearly_agg_mar = np.ndarray(shape=(len(swe), 1), dtype=float)
sm_yearly_agg_aug = np.ndarray(shape=(len(swe), 1), dtype=float)
## get historical data for soil moisture to be done
if (type == "ensavg"):
## historical file names
file_sm_mar_hist = 'TotalSoilMoist_ensavg_%s_%s_march.nc' % ("historical",
basin)
file_sm_aug_hist = 'TotalSoilMoist_ensavg_%s_%s_august.nc' % ("historical",
basin)
## other filenames
file_sm_mar = 'TotalSoilMoist_ensavg_%s_%s_march.nc' % (scenario, basin)
import numpy as np
import os
import math
import sys
args = sys.argv[1:]
variable = args[0]
chunk = args[1]
scenario = args[2]
basin = args[3]
## get data
direc = '/raid9/gergel/agg_snowpack/climatology/data/'
filename = '%s_ensavg_%s_%s_%s.nc' %("pr",scenario,basin,chunk)
lats,lons,var,datess = unpack_netcdf_file_var(direc,filename,"pr")
filename = '%s_ensavg_%s_%s_%s.nc' %("tasmax",scenario,basin,chunk)
lats,lons,var_max,datess = unpack_netcdf_file_var(direc,filename,"air_temp_max")
filename = '%s_ensavg_%s_%s_%s.nc' %("tasmin",scenario,basin,chunk)
lats,lons,var_min,datess = unpack_netcdf_file_var(direc,filename,"air_temp_min")
var_series = list()
lats_inc = list()
lons_inc = list()
## process data
for j in np.arange(len(lats)): ## loop over latitude
for k in np.arange(len(lons)): ## loop over longitude
if (math.isnan(var[0,j,k])) == False:
#if_in_box = mask_latlon(lats[j],lons[k],basin)
#adjust_mask = lat_lon_adjust(lats[j],lons[k],basin)
if_in_box = mask_out_other_mtns(lats[j],lons[k])