def estimate_relative_humidity(q,t_avg,lat,lon):
## estimates relative humidity using hypsometric equation for pressure, virtual temperature and
## average temperature
import numpy as np
from snowpack_functions import get_elev_for_lat_lon,import_gridcell_elevation
#################### CONSTANTS ###############################
Rd = 287.04 ## J kg^-1 K ^-1
p1 = 100000 ## Pa
T0 = 273.15 ## reference temperature
g = 9.81 ## m / s^2
## need to get elevations for grid cells
soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt'
elev_corr_info = import_gridcell_elevation(soil_file)
h = get_elev_for_lat_lon(elev_corr_info,lat,lon)
## Tv = T(1 + 0.61w)
Tv = t_avg * (1 + 0.61*q)
## p = e ^ (-(((z_2 - z_1) * g)/ (Rd*Tv) - ln(p1)))
hg = h * g
RdTv = Rd * Tv
p = np.exp(-((np.divide(hg,RdTv.air_temp_min)) - math.log(p1)))
## RH = 0.263*p*q[exp(17.67*T - T0)/(T - 29.65)]^-1
RH = 0.263*p*q * ((np.exp((17.67 * (t_avg - T0))/ (t_avg - 29.65)))**-1)
return (RH)
def estimate_relative_humidity(q, t_avg, lat, lon):
## estimates relative humidity using hypsometric equation for pressure, virtual temperature and
## average temperature
import numpy as np
from snowpack_functions import get_elev_for_lat_lon, import_gridcell_elevation
#################### CONSTANTS ###############################
Rd = 287.04 ## J kg^-1 K ^-1
p1 = 100000 ## Pa
T0 = 273.15 ## reference temperature
g = 9.81 ## m / s^2
## need to get elevations for grid cells
soil_file = '/raid9/gergel/agg_snowpack/soil_avail.txt'
elev_corr_info = import_gridcell_elevation(soil_file)
h = get_elev_for_lat_lon(elev_corr_info, lat, lon)
## Tv = T(1 + 0.61w)
Tv = t_avg * (1 + 0.61 * q)
## p = e ^ (-(((z_2 - z_1) * g)/ (Rd*Tv) - ln(p1)))
hg = h * g
RdTv = Rd * Tv
p = np.exp(-((np.divide(hg, RdTv.air_temp_min)) - math.log(p1)))
## RH = 0.263*p*q[exp(17.67*T - T0)/(T - 29.65)]^-1
RH = 0.263 * p * q * ((np.exp(
(17.67 * (t_avg - T0)) / (t_avg - 29.65)))**-1)
return (RH)
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() ## function to be used to find center of mass def com_array(arr):
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)
################################################ 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