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validate.py
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# Define where the script is running
host = 'jasmin'
# Import packages
import iris
import iris.plot as iplt
import sys
import numpy as np
import pandas as pd
import os
import fnmatch
import scipy
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import matplotlib.dates as mdates
from matplotlib import rcParams
from matplotlib.lines import Line2D
if host == 'jasmin':
sys.path.append('/gws/nopw/j04/bas_climate/users/ellgil82/scripts/Tools/')
elif host == 'bsl':
sys.path.append('/users/ellgil82/scripts/Tools/')
from tools import compose_date, compose_time, find_gridbox
from find_gridbox import find_gridbox
from rotate_data import rotate_data
from divg_temp_colourmap import shiftedColorMap
import time
from sklearn.metrics import mean_squared_error
import datetime
import metpy
import metpy.calc
#from cftime import datetime
from datetime import datetime, time
# Set up filepath
if host == 'jasmin':
filepath = '/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/output/alloutput/'
elif host == 'bsl':
filepath = '/data/mac/ellgil82/hindcast/output/'
## Load data
def load_vars(year, mn):
if mn == 'yes':
Tair = iris.load_cube( filepath + year+'_Tair_1p5m_daymn.nc', 'air_temperature')
Ts = iris.load_cube( filepath + year+'_Ts_daymn.nc', 'surface_temperature')
MSLP = iris.load_cube( filepath + year+'_MSLP_daymn.nc', 'air_pressure_at_sea_level')
sfc_P = iris.load_cube(filepath + year + '_sfc_P_daymn.nc', 'surface_air_pressure')
FF_10m = iris.load_cube( filepath +year+'_FF_10m_daymn.nc', 'wind_speed')
RH = iris.load_cube(filepath + year + '_RH_1p5m_daymn.nc', 'relative_humidity')
u = iris.load_cube(filepath + year + '_u_10m_daymn.nc', 'x wind component (with respect to grid)')
v = iris.load_cube(filepath + year + '_v_10m_daymn.nc', 'y wind component (with respect to grid)')
LWnet = iris.load_cube( filepath +year+'_surface_LW_net_daymn.nc', 'surface_net_downward_longwave_flux')
SWnet = iris.load_cube(filepath + year + '_surface_SW_net_daymn.nc','Net short wave radiation flux')
LWdown = iris.load_cube( filepath +year+'_surface_LW_down_daymn.nc', 'IR down')
SWdown = iris.load_cube( filepath +year+'_surface_SW_down_daymn.nc', 'surface_downwelling_shortwave_flux_in_air')
HL = iris.load_cube( filepath +year+'_latent_heat_daymn.nc', 'Latent heat flux')
HS = iris.load_cube( filepath +year+'_sensible_heat_daymn.nc', 'surface_upward_sensible_heat_flux')
#Etot = iris.load_cube(filepath + year + '_surface_E_tot_daymn.nc')
melt = iris.load_cube( filepath +year+'_land_snow_melt_flux_daymn.nc', 'Snow melt heating flux')
elif mn == 'no':
Tair = iris.load_cube( filepath +year+'_Tair_1p5m.nc', 'air_temperature')
Ts = iris.load_cube( filepath +year+'_Ts.nc', 'surface_temperature')
MSLP = iris.load_cube( filepath +year+'_MSLP.nc', 'air_pressure_at_sea_level')
sfc_P = iris.load_cube(filepath + year + '_sfc_P.nc', 'surface_air_pressure')
FF_10m = iris.load_cube( filepath +year+'_FF_10m.nc', 'wind_speed')
RH = iris.load_cube(filepath + year + '_RH_1p5m.nc', 'relative_humidity')
u = iris.load_cube(filepath + year + '_u_10m.nc', 'x wind component (with respect to grid)')
v = iris.load_cube(filepath + year + '_v_10m.nc', 'y wind component (with respect to grid)')
LWnet = iris.load_cube( filepath+year+'_surface_LW_net.nc', 'surface_net_downward_longwave_flux')
SWnet = iris.load_cube(filepath + year + '_surface_SW_net.nc','Net short wave radiation flux')
LWdown = iris.load_cube( filepath +year+'_surface_LW_down.nc', 'IR down')
SWdown = iris.load_cube( filepath +year+'_surface_SW_down.nc', 'surface_downwelling_shortwave_flux_in_air')
HL = iris.load_cube( filepath +year+'_latent_heat.nc', 'Latent heat flux')
HS = iris.load_cube( filepath +year+'_sensible_heat.nc', 'surface_upward_sensible_heat_flux')
#Etot = iris.load_cube(filepath + year + '_surface_E_tot.nc')
melt = iris.load_cube(filepath + year + '_land_snow_melt_flux.nc', 'Snow melt heating flux')
Tair.convert_units('celsius')
Ts.convert_units('celsius')
MSLP.convert_units('hPa')
sfc_P.convert_units('hPa')
FF_10m = FF_10m[:,:,1:,:]
v = v[:,:,1:,:]
# Flip direction of turbulent fluxes to match convention (positive towards surface)
HS = iris.analysis.maths.multiply(HS, -1.)
HL = iris.analysis.maths.multiply(HL, -1.)
var_list = [Tair, Ts, MSLP, sfc_P, FF_10m, RH, u, v, melt, LWnet, SWnet, LWdown, SWdown, HL, HS]
for i in var_list:
real_lon, real_lat = rotate_data(i, 2, 3)
WD = metpy.calc.wind_direction(u = u.data, v = v.data)
WD = iris.cube.Cube(data = WD, standard_name='wind_from_direction')
#Etot = LWnet.data + SWnet.data + HL.data + HS.data
#Etot = iris.cube.Cube(Etot)
SWup = iris.cube.Cube(data = SWnet.data - SWdown.data)
LWup = iris.cube.Cube(data=LWnet.data - LWdown.data)
vars_yr = {'Tair': Tair[:,0,:,:], 'Ts': Ts[:,0,:,:], 'MSLP': MSLP[:,0,:,:], 'sfc_P': sfc_P[:,0,:,:], 'FF_10m': FF_10m[:,0,:,:],
'RH': RH[:,0,:,:], 'WD': WD[:,0,:,:], 'u': u[:,0,:,:], 'v': v[:,0,:,:], 'LWnet': LWnet[:,0,:,:], 'SWnet': SWnet[:,0,:,:], 'SWdown': SWdown[:,0,:,:],
'LWdown': LWdown[:,0,:,:], 'HL': HL[:,0,:,:], 'HS': HS[:,0,:,:], 'Emelt': melt[:,0,:,:], 'SWup': SWup[:,0,:,:], 'LWup': LWup[:,0,:,:],
'lon': real_lon, 'lat': real_lat, 'year': year}
return vars_yr
#full_srs = load_vars('2016', mn = 'no')
full_srs = load_vars('1998-2017', mn = 'yes')
full_srs['Etot'] = iris.cube.Cube(data = (full_srs['LWnet'].data + full_srs['SWnet'].data + full_srs['HL'].data + full_srs['HS'].data))
#mn_srs = load_vars('1998-2017', mn = 'yes')
try:
LSM = iris.load_cube(filepath+'new_mask.nc')
orog = iris.load_cube(filepath+'orog.nc')
orog = orog[0,0,:,:]
LSM = LSM[0,0,:,:]
except iris.exceptions.ConstraintMismatchError:
print('Files not found')
def load_AWS(station, year):
## --------------------------------------------- SET UP VARIABLES ------------------------------------------------##
## Load data from AWS 14 and AWS 15 for January 2011
print('\nDayum grrrl, you got a sweet AWS...')
os.chdir(filepath)
for file in os.listdir(filepath):
if fnmatch.fnmatch(file, '%(station)s*' % locals()):
AWS_srs = pd.read_csv(str(file), na_values = -9999, header = 0)
# Calculate date, given list of years and day of year
date_list = compose_date(AWS_srs['year'], days=AWS_srs['day'])
AWS_srs['Date'] = date_list
# Set date as index
AWS_srs.index = AWS_srs['Date']
# Calculate actual time from decimal DOY (seriously, what even IS that format?)
AWS_srs['time'] = 24*(AWS_srs['Time'] - AWS_srs['day'])
case = AWS_srs.loc[year+'-01-01':year +'-12-31'] #'2015-01-01':'2015-12-31'
print '\nconverting times...'
# Convert times so that they can be plotted
time_list = []
for i in case['time']:
hrs = int(i) # will now be 1 (hour)
mins = int((i-hrs)*60) # will now be 4 minutes
secs = int(0 - hrs*60*60 + mins*60) # will now be 30
j = time(hour = hrs, minute=mins)
time_list.append(j)
case['Time'] = time_list
case['datetime'] = case.apply(lambda r : pd.datetime.combine(r['Date'],r['Time']),1)
case['E'] = case['LWnet_corr'].values + case['SWnet_corr'].values + case['Hlat'].values + case['Hsen'].values - case['Gs'].values
case['WD'][case['WD'] < 0.] = np.nan
case['FF_10m'][case['FF_10m'] < 0.] = np.nan
case['WD'] = case['WD'].interpolate() # interpolate missing values
case['FF_10m'] = case['FF_10m'].interpolate()
daily_Tair = case.resample('D')['Tair_2m']
case['Tair_min'] = daily_Tair.transform('min')
case['Tair_max'] = daily_Tair.transform('max')
u, v = metpy.calc.wind_components(case['FF_10m'], case['WD'])
case['u'] = u
case['v'] = v
try:
case['Tsobs'][case['Tsobs'] > -0.025] = 0.
daily_Ts = case.resample('D')['Tsobs']
case['Ts_min'] = daily_Ts.transform('min')
case['Ts_max'] = daily_Ts.transform('max')
except KeyError:
print('Ts not available at AWS 15')
if station == 'AWS18_SEB_2014-2017_norp.csv':
AWS_srs = AWS_srs[::2]
# Calculate months
months = [g for n, g in case.groupby(pd.TimeGrouper('M'))]
DJF = pd.concat((months[11], months[0], months[1]), axis=0)
MAM = pd.concat((months[2], months[3], months[4]), axis=0)
JJA = pd.concat((months[5], months[6], months[7]), axis=0)
SON = pd.concat((months[8], months[9], months[10]), axis=0)
return case, DJF, MAM, JJA, SON
#ANN_18, DJF, MAM, JJA, SON = load_AWS(station = 'AWS18_SEB_2014-2017_norp.csv', year = '2016')
def load_all_AWS(station, daily):
## --------------------------------------------- SET UP VARIABLES ------------------------------------------------##
## Load data from AWS 14 and AWS 15 for January 2011
print('\nDayum grrrl, you got a sweet AWS...')
if host == 'jasmin':
os.chdir(filepath)
for file in os.listdir(filepath):
if fnmatch.fnmatch(file, '%(station)s*' % locals()):
AWS_srs = pd.read_csv(str(file), na_values=-9999, header=0)
elif host == 'bsl':
os.chdir('/data/clivarm/wip/ellgil82/AWS/')
for file in os.listdir('/data/clivarm/wip/ellgil82/AWS/'):
if fnmatch.fnmatch(file, '%(station)s*' % locals()):
AWS_srs = pd.read_csv(str(file), na_values = -9999, header = 0)
# Calculate date, given list of years and day of year
date_list = compose_date(AWS_srs['year'], days=AWS_srs['day'])
AWS_srs['Date'] = date_list
# Set date as index
AWS_srs.index = AWS_srs['Date']
# Calculate actual time from decimal DOY (seriously, what even IS that format?)
try:
AWS_srs['time'] = 24.*(AWS_srs['Time'] - AWS_srs['day'])
time_list = []
for i in AWS_srs['time']:
hrs = int(i) # will now be 1 (hour)
mins = int((i-hrs)*60) # will now be 4 minutes
secs = int(0 - hrs*60*60 + mins*60) # will now be 30
j = time(hour = hrs, minute=mins)
time_list.append(j)
AWS_srs['Time'] = time_list
except TypeError:
print('Got time already m9')
AWS_srs['Time'] = pd.to_datetime(AWS_srs['Time'], format='%H:%M:%S').dt.time
print('\nconverting times...')
# Convert times so that they can be plotted
AWS_srs['datetime'] = AWS_srs.apply(lambda r : pd.datetime.combine(r['Date'],r['Time']),1)
try:
AWS_srs['E'] = AWS_srs['LWnet_corr'].values + AWS_srs['SWnet_corr'].values + AWS_srs['Hlat'].values + AWS_srs['Hsen'].values - AWS_srs['Gs'].values
except:
print('No full SEB \'ere pal...')
AWS_srs['WD'][AWS_srs['WD'] < 0.] = np.nan
AWS_srs['FF_10m'][AWS_srs['FF_10m'] < 0.] = np.nan
#AWS_srs['WD'] = AWS_srs['WD'].interpolate() # interpolate missing values
#AWS_srs['FF_10m'] = AWS_srs['FF_10m'].interpolate()
AWS_srs['WD'][AWS_srs['WD'] == 0.] = np.nan
u, v = metpy.calc.wind_components(AWS_srs['FF_10m'], AWS_srs['WD'])
AWS_srs['u'] = u
AWS_srs['v'] = v
if station == 'AWS18_SEB_2014-2017_norp.csv':
AWS_srs = AWS_srs[::2]
if station == 'AWS15_hourly_2009-2014.csv':
AWS_srs['P'][ AWS_srs['P'] < 800.] = np.nan
for j in ['SWin', 'SWout', 'LWin', 'LWout']:
AWS_srs[j][AWS_srs[j] < -999.] = np.nan
#AWS_srs[j].interpolate(method = 'linear', limit_direction = 'both') # linearly interpolate missing values
AWS_srs['SWnet'] = AWS_srs['SWin'] - AWS_srs['SWout']
AWS_srs['LWnet'] = AWS_srs['LWin'] - AWS_srs['LWout']
else:
AWS_srs['Tsobs'][AWS_srs['Tsobs'] > -0.025] = 0.
# Linearly interpolate missing values
#AWS_srs.interpolate('linear', limit_direction = 'both')
# Calculate daily means
if daily == 'yes':
AWS_srs = AWS_srs.groupby(AWS_srs.index).mean()
if station == 'AWS14_SEB_2009-2017_norp.csv':
AWS_srs = AWS_srs[1:]
daily_Tair = AWS_srs.resample('D')['Tair_2m']
AWS_srs['Tair_min'] = daily_Tair.transform('min')
AWS_srs['Tair_max'] = daily_Tair.transform('max')
try:
daily_Ts = AWS_srs.resample('D')['Tsobs']
AWS_srs['Ts_min'] = daily_Ts.transform('min')
AWS_srs['Ts_max'] = daily_Ts.transform('max')
except KeyError:
print('Ts not available at AWS 15')
## Calculate months
#months = [g for n, g in AWS_srs.groupby(pd.TimeGrouper('M'))]
#DJF = pd.concat((months[11], months[0], months[1]))
#MAM = pd.concat((months[2], months[3], months[4]))
#JJA = pd.concat((months[5], months[6], months[7]))
#SON = pd.concat((months[8], months[9], months[10]))
if host == 'jasmin':
os.chdir('/gws/nopw/j04/bas_climate/users/ellgil82/hindcast/output/alloutput/')
elif host == 'bsl':
os.chdir('/data/mac/ellgil82/hindcast/output/')
return AWS_srs #DJF, MAM, JJA, SON
print('\nLoading in AWS data\n')
ANN_14 = load_all_AWS('AWS14_SEB_2009-2017_norp.csv', daily = 'yes') # , DJF_14, MAM_14, JJA_14, SON_14
ANN_15 = load_all_AWS('AWS15_hourly_2009-2014.csv', daily = 'yes') # , DJF_15, MAM_15, JJA_15, SON_15
ANN_17 = load_all_AWS('AWS17_SEB_2011-2015_norp.csv', daily = 'yes') # , DJF_17, MAM_17, JJA_17, SON_17
ANN_18 = load_all_AWS('AWS18_SEB_2014-2017_norp.csv', daily = 'yes') # , DJF_18, MAM_18, JJA_18, SON_18
def make_model_timesrs(model_var, freq):
try:
Timesrs = model_var['Emelt'].coord('t').points
except:
Timesrs = model_var['Emelt'].coord('time').points
Timesrs = Timesrs + 0.5 # account for offset from initialisation time (t-12 hr), then + 1 to map index day[0] onto day 1.
df = pd.DataFrame()
if model_var['year'] == '1998-2017':
df['datetime'] = pd.date_range(datetime(1998,1,1,0,0,0),datetime(2017,12,31,23,59,59), freq = freq)
else:
yrs = np.repeat(np.int(model_var['year']), len(Timesrs))
date_list = compose_date(yrs, days=Timesrs)
time_list = [time(0,0,0), time(3,0,0), time(6,0,0), time(9,0,0), time(12,0,0), time(15,0,0), time(18,0,0), time(21,0,0)]
time_list = np.tile(np.asarray(time_list), len(Timesrs)/8)
df['times'] = pd.Series(time_list)
df['dates'] = pd.Series(date_list)
df['datetime'] = df.apply(lambda r : pd.datetime.combine(r['dates'],r['times']),1)
model_var['Timesrs'] = df['datetime']
def trim_model(model_var, AWS_var, daily):
#load model vars to match AWS
if daily == 'yes':
freq = 'D'
else:
freq = '3H'
make_model_timesrs(model_var, freq)
Timesrs = model_var['Timesrs']
# find where times match
if daily == 'yes':
start = np.where(Timesrs == pd.to_datetime(AWS_var.index)[0])
end = np.where(Timesrs == pd.to_datetime(AWS_var.index)[-1])
else:
start = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'])[0])
if start[0] > 0:
start = start
else:
start = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'])[1])
if start[0] > 0:
start = start
end = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'])[-1])
if end[0] > 0:
end = end
else:
end = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'])[-2])
if end[0] > 0:
end = end
else:
end = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'])[-3])
if end[0] > 0:
end = end
else:
end = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'][-1]).replace(minute = AWS_var['datetime'][-1].minute +1))
if end[0] > 0:
end = end
else:
end = np.where(Timesrs == pd.to_datetime(AWS_var['datetime'][-2]).replace(minute=AWS_var['datetime'][-2].minute + 1))
if end[0] > 0:
end = end
new_vars = {}
new_vars['year'] = '1998-2017'
for j in ['Tair','Ts', 'MSLP', 'sfc_P', 'FF_10m', 'u', 'v', 'RH', 'WD', 'LWnet', 'SWnet', 'SWdown', 'LWdown', 'LWup', 'SWup', 'HL', 'HS', 'Etot', 'Emelt', 'Timesrs']:
new_vars[j] = model_var[j][start[0][0]:end[0][0]]
new_vars['start'] = start[0][0]
new_vars['end'] = end[0][0]
return new_vars
srs_17_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_17, daily = 'yes')
srs_18_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_18, daily = 'yes')
srs_14_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_14, daily = 'yes')
srs_15_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_15, daily = 'yes')
lon_index14, lat_index14, = find_gridbox(-67.01, -61.03, full_srs['lat'], full_srs['lon'])
lon_index15, lat_index15, = find_gridbox(-67.34, -62.09, full_srs['lat'], full_srs['lon'])
lon_index17, lat_index17, = find_gridbox(-65.93, -61.85, full_srs['lat'], full_srs['lon'])
lon_index18, lat_index18, = find_gridbox(-66.48, -63.37, full_srs['lat'], full_srs['lon'])
lat_dict = {'AWS14': lat_index14,
'AWS15': lat_index15,
'AWS17': lat_index17,
'AWS18': lat_index18}
lon_dict = {'AWS14': lon_index14,
'AWS15': lon_index15,
'AWS17': lon_index17,
'AWS18': lon_index18}
station_dict = {'AWS14_SEB_2009-2017_norp.csv': 'AWS14',
'AWS15_hourly_2009-2014.csv': 'AWS15',
'AWS17_SEB_2011-2015_norp.csv': 'AWS17',
'AWS18_SEB_2014-2017_norp.csv': 'AWS18'}
print('\nTrimming model data to match observation period\n')
#srs_17_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_17, daily = 'no')
#srs_18_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_18, daily = 'no')
#srs_14_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_14, daily = 'no')
#srs_15_trimmed = trim_model(model_var = full_srs, AWS_var = ANN_15, daily = 'no')
# Calculate daily mins and maxs for each AWS
def calc_minmax(station, trimmed_srs, daily):
df = pd.DataFrame()
df['mod_T'] = np.mean(trimmed_srs['Tair'].data[:,lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1, lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2))
Timesrs = pd.date_range(datetime(1998,1,1,0,0,0),datetime(2017,12,31,23,59,59), freq = '3H')
#Timesrs = pd.date_range(datetime(2016,1,1,0,0,0),datetime(2017,1,1,0,0,0), freq = '3H')
Timesrs = Timesrs[trimmed_srs['start']:trimmed_srs['end']]
df.index = Timesrs
daily_T = df.resample('D')['mod_T']
Tmax = daily_T.transform('max')
Tmin = daily_T.transform('min')
trimmed_srs['Tmin'] = Tmin
trimmed_srs['Tmax'] = Tmax
return Tmax, Tmin
print('\nCalculating mins/maxes\n')
Tmax14, Tmin14 = calc_minmax('AWS14_SEB_2009-2017_norp.csv', srs_14_trimmed, daily = 'yes')
Tmax15, Tmin15 = calc_minmax('AWS15_hourly_2009-2014.csv', srs_15_trimmed, daily = 'yes')
Tmax17, Tmin17 = calc_minmax('AWS17_SEB_2011-2015_norp.csv', srs_17_trimmed, daily = 'yes')
Tmax18, Tmin18 = calc_minmax('AWS18_SEB_2014-2017_norp.csv', srs_18_trimmed, daily = 'yes')
mins = {'AWS14': Tmin14,
'AWS15': Tmin15,
'AWS17': Tmin17,
'AWS18': Tmin18}
maxes = {'AWS14': Tmax14,
'AWS15': Tmax15,
'AWS17': Tmax17,
'AWS18': Tmax18}#
def seas_mean(year_list, location):
for each_year in year_list:
seas_means = pd.DataFrame(index =['DJF', 'MAM', 'JJA', 'SON', 'ANN'])
seas_vals = pd.DataFrame(index =['DJF', 'MAM', 'JJA', 'SON', 'ANN'])
for each_var in file_dict.keys():
ANN = iris.load_cube(filepath + each_year + file_dict[each_var], each_var)
DJF = iris.load_cube(filepath + each_year + '/DJF_' + each_year + file_dict[each_var], each_var)
MAM = iris.load_cube(filepath + each_year + '/MAM_' + each_year + file_dict[each_var], each_var)
JJA = iris.load_cube(filepath + each_year + '/JJA_' + each_year + file_dict[each_var], each_var)
SON = iris.load_cube(filepath + each_year + '/SON_' + each_year + file_dict[each_var], each_var)
# Conversions
if each_var == 'surface_air_pressure' or each_var == 'air_pressure_at_sea_level':
for each_seas in [DJF, MAM, JJA, SON, ANN]:
each_seas.convert_units('hPa')
elif each_var == 'surface_temperature' or each_var == 'air_temperature':
for each_seas in [DJF, MAM, JJA, SON, ANN]:
each_seas.convert_units('celsius')
# calculate mean at specified location
ANN = ANN[:, 0, lat_dict[location], lon_dict[location]].data
DJF = DJF[:, 0, lat_dict[location], lon_dict[location]].data
MAM = MAM[:, 0, lat_dict[location], lon_dict[location]].data
JJA = JJA[:, 0, lat_dict[location], lon_dict[location]].data
SON = SON[:, 0, lat_dict[location], lon_dict[location]].data
seas_vals[each_var] = [DJF, MAM, JJA, SON, ANN]
seas_means[each_var] = [np.mean(DJF), np.mean(MAM), np.mean(JJA), np.mean(SON), np.mean(ANN) ]
wd_djf = metpy.calc.wind_direction(u = seas_vals['x wind component (with respect to grid)']['DJF'], v = seas_vals['y wind component (with respect to grid)']['DJF'])
wd_mam = metpy.calc.wind_direction(u = seas_vals['x wind component (with respect to grid)']['MAM'], v = seas_vals['y wind component (with respect to grid)']['MAM'])
wd_jja = metpy.calc.wind_direction(u = seas_vals['x wind component (with respect to grid)']['JJA'], v = seas_vals['y wind component (with respect to grid)']['JJA'])
wd_son = metpy.calc.wind_direction(u = seas_vals['x wind component (with respect to grid)']['SON'], v = seas_vals['y wind component (with respect to grid)']['SON'])
wd_ann = metpy.calc.wind_direction(u=seas_vals['x wind component (with respect to grid)']['ANN'], v=seas_vals['y wind component (with respect to grid)']['ANN'])
seas_vals['WD'] = [wd_djf.magnitude, wd_mam.magnitude, wd_jja.magnitude, wd_son.magnitude, wd_ann.magnitude]
seas_means['WD'] = metpy.calc.wind_direction(u = seas_means['x wind component (with respect to grid)'].values, v = seas_means['y wind component (with respect to grid)'].values)
seas_means = seas_means.transpose()
print(seas_means)
seas_means.to_csv('/data/mac/ellgil82/hindcast/output/'+ each_year +'_seas_means.csv')
#save indivdual dataframes for each year
# calculate mean for whole period
#DJF_means[each_var] = DJF_means[each_var] / len(year_list)
return seas_means, seas_vals
def calc_bias(trimmed_vars, AWS_total, station, daily, foehn):
# Calculate bias of time series
# Forecast error
vars_yr = trimmed_vars
if foehn == 'yes':
foehn_df = pd.read_csv(filepath + 'daily_foehn_frequency_all_stations.csv') # turn this into AWS 14/15/18 average, then diagnose when foehn is shown at one or more
foehn_subset = {}
foehn_df = foehn_df.iloc[vars_yr['start']:vars_yr['end']]
for v in ['WD', 'HS', 'Tair', 'Ts', 'LWdown', 'HL', 'Emelt', 'SWdown', 'LWup', 'SWnet', 'SWup', 'RH',
'FF_10m', 'sfc_P', 'MSLP', 'Etot', 'LWnet', 'u', 'v']:
sta_ts = np.mean(vars_yr[v][:, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1].data,
axis=(1, 2)) # calculate mean of variable at the correct station
sta_ts[foehn_df[station_dict[station]] == 0] = np.nan # mask values where foehn conditions at that station are not simulated
foehn_subset[v] = iris.cube.Cube(data=sta_ts)
foehn_subset['Tmax'] = vars_yr['Tmax']
foehn_subset['Tmin'] = vars_yr['Tmin']
AWS_masked = AWS_total[:sta_ts.shape[0]].copy()
AWS_masked.values[foehn_df[station_dict[station]] == 0] = np.nan
AWS_total = AWS_masked
vars_yr = foehn_subset
if daily == 'yes':
AWS_var = AWS_total.groupby(AWS_total.index).mean()
elif daily == 'no':
AWS_var = AWS_total[::3]
length = min(trimmed_vars['Tair'].shape[0], AWS_var['Tair_2m'].shape[0])
if station == 'AWS15_hourly_2009-2014.csv':
surf_met_obs = [AWS_var['Tair_2m'][:length], AWS_var['Tair_max'][:length], AWS_var['Tair_min'][:length], AWS_var['RH'][:length],
AWS_var['FF_10m'][:length], AWS_var['P'][:length], AWS_var['u'][:length], AWS_var['v'][:length],
AWS_var['SWin'][:length], AWS_var['LWin'][:length], AWS_var['SWnet'][:length],
AWS_var['LWnet'][:length]]
surf_mod = [np.nanmean(vars_yr['Tair'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
mins[station_dict[station]],
maxes[station_dict[station]],
np.nanmean(vars_yr['RH'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['FF_10m'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['sfc_P'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['u'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['v'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['SWdown'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['LWdown'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['SWnet'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['LWnet'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2))]
idx = ['Tair', 'Tmin', 'Tmax', 'RH', 'FF', 'P', 'u', 'v', 'Swdown', 'LWdown', 'SWnet', 'LWnet']
else:
surf_met_obs = [AWS_var['Tsobs'][:length], AWS_var['Tair_2m'][:length], AWS_var['Tair_min'], AWS_var['Tair_max'], AWS_var['RH'][:length], AWS_var['FF_10m'][:length], AWS_var['pres'][:length], AWS_var['u'][:length], AWS_var['v'][:length], AWS_var['SWin_corr'][:length],
AWS_var['LWin'][:length], AWS_var['SWnet_corr'][:length], AWS_var['LWnet_corr'][:length], AWS_var['Hsen'][:length], AWS_var['Hlat'][:length], AWS_var['E'][:length], AWS_var['melt_energy'][:length]]
surf_mod = [np.nanmean(vars_yr['Ts'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['Tair'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1],axis = (1,2)),
mins[station_dict[station]],
maxes[station_dict[station]],
np.nanmean(vars_yr['RH'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['FF_10m'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['sfc_P'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['u'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['v'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1, lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['SWdown'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['LWdown'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['SWnet'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
np.nanmean(vars_yr['LWnet'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)),
(np.nanmean(vars_yr['HS'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2))*-1.),
(np.nanmean(vars_yr['HL'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2))*-1.),
np.nanmean(vars_yr['Etot'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.nanmean(vars_yr['Emelt'].data[:length, lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2))]
idx = ['Ts', 'Tair', 'Tmin', 'Tmax', 'RH', 'FF', 'P', 'u', 'v', 'Swdown', 'LWdown', 'SWnet', 'LWnet', 'HS', 'HL', 'Etot', 'Emelt']
melt_nonzero = np.copy(surf_met_obs[-1])
melt_nonzero[melt_nonzero == 0.] = np.nan
AWS_nonz_mn = np.nanmean(melt_nonzero)
mod_melt_nonzero = np.copy(surf_mod[-2])
mod_melt_nonzero[mod_melt_nonzero == 0.] = np.nan
mod_nonz_mn = np.nanmean(mod_melt_nonzero)
calc_mod_melt_nonzero = np.copy(surf_mod[-1])
calc_mod_melt_nonzero[calc_mod_melt_nonzero == 0.] = np.nan
calc_mod_nonz_mn = np.nanmean(calc_mod_melt_nonzero)
nonz_bias = np.nanmean(mod_melt_nonzero - melt_nonzero)
mean_obs = []
mean_mod = []
bias = []
errors = []
r2s = []
rmses = []
for i in np.arange(len(surf_met_obs)):
b = surf_mod[i] - surf_met_obs[i]
errors.append(b)
mean_obs.append(np.mean(surf_met_obs[i]))
mean_mod.append(np.mean(surf_mod[i]))
bias.append(mean_mod[i] - mean_obs[i])
slope, intercept, r2, p, sterr = scipy.stats.linregress(surf_met_obs[i], surf_mod[i])
r2s.append(r2)
mse = mean_squared_error(y_true = surf_met_obs[i], y_pred = surf_mod[i])
rmse = np.sqrt(mse)
rmses.append(rmse)
df = pd.DataFrame(index = idx)
df['obs mean'] = pd.Series(mean_obs, index = idx)
df['mod mean'] = pd.Series(mean_mod, index = idx)
df['bias'] =pd.Series(bias, index=idx)
df['rmse'] = pd.Series(rmses, index = idx)
df['% RMSE'] = ( df['rmse']/df['obs mean'] ) * 100
df['correl'] = pd.Series(r2s, index = idx)
for i in range(len(surf_mod)):
slope, intercept, r2, p, sterr = scipy.stats.linregress(surf_met_obs[i], surf_mod[i])
print(idx[i])
print('\nr2 = %s\n' % r2)
if daily == 'yes':
df.to_csv(filepath + trimmed_vars['year'] + '_' + station_dict[station] + '_bias_RMSE_daily_mean.csv') # change to be station-specific
elif daily == 'no':
df.to_csv(filepath + trimmed_vars['year'] + '_' + station_dict[station] + '_bias_RMSE.csv')
print(df)
#calc_bias(srs_14_trimmed, ANN_14, station = 'AWS14_SEB_2009-2017_norp.csv', daily = 'yes', foehn='yes')
#ANN_15 = ANN_15.interpolate('linear', limit_direction = 'both')
#calc_bias(srs_15_trimmed, ANN_15, station = 'AWS15_hourly_2009-2014.csv', daily = 'yes')
#calc_bias(srs_17_trimmed, ANN_17, station = 'AWS17_SEB_2011-2015_norp.csv', daily = 'yes')
#ANN_18 = ANN_18.interpolate('linear', limit_direction = 'both')
#calc_bias(srs_18_trimmed, ANN_18, station = 'AWS18_SEB_2014-2017_norp.csv', daily = 'yes')
#inlet_df = pd.concat([AWS18_df, AWS17_df])
#inlet_df = inlet_df.groupby(inlet_df.index).mean()
#calc_bias(srs_14_trimmed, ANN_14, station = 'AWS14_SEB_2009-2017_norp.csv', daily = 'no')
#ANN_15 = ANN_15.interpolate('linear', limit_direction = 'both')
#calc_bias(srs_15_trimmed, ANN_15, station = 'AWS15_hourly_2009-2014.csv', daily = 'no')
#calc_bias(srs_17_trimmed, ANN_17, station = 'AWS17_SEB_2011-2015_norp.csv', daily = 'no')
#ANN_18 = ANN_18.interpolate('linear', limit_direction = 'both')
#calc_bias(srs_18_trimmed, ANN_18, station = 'AWS18_SEB_2014-2017_norp.csv', daily = 'no')
def calc_seas_bias(vars_yr, station, daily, foehn, load_again):
# reload AWS data
os.chdir(filepath)
AWS_var = load_all_AWS(station, daily)
length = min(vars_yr['Tair'].shape[0], AWS_var['Tair_2m'].shape[0])
idx = ['Ts', 'Tair', 'Tmin', 'Tmax', 'RH', 'FF', 'P', 'u', 'v', 'Swdown', 'SWup', 'SWnet', 'LWdown', 'LWup',
'LWnet', 'HS', 'HL', 'Etot', 'Emelt', 'datetime']
if load_again == 'yes':
if station == 'AWS15_hourly_2009-2014.csv':
surf_met_obs = [AWS_var['Tair_2m'][:length].values, AWS_var['Tair_min'][:length].values, AWS_var['Tair_max'][:length].values,
AWS_var['RH'][:length].values, AWS_var['FF_10m'][:length].values, AWS_var['P'][:length].values,
AWS_var['u'][:length].values, AWS_var['v'][:length].values, AWS_var['SWin'][:length].values,
AWS_var['SWout'][:length].values* -1., AWS_var['SWnet'][:length].values, AWS_var['LWin'][:length].values,
AWS_var['LWout'][:length].values* -1., AWS_var['LWnet'][:length].values, AWS_var.index.values]
surf_mod = [np.mean(vars_yr['Tair'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
mins[station_dict[station]],
maxes[station_dict[station]],
np.mean(vars_yr['RH'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(
vars_yr['FF_10m'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(
vars_yr['sfc_P'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['u'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['v'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['SWdown'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean( vars_yr['SWup'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['SWnet'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean( vars_yr['LWdown'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['LWup'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['LWnet'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
vars_yr['Timesrs']]
idx = ['Tair', 'Tmin', 'Tmax', 'RH', 'FF', 'P', 'u', 'v', 'Swdown', 'SWup', 'SWnet', 'LWdown', 'LWup', 'LWnet','datetime']
else:
surf_met_obs = [AWS_var['Tsobs'][:length].values, AWS_var['Tair_2m'][:length].values, AWS_var['Tair_min'][:length].values, AWS_var['Tair_max'][:length].values,
AWS_var['RH'][:length].values, AWS_var['FF_10m'][:length].values, AWS_var['pres'][:length].values, AWS_var['u'][:length].values,
AWS_var['v'][:length].values, AWS_var['SWin_corr'][:length].values, AWS_var['SWout'][:length].values* -1., AWS_var['SWnet_corr'][:length].values,
AWS_var['LWin'][:length].values, AWS_var['LWout_corr'][:length].values* -1., AWS_var['LWnet_corr'][:length].values,
AWS_var['Hsen'][:length].values, AWS_var['Hlat'][:length].values, AWS_var['E'][:length].values,
AWS_var['melt_energy'][:length].values, AWS_var.index.values]
surf_mod = [np.mean(vars_yr['Ts'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['Tair'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
mins[station_dict[station]],maxes[station_dict[station]],
np.mean(vars_yr['RH'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['FF_10m'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['sfc_P'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['u'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['v'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean( vars_yr['SWdown'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['SWup'].data[:length,
lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean(vars_yr['SWnet'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean( vars_yr['LWdown'].data[:length,lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean( vars_yr['LWup'].data[:length, (lat_dict[station_dict[station]] - 1):(lat_dict[station_dict[station]] + 1),
(lon_dict[station_dict[station]] - 1):(lon_dict[station_dict[station]] + 1)], axis=(1, 2)),
np.mean(vars_yr['LWnet'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
(np.mean(vars_yr['HS'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2))),
(np.mean(vars_yr['HL'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2))),
np.mean( vars_yr['Etot'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)),
np.mean( vars_yr['Emelt'].data[:length, lat_dict[station_dict[station]] - 1:lat_dict[station_dict[station]] + 1,
lon_dict[station_dict[station]] - 1:lon_dict[station_dict[station]] + 1], axis=(1, 2)), vars_yr['Timesrs']]
if foehn=='yes':
foehn_str = 'foehn'
foehn_df = pd.read_csv(filepath + 'daily_foehn_frequency_all_stations.csv') # turn this into AWS 14/15/18 average, then diagnose when foehn is shown at one or more
foehn_df = foehn_df.iloc[vars_yr['start']:vars_yr['end']]
foehn_df['AWS17'] = foehn_df['AWS18'].copy() #place-holder til I add 17 in
for f in range(len(surf_mod)-1):
try:
surf_met_obs[f][foehn_df[station_dict[station]]==0]=np.nan
surf_mod[f][foehn_df[station_dict[station]]==0]=np.nan # mask values where foehn conditions at that station are not simulated
except pd.core.indexing.IndexingError:
surf_met_obs[f][foehn_df[station_dict[station]] == 0] = np.nan
surf_mod[f].values[foehn_df[station_dict[station]] == 0] = np.nan
except:
print("Not THAT one, you silly goose")
else:
foehn_str = 'non-foehn'
obs_df = pd.DataFrame(surf_met_obs[:length], index = idx)
mod_df = pd.DataFrame(surf_mod, index = idx)
obs_df.to_csv(filepath + 'Surface_observed_time_series_' + foehn_str + '_' + station_dict[station] + '.csv')
mod_df.to_csv(filepath + 'Surface_modelled_time_series_' + foehn_str + '_' + station_dict[station] + '.csv')
else:
if foehn=='yes':
foehn_str = 'foehn'
else:
foehn_str = 'non-foehn'
obs_df = pd.read_csv(filepath + 'Surface_observed_time_series_' + foehn_str + '_' + station_dict[station] + '.csv',)
mod_df = pd.read_csv(filepath + 'Surface_modelled_time_series_' + foehn_str + '_' + station_dict[station] + '.csv')
mod_df.index=idx
obs_df.index=idx
mod_df=mod_df.transpose()
obs_df = obs_df.transpose()[:length]
obs_df['datetime'] = mod_df['datetime']
mod_DJF = mod_df.loc[
(mod_df['datetime'].dt.month == 1) | (mod_df['datetime'].dt.month == 2) | (mod_df['datetime'].dt.month == 12)]
obs_DJF = obs_df.loc[
(obs_df['datetime'].dt.month == 1) | (obs_df['datetime'].dt.month == 2) | (obs_df['datetime'].dt.month == 12)]
mod_MAM = mod_df.loc[
(mod_df['datetime'].dt.month == 3) | (mod_df['datetime'].dt.month == 4) | (mod_df['datetime'].dt.month == 5)]
obs_MAM = obs_df.loc[
(obs_df['datetime'].dt.month == 3) | (obs_df['datetime'].dt.month == 4) | (obs_df['datetime'].dt.month == 5)]
mod_JJA = mod_df.loc[
(mod_df['datetime'].dt.month == 6) | (mod_df['datetime'].dt.month == 7) | (mod_df['datetime'].dt.month == 8)]
obs_JJA = obs_df.loc[
(obs_df['datetime'].dt.month == 6) | (obs_df['datetime'].dt.month == 7) | (obs_df['datetime'].dt.month == 8)]
mod_SON = mod_df.loc[
(mod_df['datetime'].dt.month == 9) | (mod_df['datetime'].dt.month == 10) | (mod_df['datetime'].dt.month == 11)]
obs_SON = obs_df.loc[
(obs_df['datetime'].dt.month == 9) | (obs_df['datetime'].dt.month == 10) | (obs_df['datetime'].dt.month == 11)]
for obs_df, mod_df, seas_name in zip([obs_DJF, obs_MAM, obs_JJA, obs_SON], [mod_DJF, mod_MAM, mod_JJA, mod_SON], ['DJF', 'MAM', 'JJA', 'SON']):
a = obs_df[:length].drop('datetime', axis = 1)
b = mod_df[:length].drop('datetime', axis = 1)
bias = b - a
sterr = []
r = []
p = []
rmses = []
a = a.replace(pd.NaT, np.NaN)
b = b.replace(pd.NaT, np.NaN)
shp = min(a.shape[0], b.shape[0])
mask = ~np.isnan(a['u'][:shp].values.tolist()) # mask nans if using foehn mask
for vars in idx[:-1]:
slope, intercept, r_val, p_val, sterr_val = scipy.stats.linregress(a[vars].values[:shp][mask].tolist(), b[vars][:shp].values[mask].tolist()) #np.array(a[vars].values)[mask], np.array(b[vars].values)[mask]) #tolist()
r.append(r_val)
p.append(p_val)
sterr.append(sterr_val)
mse = mean_squared_error(y_true=a[vars].values[:shp][mask].tolist(), y_pred=b[vars].values[:shp][mask].tolist())
rmse = np.sqrt(mse)
rmses.append(rmse)
stats_df = pd.DataFrame()
stats_df['bias'] = pd.Series(bias.mean())
stats_df['r'] = pd.Series(r, index = idx[:-1])
stats_df['p'] = pd.Series(p, index = idx[:-1])
stats_df['sterr'] = pd.Series(sterr, index = idx[:-1])
stats_df['rmse'] = pd.Series(rmses, index = idx[:-1])
if daily == 'yes':
stats_df.to_csv(filepath + seas_name + '_' + station_dict[station] + '_' + foehn_str + '_seasonal_validation_daily.csv')
obs_df.to_csv(filepath + seas_name + '_' + station_dict[station] + '_' + foehn_str + '_observed_time_srs_daily.csv')
mod_df.to_csv(filepath + seas_name + '_' + station_dict[station] + '_' + foehn_str + '_modelled_time_srs_daily.csv')
else:
stats_df.to_csv(filepath + seas_name + '_' + station_dict[station] + '_' + foehn_str + '_' + '_seasonal_validation.csv')
obs_df.to_csv(filepath + seas_name + '_' + station_dict[station] + '_' + foehn_str + '_observed_time_srs.csv')
mod_df.to_csv(filepath + seas_name + '_' + station_dict[station] + '_' + foehn_str + '_modelled_time_srs.csv')
return obs_df, mod_df
obs_df14_foehn, mod_df14_foehn = calc_seas_bias(srs_14_trimmed, station='AWS14_SEB_2009-2017_norp.csv', daily = 'yes', foehn='yes', load_again = 'yes')
obs_df15_foehn, mod_df15_foehn = calc_seas_bias(srs_15_trimmed, station='AWS15_hourly_2009-2014.csv', daily = 'yes', foehn='yes', load_again = 'yes')
obs_df17_foehn, mod_df17_foehn = calc_seas_bias(srs_17_trimmed, station='AWS17_SEB_2011-2015_norp.csv', daily = 'yes', foehn='yes', load_again = 'yes')
obs_df18_foehn, mod_df18_foehn = calc_seas_bias(srs_18_trimmed, station='AWS18_SEB_2014-2017_norp.csv', daily = 'yes', foehn='yes', load_again = 'yes')
print('\nValidating seasonally\n')
Tmax14, Tmin14 = calc_minmax('AWS14_SEB_2009-2017_norp.csv', srs_14_trimmed, daily = 'yes')
Tmax15, Tmin15 = calc_minmax('AWS15_hourly_2009-2014.csv', srs_15_trimmed, daily='yes')
Tmax18, Tmin18 = calc_minmax('AWS18_SEB_2014-2017_norp.csv', srs_18_trimmed, daily='yes')
Tmax17, Tmin17 = calc_minmax('AWS17_SEB_2011-2015_norp.csv', srs_17_trimmed, daily='yes')
mins = {'AWS14': Tmin14,
'AWS15': Tmin15,
'AWS17': Tmin17,
'AWS18': Tmin18}
maxes = {'AWS14': Tmax14,
'AWS15': Tmax15,
'AWS17': Tmax17,
'AWS18': Tmax18}
obs_df14, mod_df14 = calc_seas_bias(srs_14_trimmed, station='AWS14_SEB_2009-2017_norp.csv', daily = 'yes', foehn='no', load_again='yes')
obs_df15, mod_df15 = calc_seas_bias(srs_15_trimmed, station='AWS15_hourly_2009-2014.csv', daily = 'yes', foehn='no', load_again = 'yes')
obs_df17, mod_df17 = calc_seas_bias(srs_17_trimmed, station='AWS17_SEB_2011-2015_norp.csv', daily = 'yes', foehn='no', load_again = 'yes')
obs_df18, mod_df18 = calc_seas_bias(srs_18_trimmed, station='AWS18_SEB_2014-2017_norp.csv', daily = 'yes', foehn='no', load_again = 'yes')
month_num_to_season = { 1:'DJF', 2:'DJF',
3:'MAM', 4:'MAM', 5:'MAM',
6:'JJA', 7:'JJA', 8:'JJA',
9:'SON', 10:'SON', 11:'SON',
12:'DJF'}
grouped = bias.groupby(lambda x: month_num_to_season.get(x.month))
def remove_diurnal_cyc(input_var):
if input_var.ndim >= 2:
data = input_var.data
nt, ny, nx = data.shape
data = data.reshape(nt, ny*nx)
tmax, ngrid = data.shape
diur = np.zeros((8, ngrid)) # _number of hours = 3 hourly output
for hr in np.arange(8):
idx = np.arange(hr, tmax, 8)
diur[hr, :] = data[idx].mean(axis=0)
day_cyc = np.reshape(diur, (8, ny, nx))
diur = np.tile(diur, [int(len(input_var.coord('t').points) / 8.), 1]) # 24 hours in 37 days
data = (data - diur).reshape(nt, ny, nx)
else:
data = input_var.values
diur = np.zeros((24))
for hr in np.arange(24):
idx = np.arange(hr, len(data), 24)
diur[hr] = data[idx].mean(axis=0)
day_cyc = np.reshape(diur, (24))
diur = np.tile(diur, [int(len(data)/24.), 1])
return data, day_cyc
percentiles = []
for each_var in [T_surf, T_air, RH, sp_srs]:
p95 = np.percentile(each_var, 95, axis = (1,2))
p5 = np.percentile(each_var, 5, axis = (1,2))
percentiles.append(p5)
percentiles.append(p95)
def calc_melt(AWS_vars, model_vars):
Lf = 334000 # J kg-1
rho_H2O = 999.7 # kg m-3
melt_m_per_3hr_obs = (AWS_vars['melt_energy'][::3] / (Lf * rho_H2O)) # in mm per hr min
melt_m_per_s_obs = melt_m_per_3hr_obs * (3600 * 3) # multiply by (60 seconds * 60 mins) to get flux per second
total_melt_mmwe = melt_m_per_s_obs * 1000
obs_total_melt_cmv = np.cumsum(total_melt_mmwe, axis=0)[-1]
melt_m_per_hr_mod = (model_vars['Emelt'] / (Lf * rho_H2O)) # in mm per 30 min
melt_m_per_s_mod = melt_m_per_hr_mod * (3600 * 3) # multiply by (60 seconds * 30 mins) to get flux per second
total_melt_mmwe = melt_m_per_s_mod * 1000
mod_total_melt_cmv = np.cumsum(total_melt_mmwe, axis=0)[-1]
print(obs_total_melt_cmv, mod_total_melt_cmv)
return obs_total_melt_cmv, mod_total_melt_cmv
#obs_total_melt_cmv, mod_total_melt_cmv = calc_melt(AWS_vars = AWS14_SEB, model_vars = vars_2011)
def calc_nonzero_melt(year, station, var_yr):
AWS_var, DJF, MAM, JJA, SON = load_AWS(year = year, station = station)
AWS_melt = AWS_var['melt_energy']
melt_nonzero = np.copy(AWS_melt)
melt_nonzero[melt_nonzero == 0.] = np.nan
AWS_nonz_mn = np.nanmean(melt_nonzero)
mod_melt_nonzero = np.copy(np.mean(var_yr['Emelt'].data[:,lat_dict[station_dict[station]]-1:lat_dict[station_dict[station]]+1, lon_dict[station_dict[station]]-1:lon_dict[station_dict[station]]+1], axis = (1,2)))
mod_melt_nonzero[mod_melt_nonzero == 0.] = np.nan
mod_nonz_mn = np.nanmean(mod_melt_nonzero)
nonz_bias = np.nanmean(mod_melt_nonzero - melt_nonzero[::3])
return AWS_nonz_mn, mod_nonz_mn, nonz_bias
def calc_melt_days(melt_var):
melt = np.copy(melt_var)
out = melt.reshape(-1, 8, melt.shape[1], melt.shape[2]).sum(1)
melt_days = np.count_nonzero(out, axis = 0)
return melt_days
#melt_days_calc = calc_melt_days(vars_2012['Emelt_calc'].data)
#melt_days = calc_melt_days(vars_2012['Emelt'].data)
#np.mean(melt_days_calc)
#np.mean(melt_days)
def calc_melt_duration(melt_var):
melt = np.copy(melt_var)
melt_periods = np.count_nonzero(melt, axis = 0)
melt_periods = melt_periods*3. # multiply by 3 to get number of hours per year (3-hourly data)
return melt_periods
#melt_dur_calc = calc_melt_duration(vars_2011['Emelt_calc'].data)
#melt_dur = calc_melt_duration(vars_2011['Emelt'].data)
#np.mean(melt_dur_calc)
#np.mean(melt_dur)
## ---------------------------------------- PLOTTING ROUTINES ------------------------------------------------- ##
## Set up plotting options
rcParams['font.family'] = 'sans-serif'
rcParams['font.sans-serif'] = ['Segoe UI', 'Helvetica', 'Liberation sans', 'Tahoma', 'DejaVu Sans', 'Verdana']
from windrose import WindroseAxes
import matplotlib.cm as cm
#AWS_dict = {AWS14_SEB: 'AWS14',
# AWS15: 'AWS15',
# AWS17: 'AWS17',
# AWS18_SEB: 'AWS18'}
#yr_dict = {vars_2014: '2014',
# vars_2012: '2012',
# vars_2016: '2016',
# vars_2017: '2017'}
def wind_rose(year, station):
vars_yr = load_vars(year, mn = 'no')
AWS_var = load_AWS(station, year)
fig = plt.figure(figsize = (16,8))
rect = [0.05, 0.1, 0.45, 0.6]
wa = WindroseAxes(fig, rect)
fig.add_axes(wa)
# define data limits
max_mod = max(np.mean(vars_yr['FF_10m'][:,lat_index14-1:lat_index14+1, lon_index14-1:lon_index14+1].data, axis = (1,2)))
max_obs = max(AWS_var['FF'])
wa.set_title('Observed', fontsize = 28, color = 'dimgrey', pad = 50)
wa.axes.spines['polar'].set_visible(False)
wa.tick_params(axis='both', which='both', labelsize=24, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
wa.bar(AWS_var['WD'], AWS_var['FF'], bins = np.arange(0, max(max_mod, max_obs),4), cmap = plt.get_cmap('viridis'), normed = True,opening=0.8, edgecolor='white')
wa.set_yticklabels([])
rect = [0.55, 0.1, 0.45, 0.6]
wa = WindroseAxes(fig, rect)
fig.add_axes(wa)
wa.set_title('Modelled', fontsize=28, color='dimgrey', pad = 50)
wa.bar(np.mean(vars_yr['WD'][:,lat_index14-1:lat_index14+1, lon_index14-1:lon_index14+1].data, axis = (1,2)),
np.mean(vars_yr['FF_10m'][:,lat_index14-1:lat_index14+1, lon_index14-1:lon_index14+1].data, axis = (1,2)),
bins = np.arange(0, max(max_mod, max_obs),4), cmap = plt.get_cmap('viridis'), normed = True, opening=0.8, edgecolor='white')
lgd = wa.set_legend( bbox_to_anchor=(-0.5, 0.9))
frame = lgd.get_frame()
frame.set_facecolor('white')
for ln in lgd.get_texts():
plt.setp(ln, color='dimgrey', fontsize = 18)
lgd.get_frame().set_linewidth(0.0)
wa.axes.spines['polar'].set_visible(False)
wa.set_yticklabels([])
wa.tick_params(axis='both', which='both', labelsize=24, tick1On=False, tick2On=False, labelcolor='dimgrey', pad=10)
plt.savefig('/users/ellgil82/figures/Hindcast/validation/wind_rose_' + station + '_' + year + '.png')
plt.savefig('/users/ellgil82/figures/Hindcast/validation/wind_rose_' + station + '_' + year + '.eps')
plt.show()
#wind_rose(year = '2011', station = 'AWS14')
def plot_diurnal(var, domain):
orog = iris.load_cube('OFCAP_orog.nc', 'surface_altitude')
lsm = iris.load_cube('OFCAP_lsm.nc', 'land_binary_mask')
orog = orog[0,0,:,:]
lsm = lsm[0,0,:,:]
# Load diurnal cycles in
if var == 'SEB':
diur_SWdown = iris.load_cube('diurnal_SWdown.nc', 'surface_downwelling_shortwave_flux_in_air')
diur_SWup = iris.load_cube('diurnal_SWup.nc', 'Net short wave radiation flux')
diur_SWnet = iris.load_cube('diurnal_SWnet.nc', 'Net short wave radiation flux')
diur_LWnet = iris.load_cube('diurnal_LWnet.nc', 'surface_net_downward_longwave_flux')
diur_LWdown = iris.load_cube('diurnal_LWdown.nc', 'IR down')
diur_LWup = iris.load_cube('diurnal_LWup.nc', 'surface_net_downward_longwave_flux')
diur_HL =iris.load_cube('diurnal_HL.nc', 'Latent heat flux')
diur_HS = iris.load_cube('diurnal_HS.nc', 'surface_upward_sensible_heat_flux')
vars_yr = {'SWdown': diur_SWdown, 'SWnet': diur_SWnet, 'SWup': diur_SWup, 'LWdown': diur_LWdown, 'LWnet': diur_LWnet, 'LWup': diur_LWup, 'HL': diur_HL, 'HS': diur_HS}
colour_dict = {'SWdown': '#6fb0d2', 'SWnet': '#6fb0d2', 'SWup': '#6fb0d2', 'LWdown': '#86ad63', 'LWnet': '#86ad63', 'LWup': '#86ad63','HL': '#33a02c', 'HS': '#1f78b4'}
UTC_3 = pd.DataFrame()
for x in vars_yr:
UTC_3[x] = np.concatenate((np.mean(vars_yr[x][5:, 0, 199:201, 199:201].data, axis=(1, 2)),
np.mean(vars_yr[x][:5, 0, 199:201, 199:201].data, axis=(1, 2))), axis=0)
elif var == 'met':
diur_Ts = iris.load_cube('diurnal_Ts.nc','surface_temperature')
diur_Tair = iris.load_cube('diurnal_Tair.nc', 'air_temperature')
for i in [diur_Tair, diur_Ts]:
i.convert_units('celsius')
diur_u = iris.load_cube('diurnal_u.nc', 'eastward_wind')
diur_v = iris.load_cube('diurnal_v.nc', 'northward_wind')
diur_v = diur_v[:,:,1::]
real_lon, real_lat = rotate_data(diur_v, 2, 3)
real_lon, real_lat = rotate_data(diur_u, 2, 3)
diur_q = iris.load_cube('diurnal_q.nc', 'specific_humidity')
vars_yr = {'Ts': diur_Ts, 'Tair': diur_Tair, 'u': diur_u, 'v': diur_v,'q': diur_q}
colour_dict = {'Ts': '#dd1c77', 'Tair': '#91003f', 'ff': '#238b45', 'q': '#2171b5'}
if domain == 'ice shelf' or domain == 'Larsen C':
# Create Larsen mask
Larsen_mask = np.zeros((400, 400))
lsm_subset = lsm.data[35:260, 90:230]
Larsen_mask[35:260, 90:230] = lsm_subset
Larsen_mask[orog.data > 25] = 0
Larsen_mask = np.logical_not(Larsen_mask)
UTC_3 = pd.DataFrame()
for x in vars_yr:
UTC_3[x] = np.ma.concatenate((np.ma.masked_array(vars_yr[x][5:, 0, :, :].data, mask=np.broadcast_to(Larsen_mask, vars_yr[x][5:, 0, :, :].shape)).mean(axis=(1, 2)),
np.ma.masked_array(vars_yr[x][:5, 0, :, :].data, mask=np.broadcast_to(Larsen_mask, vars_yr[x][:5, 0, :, :].shape)).mean(axis=(1, 2))), axis=0)
elif domain == 'AWS 14' or domain == 'AWS14':
UTC_3 = pd.DataFrame()
diur_ff = iris.cube.Cube(data = np.sqrt(vars_yr['v'].data**2)+(vars_yr['u'].data**2))
UTC_3['ff'] = np.concatenate((np.mean(diur_ff[5:, 0, 199:201, 199:201].data, axis=(1, 2)),
np.mean(diur_ff[:5, 0, 199:201, 199:201].data, axis=(1, 2))), axis=0)
for x in vars_yr:
UTC_3[x] = np.concatenate((np.mean(vars_yr[x][5:, 0, 199:201, 199:201], axis=(1, 2)),
np.mean(vars_yr[x][:5, 0, 199:201, 199:201], axis=(1, 2))), axis=0)
elif domain == 'AWS 15' or domain == 'AWS15':
UTC_3 = pd.DataFrame()
for x in vars_yr:
UTC_3[x] = np.concatenate((np.mean(vars_yr[x][5:, 0, 161:163, 182:184].data, axis=(1, 2)),
np.mean(vars_yr[x][:5, 0, 161:163, 182:184].data, axis=(1, 2))), axis=0)
diur_ff = iris.cube.Cube(data=np.sqrt(vars_yr['v'].data ** 2) + (vars_yr['u'].data ** 2))
UTC_3['ff'] = np.concatenate((np.mean(diur_ff[5:, 0, 161:163, 182:184].data, axis=(1, 2)),
np.mean(diur_ff[:5, 0, 161:163, 182:184].data, axis=(1, 2))), axis=0)
## Set up plotting options
if var == 'SEB':
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.spines['top'].set_visible(False)
plt.setp(ax.spines.values(), linewidth=3, color='dimgrey')