def harvest_and_save_blindern(from_string, to_string):
stnr_met_blind = 18700 # Blindern (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr_met_blind, 2, output='csv')
rr = gws.getMetData(stnr_met_blind, 'RR', from_date, to_date, 0, output='raw list')
rr = we.millimeter_from_meter(rr)
rr_test_message = we.test_for_missing_elements(rr, from_date, to_date)
tam = gws.getMetData(stnr_met_blind, 'TAM', from_date, to_date, 0, output='raw list')
tam_test_message = we.test_for_missing_elements(tam, from_date, to_date)
nnm = gws.getMetData(stnr_met_blind, 'NNM', from_date, to_date, 0, output='raw list')
nnm_test_message = we.test_for_missing_elements(nnm, from_date, to_date)
file_name = '{2}RR TAM NNM Blindern 18700 {0} to {1}.txt'.format(from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = ['RR [mm/day] on Blindern 18700 from eklima.met.no',
'TAM [C] daily avg on Blindern 18700 from eklima.met.no',
'NNM [%/100] daily avg on Blindern 18700 from eklima.met.no']
mfd.write_vardat2(file_name, [rr, tam, nnm], WSYS, OPER, DCHA)
return
def harvest_and_save_nordnesfjelet(from_string, to_string):
stnr = 91500 # Nordnesfjellet (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr, 2, output='csv')
qli = gws.getMetData(stnr, 'QLI', from_date, to_date, 2, output='raw list')
qli_test_message = we.test_for_missing_elements(qli, from_date, to_date, time_step=60*60)
ta = gws.getMetData(stnr, 'ta', from_date, to_date, 2, output='raw list')
ta_test_message = we.test_for_missing_elements(ta, from_date, to_date, time_step=3600)
rr_1 = gws.getMetData(stnr, 'rr_1', from_date, to_date, 2, output='raw list')
rr_1 = we.millimeter_from_meter(rr_1)
rr_test_message = we.test_for_missing_elements(rr_1, from_date, to_date, time_step=3600)
file_name = '{2}QLI TA RR Nordnesfjellet 91500 {0} to {1}.txt'.format(from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = ['QLI [Wh/m2] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'TA [C] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'RR_1 [mm/hr] on Nordnesfjellet 91500 from eklima.met.no']
mfd.write_vardat2(file_name, [qli, ta, rr_1], WSYS, OPER, DCHA)
return
def run_semsvann_eb(startDate, endDate):
# TODO: get coordinates from the ObsLocation in regObs
location_name = 'Semsvannet v/Lo 145 moh'
wsTemp = gws.getMetData(19710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(19710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(19710, 'RR', startDate, endDate, 0, 'list')
wsWind = gws.getMetData(18700, 'FFM', startDate, endDate, 0, 'list')
wsCC = gws.getMetData(18700, 'NNM', startDate, endDate, 0, 'list')
utm33_y = 6644410
utm33_x = 243940
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
wind = we.strip_metadata(wsWind)
cloud_cover = we.strip_metadata(wsCC)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate,
endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x,
utm33_y,
date,
temp,
prec,
prec_snow,
cloud_cover=cloud_cover,
wind=wind,
rel_hum=rel_hum,
pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Semsvann EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Semsvann MET with EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover,
energy_balance,
observed_ice,
date,
temp,
sno_tot,
plot_filename,
prec=prec,
wind=wind,
clouds=cloud_cover)
def run_otrovann_eb(startDate, endDate):
location_name = 'Otrøvatnet v/Nystuen 971 moh'
wsTemp = gws.getMetData(54710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(54710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(54710, 'RR', startDate, endDate, 0, 'list')
utm33_y = 6802070
utm33_x = 130513
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
cloud_cover = dp.clouds_from_precipitation(prec)
wind = [const.avg_wind_const] * len(date)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
# available_elements = gws.getElementsFromTimeserieTypeStation(54710, 0, 'csv')
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate,
endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x,
utm33_y,
date,
temp,
prec,
prec_snow,
cloud_cover,
wind,
rel_hum=rel_hum,
pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Ortovann MET EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Ortovann MET with EB {1}-{2}.png'.format(
se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover,
energy_balance,
observed_ice,
date,
temp,
sno_tot,
plot_filename,
prec=prec,
wind=wind,
clouds=cloud_cover)
def __test_clouds_from_short_wave():
date_list = [dt.date.today() - dt.timedelta(days=x) for x in range(0, 365)]
date_list = [
dt.datetime.combine(d, dt.datetime.min.time()) for d in date_list
]
date_list.reverse()
# test Nordnesfjellet i Troms
station_id = 91500
short_wave_id = 'QSI'
long_wave_id = 'QLI'
temperature_id = 'TA'
timeseries_type = 2
utm_e = 711075
utm_n = 7727719
short_wave = gws.getMetData(station_id, short_wave_id, date_list[0],
date_list[-1], timeseries_type)
long_wave = gws.getMetData(station_id, long_wave_id, date_list[0],
date_list[-1], timeseries_type)
temperature = gws.getMetData(station_id, temperature_id, date_list[0],
date_list[-1], timeseries_type)
short_wave = we.fix_data_quick(short_wave)
long_wave = we.fix_data_quick(long_wave)
temperature_daily = we.fix_data_quick(temperature)
short_wave_daily = we.make_daily_average(short_wave)
short_wave_daily = we.multiply_constant(
short_wave_daily,
24 * 3600 / 1000) # Wh/m2 * 3600 s/h * kJ/1000J (energy) over 24hrs
long_wave_daily = we.make_daily_average(long_wave)
long_wave_daily = we.multiply_constant(long_wave_daily, 24 * 3600 / 1000)
temperature_daily = we.make_daily_average(temperature)
Short_wave_list = we.strip_metadata(short_wave_daily)
I_clear_sky_list = [
irradiance_clear_sky(utm_e, utm_n, d) for d in date_list
]
Cloud_cover = clouds_from_short_wave(utm_e, utm_n, Short_wave_list,
date_list)
import matplotlib.pyplot as plt
plt.plot(date_list, Short_wave_list)
plt.plot(date_list, I_clear_sky_list)
plt.plot(date_list, Cloud_cover)
plt.ylim(0, 50000)
plt.show()
def harvest_and_save_blindern(from_string, to_string):
stnr_met_blind = 18700 # Blindern (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr_met_blind, 2, output='csv')
rr = gws.getMetData(stnr_met_blind,
'RR',
from_date,
to_date,
0,
output='raw list')
rr = we.millimeter_from_meter(rr)
rr_test_message = we.test_for_missing_elements(rr, from_date, to_date)
tam = gws.getMetData(stnr_met_blind,
'TAM',
from_date,
to_date,
0,
output='raw list')
tam_test_message = we.test_for_missing_elements(tam, from_date, to_date)
nnm = gws.getMetData(stnr_met_blind,
'NNM',
from_date,
to_date,
0,
output='raw list')
nnm_test_message = we.test_for_missing_elements(nnm, from_date, to_date)
file_name = '{2}RR TAM NNM Blindern 18700 {0} to {1}.txt'.format(
from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = [
'RR [mm/day] on Blindern 18700 from eklima.met.no',
'TAM [C] daily avg on Blindern 18700 from eklima.met.no',
'NNM [%/100] daily avg on Blindern 18700 from eklima.met.no'
]
mfd.write_vardat2(file_name, [rr, tam, nnm], WSYS, OPER, DCHA)
return
def harvest_and_save_nordnesfjelet(from_string, to_string):
stnr = 91500 # Nordnesfjellet (met)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
#elems_blind = gws.getElementsFromTimeserieTypeStation(stnr, 2, output='csv')
qli = gws.getMetData(stnr, 'QLI', from_date, to_date, 2, output='raw list')
qli_test_message = we.test_for_missing_elements(qli,
from_date,
to_date,
time_step=60 * 60)
ta = gws.getMetData(stnr, 'ta', from_date, to_date, 2, output='raw list')
ta_test_message = we.test_for_missing_elements(ta,
from_date,
to_date,
time_step=3600)
rr_1 = gws.getMetData(stnr,
'rr_1',
from_date,
to_date,
2,
output='raw list')
rr_1 = we.millimeter_from_meter(rr_1)
rr_test_message = we.test_for_missing_elements(rr_1,
from_date,
to_date,
time_step=3600)
file_name = '{2}QLI TA RR Nordnesfjellet 91500 {0} to {1}.txt'.format(
from_string, to_string, env.data_path)
WSYS = "github/Ice-modelling/rundataharvester.py"
OPER = "Ragnar Ekker"
DCHA = [
'QLI [Wh/m2] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'TA [C] avg pr hr on Nordnesfjellet 91500 from eklima.met.no',
'RR_1 [mm/hr] on Nordnesfjellet 91500 from eklima.met.no'
]
mfd.write_vardat2(file_name, [qli, ta, rr_1], WSYS, OPER, DCHA)
return
def __test_clouds_from_short_wave():
date_list = [dt.date.today() - dt.timedelta(days=x) for x in range(0, 365)]
date_list = [dt.datetime.combine(d, dt.datetime.min.time()) for d in date_list]
date_list.reverse()
# test Nordnesfjellet i Troms
station_id = 91500
short_wave_id = 'QSI'
long_wave_id = 'QLI'
temperature_id = 'TA'
timeseries_type = 2
utm_e = 711075
utm_n = 7727719
short_wave = gws.getMetData(station_id, short_wave_id, date_list[0], date_list[-1], timeseries_type)
long_wave = gws.getMetData(station_id, long_wave_id, date_list[0], date_list[-1], timeseries_type)
temperature = gws.getMetData(station_id, temperature_id, date_list[0], date_list[-1], timeseries_type)
short_wave = we.fix_data_quick(short_wave)
long_wave = we.fix_data_quick(long_wave)
temperature_daily = we.fix_data_quick(temperature)
short_wave_daily = we.make_daily_average(short_wave)
short_wave_daily = we.multiply_constant(short_wave_daily, 24 * 3600 / 1000) # Wh/m2 * 3600 s/h * kJ/1000J (energy) over 24hrs
long_wave_daily = we.make_daily_average(long_wave)
long_wave_daily = we.multiply_constant(long_wave_daily, 24 * 3600 / 1000)
temperature_daily = we.make_daily_average(temperature)
Short_wave_list = we.strip_metadata(short_wave_daily)
I_clear_sky_list = [irradiance_clear_sky(utm_e, utm_n, d) for d in date_list]
Cloud_cover = clouds_from_short_wave(utm_e, utm_n, Short_wave_list, date_list)
import matplotlib.pyplot as plt
plt.plot(date_list, Short_wave_list)
plt.plot(date_list, I_clear_sky_list)
plt.plot(date_list, Cloud_cover)
plt.ylim(0, 50000)
plt.show()
def run_semsvann_eb(startDate, endDate):
# TODO: get coordinates from the ObsLocation in regObs
location_name = 'Semsvannet v/Lo 145 moh'
wsTemp = gws.getMetData(19710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(19710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(19710, 'RR', startDate, endDate, 0, 'list')
wsWind = gws.getMetData(18700, 'FFM', startDate, endDate, 0, 'list')
wsCC = gws.getMetData(18700, 'NNM', startDate, endDate, 0, 'list')
utm33_y = 6644410
utm33_x = 243940
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
wind = we.strip_metadata(wsWind)
cloud_cover = we.strip_metadata(wsCC)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate, endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x, utm33_y, date,
temp, prec, prec_snow, cloud_cover=cloud_cover, wind=wind, rel_hum=rel_hum, pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Semsvann EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Semsvann MET with EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover, energy_balance, observed_ice, date, temp, sno_tot, plot_filename, prec=prec, wind=wind, clouds=cloud_cover)
def run_otrovann_eb(startDate, endDate):
location_name = 'Otrøvatnet v/Nystuen 971 moh'
wsTemp = gws.getMetData(54710, 'TAM', startDate, endDate, 0, 'list')
wsSno = gws.getMetData(54710, 'SA', startDate, endDate, 0, 'list')
wsPrec = gws.getMetData(54710, 'RR', startDate, endDate, 0, 'list')
utm33_y = 6802070
utm33_x = 130513
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSno)
prec_snow = dp.delta_snow_from_total_snow(sno_tot)
prec = we.strip_metadata(wsPrec)
cloud_cover = dp.clouds_from_precipitation(prec)
wind = [const.avg_wind_const] * len(date)
rel_hum = [const.rel_hum_air] * len(date)
pressure_atm = [const.pressure_atm] * len(date)
# available_elements = gws.getElementsFromTimeserieTypeStation(54710, 0, 'csv')
observed_ice = gro.get_all_season_ice_on_location(location_name, startDate, endDate)
ice_cover, energy_balance = it.calculate_ice_cover_eb(
utm33_x, utm33_y, date, temp, prec, prec_snow, cloud_cover, wind, rel_hum=rel_hum, pressure_atm=pressure_atm,
inn_column=copy.deepcopy(observed_ice[0]))
# Need datetime objects from now on
from_date = dt.datetime.strptime(startDate, "%Y-%m-%d")
to_date = dt.datetime.strptime(endDate, "%Y-%m-%d")
plot_filename = '{0}Ortovann MET EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
# pts.plot_ice_cover(ice_cover, observed_ice, date, temp, sno_tot, plot_filename)
plot_filename = '{0}Ortovann MET with EB {1}-{2}.png'.format(se.plot_folder, from_date.year, to_date.year)
pts.plot_ice_cover_eb(ice_cover, energy_balance, observed_ice, date, temp, sno_tot, plot_filename,
prec=prec, wind=wind, clouds=cloud_cover)
def run_mosselva(from_date,
to_date,
make_plots=True,
plot_folder=se.plot_folder,
forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Mosselva'
y = 6595744
x = 255853
altitude = 25
met_stnr = 17150 # Rygge målestasjon (met.no)
first_ice = ice.IceColumn(dt.datetime(int(from_date[0:4]), 12, 31), [])
first_ice.add_metadata('LocationName', location_name) # used when plotting
observed_ice = [first_ice]
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
gridTempNewElevation = we.adjust_temperature_to_new_altitude(
gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation,
get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice),
date,
temp,
sno,
cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
sno_tot, plot_path_and_filename)
def harvest_for_mylake_hakkloa(from_string, to_string):
"""Method gathers meteorological parameters needed to run myLake on Hakkloa and saves them to a
formatted file as needed to run myLake. For columns needed se MyLakeInput class.
:param from_string: {string} from date to acquire data
:param to_string: {string} to date to acquire data
:return:
HydraII parameters:
Wind 15
Temperature 17
Relative humidity 2
"""
utm33_x = 259942 # Hakloa (buoy)
utm33_y = 6671218 # Hakloa (buoy)
stnr_met_blind = 18700 # Blindern (met)
stnr_met_bjorn = 18500 # Bjørnholt (met)
stnr_nve = '6.24.4' # Hakloa (NVE)
hydraii_temperature = '{0}6.24.4.17.1 Hakkloa temperatur 20110101-20151009.txt'.format(
env.data_path)
hydraii_wind = '{0}6.24.4.15.1 Hakkloa vind 20110101-20151009.txt'.format(
env.data_path)
hydraii_relative_humidity = '{0}6.24.4.2.1 Hakkloa relativ fuktighet 20110101-20151009.txt'.format(
env.data_path)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
data = MyLakeInput(from_date, to_date)
data.output_file_path = '{0}HAK_input'.format(env.data_path)
data.output_header = 'MyLake model resources data for Hakkloa from Hakkloa (NVE), Bjørnholt (met) and Blindern (met) stations'
data.add_Global_rad(
we.constant_weather_element('Hakkloa', from_date, to_date,
'Global_rad', None))
data.add_Cloud_cov(
gws.getMetData(stnr_met_blind, 'NNM', from_date, to_date, 0))
data.add_Air_temp(
gfd.read_hydra_time_value(stnr_nve, '17.1', hydraii_temperature,
from_date, to_date))
data.add_Relat_hum(
gfd.read_hydra_time_value(stnr_nve, '2.1', hydraii_relative_humidity,
from_date, to_date))
data.add_Air_press(
gws.getMetData(stnr_met_blind, 'POM', from_date, to_date, 0))
data.add_Wind_speed(
gfd.read_hydra_time_value(stnr_nve, '15.1', hydraii_wind, from_date,
to_date))
data.add_Precipitation(
we.millimeter_from_meter(
gws.getMetData(stnr_met_bjorn, 'RR', from_date, to_date, 0)))
# Inflow is water shed area * precipitation
Inflow = []
precipitation = data.Precipitation # precipitation in [mm]
water_shed_area = 6.49 * 10**6 # [m2]
for p in precipitation:
value = p.Value / 1000 * water_shed_area
Inflow.append(we.WeatherElement('Hakkloa', p.Date, 'Inflow', value))
data.add_Inflow(Inflow)
# Inflow temperature is assumed air temp but never below 0C and if snow always 0C
snow = gcs.getGriddata(utm33_x, utm33_y, 'sd', from_date, to_date)
temperature = data.Air_temp
#mfd.write_weather_element_list(temperature)
#mfd.write_weather_element_list(snow)
#mfd.write_weather_element_list(data.Relat_hum)
#mfd.write_weather_element_list(data.Wind_speed)
Inflow_T = []
for i in range(0, len(snow), 1):
date = temperature[i].Date
value = max(0., temperature[i].Value) # water never below 0C
if snow[i].Value > 0. and value > 0.: # if snow, water never over 0C
value = 0.
Inflow_T.append(we.WeatherElement('Hakkloa', date, 'Inflow_T', value))
data.add_Inflow_T(Inflow_T)
data.add_Inflow_C(
we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_C',
.5))
data.add_Inflow_S(
we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_S',
.01))
data.add_Inflow_TP(
we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_TP',
44.))
data.add_Inflow_DOP(
we.constant_weather_element('Hakkloa', from_date, to_date,
'Inflow_DOP', 7.))
data.add_Inflow_Chla(
we.constant_weather_element('Hakkloa', from_date, to_date,
'Inflow_Chla', .1))
data.add_Inflow_DOC(
we.constant_weather_element('Hakkloa', from_date, to_date,
'Inflow_DOC', 3000.))
return data
def run_mosselva(from_date, to_date, make_plots=True, plot_folder=se.plot_folder, forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Mosselva'
y = 6595744
x = 255853
altitude = 25
met_stnr = 17150 # Rygge målestasjon (met.no)
first_ice = ice.IceColumn(dt.datetime(int(from_date[0:4]), 12, 31), [])
first_ice.add_metadata('LocationName', location_name) # used when plotting
observed_ice = [first_ice]
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice), date, temp, sno, cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, sno_tot, plot_path_and_filename)
def calculate_and_plot_location(location_name,
from_date,
to_date,
sub_plot_folder='',
make_plots=True,
return_values=False):
""" due to get_all_season_ice returns data grouped be location_id
For a given LocationName in regObs calculate the ice cover between two dates. Optional, make plots
and/or return the calculations and observations for this location. Different sources for weather data
may be given, chartserver grid is default.
:param location_name:
:param from_date: [String] 'yyyy-mm-dd'
:param to_date: [String] 'yyyy-mm-dd'
:param sub_plot_folder:
:param make_plots:
:param return_values: [bool] If true the calculated and observed data is returned
"""
loc = slp.get_for_location(location_name)
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
lake_file_name = '{0} {1}'.format(
fe.make_standard_file_name(loc.file_name), year)
observed_ice = gro.get_observations_on_location_id(loc.regobs_location_id,
year)
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# special rule for this season.
if year == '2018-19':
from_date = dt.datetime(2017, 10, 15)
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if loc.weather_data_source == 'eKlima':
wsTemp = gws.getMetData(loc.eklima_TAM, 'TAM', from_date, to_date, 0,
'list')
temp, date = we.strip_metadata(wsTemp, True)
wsSno = gws.getMetData(loc.eklima_SA, 'SA', from_date, to_date, 0,
'list')
snotot = we.strip_metadata(wsSno)
sno = dp.delta_snow_from_total_snow(snotot)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0,
'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} eklima.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'grid':
x, y = loc.utm_east, loc.utm_north
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(gridTemp, get_date_times=True)
sno = we.strip_metadata(gridSno, False)
snotot = we.strip_metadata(gridSnoTot, False)
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0,
'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} grid.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'nve':
x, y = loc.utm_east, loc.utm_north
# Temp from NVE station or grid if not.
if loc.nve_temp:
temp_obj = gcsd.getStationdata(loc.nve_temp,
'17.1',
from_date,
to_date,
timeseries_type=0)
else:
temp_obj = gcsd.getGriddata(x, y, 'tm', from_date, to_date)
temp, date = we.strip_metadata(temp_obj, get_date_times=True)
# Snow from NVE station or grid if not.
if loc.nve_snow:
snotot_obj = gcsd.getStationdata(loc.nve_snow,
'2002.1',
from_date,
to_date,
timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = dp.delta_snow_from_total_snow(snotot_obj)
else:
snotot_obj = gcsd.getGriddata(x,
y,
'sd',
from_date,
to_date,
timeseries_type=0)
sno_obj = gcsd.getGriddata(x,
y,
'fsw',
from_date,
to_date,
timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = we.strip_metadata(sno_obj)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
cc_obj = gws.getMetData(18700, 'NNM', from_date, to_date, 0,
'list')
else:
cc_obj = dp.clouds_from_precipitation(sno)
cc = we.strip_metadata(cc_obj)
plot_filename = '{0}{1} nve.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'file':
date, temp, sno, snotot = gfd.read_weather(from_date, to_date,
loc.input_file)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} file.png'.format(
se.plot_folder + sub_plot_folder, lake_file_name)
else:
ml.log_and_print(
"[Error] runicethickness -> calculate_and_plot_location: Invalid scource for weather data."
)
return
try:
if len(observed_ice) == 0:
calculated_ice = it.calculate_ice_cover_air_temp(
ice.IceColumn(date[0], []), date, temp, sno, cc)
else:
calculated_ice = it.calculate_ice_cover_air_temp(
copy.deepcopy(observed_ice[0]), date, temp, sno, cc)
if make_plots:
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
snotot, plot_filename)
except:
error_msg = sys.exc_info()[0]
ml.log_and_print(
"[Error] calculateandplot.py -> calculate_and_plot_location: {}. Could not plot {}."
.format(error_msg, location_name))
calculated_ice = None
if return_values:
return calculated_ice, observed_ice
def calculate_and_plot_location(location_name, from_date, to_date, sub_plot_folder='', make_plots=True, return_values=False):
""" due to get_all_season_ice returns data grouped be location_id
For a given LocationName in regObs calculate the ice cover between two dates. Optional, make plots
and/or return the calculations and observations for this location. Different sources for weather data
may be given, chartserver grid is default.
:param location_name:
:param from_date: [String] 'yyyy-mm-dd'
:param to_date: [String] 'yyyy-mm-dd'
:param sub_plot_folder:
:param make_plots:
:param return_values: [bool] If true the calculated and observed data is returned
"""
loc = slp.get_for_location(location_name)
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
lake_file_name = '{0} {1}'.format(fe.make_standard_file_name(loc.file_name), year)
observed_ice = gro.get_observations_on_location_id(loc.regobs_location_id, year)
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# special rule for this season.
if year == '2018-19':
from_date = dt.datetime(2017, 10, 15)
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if loc.weather_data_source == 'eKlima':
wsTemp = gws.getMetData(loc.eklima_TAM, 'TAM', from_date, to_date, 0, 'list')
temp, date = we.strip_metadata(wsTemp, True)
wsSno = gws.getMetData(loc.eklima_SA, 'SA', from_date, to_date, 0, 'list')
snotot = we.strip_metadata(wsSno)
sno = dp.delta_snow_from_total_snow(snotot)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0, 'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} eklima.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'grid':
x, y = loc.utm_east, loc.utm_north
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
temp, date = we.strip_metadata(gridTemp, get_date_times=True)
sno = we.strip_metadata(gridSno, False)
snotot = we.strip_metadata(gridSnoTot, False)
if loc.eklima_NNM:
wsCC = gws.getMetData(loc.eklima_NNM, 'NNM', from_date, to_date, 0, 'list')
cc = we.strip_metadata(wsCC)
else:
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} grid.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'nve':
x, y = loc.utm_east, loc.utm_north
# Temp from NVE station or grid if not.
if loc.nve_temp:
temp_obj = gcsd.getStationdata(loc.nve_temp, '17.1', from_date, to_date, timeseries_type=0)
else:
temp_obj = gcsd.getGriddata(x, y, 'tm', from_date, to_date)
temp, date = we.strip_metadata(temp_obj, get_date_times=True)
# Snow from NVE station or grid if not.
if loc.nve_snow:
snotot_obj = gcsd.getStationdata(loc.nve_snow, '2002.1', from_date, to_date, timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = dp.delta_snow_from_total_snow(snotot_obj)
else:
snotot_obj = gcsd.getGriddata(x, y, 'sd', from_date, to_date, timeseries_type=0)
sno_obj = gcsd.getGriddata(x, y, 'fsw', from_date, to_date, timeseries_type=0)
snotot = we.strip_metadata(snotot_obj)
sno = we.strip_metadata(sno_obj)
# Clouds. If not from met.no it is parametrised from precipitation.
if loc.eklima_NNM:
cc_obj = gws.getMetData(18700, 'NNM', from_date, to_date, 0, 'list')
else:
cc_obj = dp.clouds_from_precipitation(sno)
cc = we.strip_metadata(cc_obj)
plot_filename = '{0}{1} nve.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
elif loc.weather_data_source == 'file':
date, temp, sno, snotot = gfd.read_weather(from_date, to_date, loc.input_file)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}{1} file.png'.format(se.plot_folder + sub_plot_folder, lake_file_name)
else:
ml.log_and_print("[Error] runicethickness -> calculate_and_plot_location: Invalid scource for weather data.")
return
try:
if len(observed_ice) == 0:
calculated_ice = it.calculate_ice_cover_air_temp(ice.IceColumn(date[0], []), date, temp, sno, cc)
else:
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(observed_ice[0]), date, temp, sno, cc)
if make_plots:
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, snotot, plot_filename)
except:
error_msg = sys.exc_info()[0]
ml.log_and_print("[Error] calculateandplot.py -> calculate_and_plot_location: {}. Could not plot {}.".format(error_msg, location_name))
calculated_ice = None
if return_values:
return calculated_ice, observed_ice
def harvest_for_mylake_hakkloa(from_string, to_string):
"""Method gathers meteorological parameters needed to run myLake on Hakkloa and saves them to a
formatted file as needed to run myLake. For columns needed se MyLakeInput class.
:param from_string: {string} from date to acquire data
:param to_string: {string} to date to acquire data
:return:
HydraII parameters:
Wind 15
Temperature 17
Relative humidity 2
"""
utm33_x = 259942 # Hakloa (buoy)
utm33_y = 6671218 # Hakloa (buoy)
stnr_met_blind = 18700 # Blindern (met)
stnr_met_bjorn = 18500 # Bjørnholt (met)
stnr_nve = '6.24.4' # Hakloa (NVE)
hydraii_temperature = '{0}6.24.4.17.1 Hakkloa temperatur 20110101-20151009.txt'.format(env.data_path)
hydraii_wind = '{0}6.24.4.15.1 Hakkloa vind 20110101-20151009.txt'.format(env.data_path)
hydraii_relative_humidity = '{0}6.24.4.2.1 Hakkloa relativ fuktighet 20110101-20151009.txt'.format(env.data_path)
from_date = dt.datetime.strptime(from_string, "%Y-%m-%d")
to_date = dt.datetime.strptime(to_string, "%Y-%m-%d")
data = MyLakeInput(from_date, to_date)
data.output_file_path = '{0}HAK_input'.format(env.data_path)
data.output_header = 'MyLake model resources data for Hakkloa from Hakkloa (NVE), Bjørnholt (met) and Blindern (met) stations'
data.add_Global_rad(we.constant_weather_element('Hakkloa', from_date, to_date, 'Global_rad', None))
data.add_Cloud_cov(gws.getMetData(stnr_met_blind, 'NNM', from_date, to_date, 0))
data.add_Air_temp(gfd.read_hydra_time_value(stnr_nve, '17.1', hydraii_temperature, from_date, to_date))
data.add_Relat_hum(gfd.read_hydra_time_value(stnr_nve, '2.1', hydraii_relative_humidity, from_date, to_date))
data.add_Air_press(gws.getMetData(stnr_met_blind, 'POM', from_date, to_date, 0))
data.add_Wind_speed(gfd.read_hydra_time_value(stnr_nve, '15.1', hydraii_wind, from_date, to_date))
data.add_Precipitation(we.millimeter_from_meter(gws.getMetData(stnr_met_bjorn, 'RR', from_date, to_date, 0)))
# Inflow is water shed area * precipitation
Inflow = []
precipitation = data.Precipitation # precipitation in [mm]
water_shed_area = 6.49 * 10**6 # [m2]
for p in precipitation:
value = p.Value/1000*water_shed_area
Inflow.append(we.WeatherElement('Hakkloa', p.Date, 'Inflow', value))
data.add_Inflow(Inflow)
# Inflow temperature is assumed air temp but never below 0C and if snow always 0C
snow = gcs.getGriddata(utm33_x, utm33_y, 'sd', from_date, to_date)
temperature = data.Air_temp
#mfd.write_weather_element_list(temperature)
#mfd.write_weather_element_list(snow)
#mfd.write_weather_element_list(data.Relat_hum)
#mfd.write_weather_element_list(data.Wind_speed)
Inflow_T = []
for i in range(0, len(snow), 1):
date = temperature[i].Date
value = max(0., temperature[i].Value) # water never below 0C
if snow[i].Value > 0. and value > 0.: # if snow, water never over 0C
value = 0.
Inflow_T.append(we.WeatherElement('Hakkloa', date, 'Inflow_T', value))
data.add_Inflow_T(Inflow_T)
data.add_Inflow_C(we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_C', .5))
data.add_Inflow_S(we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_S', .01))
data.add_Inflow_TP(we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_TP', 44.))
data.add_Inflow_DOP(we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_DOP', 7.))
data.add_Inflow_Chla(we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_Chla', .1))
data.add_Inflow_DOC(we.constant_weather_element('Hakkloa', from_date, to_date, 'Inflow_DOC', 3000.))
return data
def run_semsvann(from_date, to_date, make_plots=True, plot_folder=se.plot_folder, forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Semsvann'
regobs_location_id = 2227
x = 243655
y = 6644286
altitude = 145
met_stnr = 19710 # Asker (Sem)
met_stnr_NNM = 18700 # Blindern
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
observed_ice = gro.get_observations_on_location_id(regobs_location_id, year)
first_ice = observed_ice[0]
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
wsSnoTot = gws.getMetData(met_stnr, 'SA', from_date, to_date, 0, 'list')
wsCC = gws.getMetData(met_stnr_NNM, 'NNM', from_date, to_date, 0, 'list')
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSnoTot)
sno = dp.delta_snow_from_total_snow(sno_tot)
cc = we.strip_metadata(wsCC)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# Grid altitude and lake at same elevations.
gridTempNewElevation = we.adjust_temperature_to_new_altitude(gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation, get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice), date, temp, sno, cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno, sno_tot, plot_path_and_filename)
def run_semsvann(from_date,
to_date,
make_plots=True,
plot_folder=se.plot_folder,
forcing='grid'):
"""
:param from_date:
:param to_date:
:param make_plots:
:param plot_folder:
:return:
"""
location_name = 'Semsvann'
regobs_location_id = 2227
x = 243655
y = 6644286
altitude = 145
met_stnr = 19710 # Asker (Sem)
met_stnr_NNM = 18700 # Blindern
year = '{0}-{1}'.format(from_date[0:4], to_date[2:4])
observed_ice = gro.get_observations_on_location_id(regobs_location_id,
year)
first_ice = observed_ice[0]
# Change dates to datetime. Some of the getdata modules require datetime
from_date = dt.datetime.strptime(from_date, '%Y-%m-%d')
to_date = dt.datetime.strptime(to_date, '%Y-%m-%d')
# if to_date forward in time, make sure it doesnt go to far..
if to_date > dt.datetime.now():
to_date = dt.datetime.now() + dt.timedelta(days=7)
if forcing == 'eKlima':
wsTemp = gws.getMetData(met_stnr, 'TAM', from_date, to_date, 0, 'list')
wsSnoTot = gws.getMetData(met_stnr, 'SA', from_date, to_date, 0,
'list')
wsCC = gws.getMetData(met_stnr_NNM, 'NNM', from_date, to_date, 0,
'list')
temp, date = we.strip_metadata(wsTemp, get_date_times=True)
sno_tot = we.strip_metadata(wsSnoTot)
sno = dp.delta_snow_from_total_snow(sno_tot)
cc = we.strip_metadata(wsCC)
plot_filename = '{0}_{1}_eklima.png'.format(location_name, year)
elif forcing == 'grid':
gridTemp = gts.getgts(x, y, 'tm', from_date, to_date)
gridSno = gts.getgts(x, y, 'sdfsw', from_date, to_date)
gridSnoTot = gts.getgts(x, y, 'sd', from_date, to_date)
# Grid altitude and lake at same elevations.
gridTempNewElevation = we.adjust_temperature_to_new_altitude(
gridTemp, altitude)
temp, date = we.strip_metadata(gridTempNewElevation,
get_date_times=True)
sno = we.strip_metadata(gridSno)
sno_tot = we.strip_metadata(gridSnoTot)
cc = dp.clouds_from_precipitation(sno)
plot_filename = '{0}_{1}_grid.png'.format(location_name, year)
else:
temp, date = None, None
sno = None
sno_tot = None
cc = None
plot_filename = '{0}_{1}_no_forcing.png'.format(location_name, year)
calculated_ice = it.calculate_ice_cover_air_temp(copy.deepcopy(first_ice),
date,
temp,
sno,
cloud_cover=cc)
if make_plots:
plot_path_and_filename = '{0}{1}'.format(plot_folder, plot_filename)
pts.plot_ice_cover(calculated_ice, observed_ice, date, temp, sno,
sno_tot, plot_path_and_filename)