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 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 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 input 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 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 input 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
