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Commit 3ff8417c authored by JakobDeutloff's avatar JakobDeutloff
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Errors in build_confic_mosaic removed and flags added in build_initial_state

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......@@ -74,8 +74,10 @@ config['bottom_flag'] = 1
config['tank_flag'] = 1
# ________________________snow______________________
config['precip_flag'] = 0
config['freeboard_snow_flag'] = 0 # < Niels, 2017
config['snow_flush_flag'] = 1 # < Niels, 2017
config['freeboard_snow_flag'] = 0
config['snow_flush_flag'] = 1
config['styropor_flag'] = 0
config['lab_snow_flag'] = 0
# ________________________debugging_____________________
config['debug_flag'] = 1 # set to 2 for output of all ice layers each timestep
# ________________________bgc_______________________
......
......@@ -128,29 +128,29 @@ data_spinup['downward thermal radiation adapted (W/m^2)'] = lw_spinup + 10
# Concatenate Data
data = pd.concat([data_spinup[0:-1], data_mosaic.rename(columns={'Snowrate (m/s)' : 'Snowfall (m/s)',
'Downward thermal radiation (W/m^2)' : 'downward thermal radiation adapted (W/m^2)',
'Downward solar radiation (W/m^2)' : 'downward solar radiation (W/m^2)'})], axis=0)
'Downward solar radiation (W/m^2)' : 'downward solar radiation (W/m^2)'})], axis=0, sort=True)
path = r'../../../input'
# snow only use 10% of calculated snow rate - uniform for whole time series
snow = data['Snowfall (m/s)']/10
# Increase snow rates at beginning of ts to get thicker snow right at the beginning
snow['2019-01'] = snow['2019-01'] * 10
snow.to_csv(path + r'\precip_l.txt', header=False, index=False, float_format='%.7e')
snow.to_csv(path_input + r'/precip_l.txt', header=False, index=False, float_format='%.7e')
# LW
data['downward thermal radiation adapted (W/m^2)'].to_csv(path + r'\fl_lw.txt', header=False, index=False,
data['downward thermal radiation adapted (W/m^2)'].to_csv(path_input + r'/fl_lw.txt', header=False, index=False,
float_format='%.7e')
# SW
data['downward solar radiation (W/m^2)'].to_csv(path + r'\fl_sw.txt', header=False, index=False, float_format='%.7e')
data['downward solar radiation (W/m^2)'].to_csv(path_input + r'/fl_sw.txt', header=False, index=False, float_format='%.7e')
# latent
data['latent_hf (W/m^2)'].to_csv(path + r'\fl_lat.txt', header=False, index=False, float_format='%.7e')
data['latent_hf (W/m^2)'].to_csv(path_input + r'/fl_lat.txt', header=False, index=False, float_format='%.7e')
# sensible
data['sensible_hf (W/m^2)'].to_csv(path + r'\fl_sen.txt', header=False, index=False, float_format='%.7e')
data['sensible_hf (W/m^2)'].to_csv(path_input + r'/fl_sen.txt', header=False, index=False, float_format='%.7e')
# temperature - convert to °C
(data['2m temp (K)']-273.15).to_csv(path + r'\T2m.txt', header=False, index=False, float_format='%.7e')
(data['2m temp (K)']-273.15).to_csv(path_input + r'/T2m.txt', header=False, index=False, float_format='%.7e')
# **********************************************************************************************************************
......
{
"start_time": "2022-01-01 00:00:00",
"dt": 60,
"time": 0.0,
"time_out": 302400.0,
"time_total": 65318400.0,
"length_input": 1088640.0,
"timestep_data": 60,
"thick_0": 0.03,
"Nlayer": 20,
"N_active": 1,
"N_top": 5,
"N_bottom": 5,
"N_middle": 10,
"boundflux_flag": 2,
"albedo_flag": 2,
"grav_heat_flag": 1,
"flush_heat_flag": 1,
"flood_flag": 2,
"flush_flag": 5,
"grav_flag": 2,
"harmonic_flag": 2,
"prescribe_flag": 1,
"salt_flag": 1,
"turb_flag": 2,
"bottom_flag": 1,
"tank_flag": 1,
"precip_flag": 0,
"freeboard_snow_flag": 0,
"snow_flush_flag": 1,
"styropor_flag": 0,
"lab_snow_flag": 0,
"debug_flag": 1,
"bgc_flag": 1,
"initial_state_flag": 1,
"tank_depth": 1,
"alpha_flux_stable": 15,
"alpha_flux_instable": 22,
"N_bgc": 2,
"bgc_bottom_1": 385,
"bgc_bottom_2": 385,
"thick_1": 0.03,
"m_1": 30.84,
"S_abs_1": 1048.56,
"H_abs_1": 0
}
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Testcase_3
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