Processing: Station Nord 2024

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Processing: Station Nord 2024#

In this notebook, we’ll run thermal and solar simulations using radiosonde and ceilometer data from Station Nord, Greenland (2024 campaign).

Note

Campaign data required. This notebook uses radiosonde and ceilometer data from Station Nord that is not publicly available. You can adapt the workflow to your own data by replacing the file paths.

Setup#

ds_rs = xr.open_mfdataset(glob.glob('/projekt_agmwend/home_rad/Joshua/STN2024/RS_STN/radiosonde_netcdf/*.nc'), combine='by_coords')

ds_rs

import glob
ds_ceilo = xr.open_dataset('/projekt_agmwend/data/Station_Nord_2024/Ceilometer/Ceilometer_STN_all.nc')

import xarray as xr
from datetime import datetime
from pyradtran.io_unified import RadiosondeAtmosphereGenerator
from pathlib import Path


# 2. Build a LibRadtran atmosphere file from the closest IGRA sounding
atm_path = RadiosondeAtmosphereGenerator.create_radiosonde_atmosphere_file(
    time=datetime(2023, 12, 30, 0),
    latitude=lat,
    longitude=lon,
    output_filepath="work/sonde_nya.dat",
)

ds_gp = ds_rs[['altitude', 'air_temperature', 'relative_humidity']].groupby('time.dayofyear')
atm_paths = []
for day, ds_day in ds_gp:
    print(f"Day {day}:")
    print(ds_day)
    date = ds_day['time'].dt.date.values[0]
    df_day = ds_day.to_dataframe().reset_index().dropna(how='any').sort_values(by='altitude', ascending=True).drop(columns=['time']).drop_duplicates()
    df_day['altitude'] /= 1000
    df_day['air_temperature'] += 273.15
    atm_name = f"work/stn2024_day_{date}.csv"
    df_day.to_csv(atm_name, index=False, header=False, sep='\t')
    atm_paths.append(atm_name)

atm_paths[11]

ds_sim_thermal = ds.isel(time=slice(0,2)).pyradtran.run(
    config_path=thermal_config_path,
    return_dataset=True,
    save_to_file=False,
    parameter_overrides={"atmosphere_file": str(atm_paths[10])},
    show_progress=False,
)

fig, ax = plt.subplots(2, 1, figsize=(12, 4), sharex=True)

(ds_sim_solar.drop_vars(['latitude', 'longitude', 'altitude']).eglo / 1000).plot()
(ds_sim_thermal.drop_vars(['latitude', 'longitude', 'altitude']).eglo).plot(ax=ax[0])

for a in ax:
    a.set_ylabel('F_down (W/m²)')
    a.grid()
    a.spines[['top', 'right']].set_visible(False)

# 3. Run pyradtran from the xarray accessor, overriding the atmosphere file
result_ds = ds.pyradtran.run(
    config_path=solar_config_path,
    parameter_overrides={"radiosonde": str(atm_path)+" H2O RH"},
    return_dataset=True,
    save_to_file=False,
    show_progress=False,
)
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