COMPEX

Clouds over cOMPlEX environment COMPEX (blog, wiki) - Polar 5 campaign out of Longyearbyen (13.3.-15.4.2026).

Detailed flight logs

RF#	Date	Description	TO	TD	HH	Track (png)	Track (KML)
RF01	15.03.2026	Investigate advected clouds over sea ice west of Svalbard.	10:10	14:00	03:50	png	KML
RF02	16.03.2026	Clouds over sea ice and ocean (south east of Svalbard) #1	9:17	13:33	04:16	png	KML
RF03	17.03.2026	Statistics of clouds over sea ice.	9:28	14:06	04:38	png	KML
RF04	19.03.2026	Kongsfjorden survey to investigate sublimation processes.	9:54	14:41	04:47	png	KML
RF05	24.03.2026	Investigate the place of origin of an cold air outbreak	9:55	14:14	04:16	png	KML
RF06	25.03.2026	Ny-Alesund overflight, MPCs northwest of Svalbard and EarthCARE overpass	12:17	16:55	04:48	png	KML
RF07	26.03.2026	Capture developing cloud streets during CAOs using quasi-lagrangian approach	09:58	13:47	03:49	png	KML
RF08	27.03.2026	Radiation square, CAOs and EarthCARE overpass	12:03	16:13	04:10	png	KML
RF09	30.03.2026	Lagrangian sampling of WAI clouds (for Manfred)	10:54	14:35	03:41	png	KML
RF10	31.03.2026	Spatial variability of clouds over Kongsfjorden, convergence line NW of Svalbard	08:00	13:02	05:02	png	KML
RF11	02.04.2026	Lead experiment to measure profiles up- and downstream of lead #1	07:52	11:45	03:53	png	KML
RF12	04.04.2026	Investigate the place of origin of an cold air outbreak along marginal sea ice zone	07:47	12:20	04:33	png	KML
RF13	11.04.2026	Spatial and temporal variability of clouds over Kongsfjorden with approaching front	06:52	09:36	02:44	png	KML
RF14	13.04.2026	Lead experiment to measure profiles up- and downstream of lead #2	07:50	12:03	04:13	png	KML
RF15	14.04.2026	Clouds over sea ice and ocean (south east of Svalbard) #2	07:30	09:33	02:03	png	KML

Fig. 4 Flight tracks of the COMPEX campaign.

%matplotlib inline
import ac3airborne
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np
plt.style.use("../mplstyle/book")

# load intake catalog and flight segments
cat = ac3airborne.get_intake_catalog()
meta = ac3airborne.get_flight_segments()

ancillary = ['AMSR2_SIC']

def data_availability(mission, platform):
    """
    Plot data availability matrix
    """
    flights = list(filter(lambda d: meta[mission][platform][d]['mission'] == mission,  meta[mission][platform]))
    flights_dc = dict(filter(lambda d: mission == meta[mission][platform][d[0]]['mission'],  meta[mission][platform].items()))
    # store data availability in a matrix
    # data_availability = np.zeros(shape=(len(list(cat[mission][platform])),
    #                                     len(list(meta[mission][platform]))))
    data_availability = np.zeros(shape=(len(list(cat[mission][platform])),
                                        len(flights)))
    for i, dataset in enumerate(list(cat[mission][platform])):
        # data_availability[i, :] = np.isin(list(meta[mission][platform]), 
        #                                   list(cat[mission][platform][dataset]))
        data_availability[i, :] = np.isin(flights, 
                                          list(cat[mission][platform][dataset]))

    # plot data availability
    yscale = data_availability.shape[1]
    fig, ax = plt.subplots(1, 1, figsize=(5, 2+0.4*yscale))
    
    ax.pcolormesh(range(data_availability.shape[0]), 
                  range(data_availability.shape[1]), 
                  data_availability.T, 
                  shading='nearest',
                  vmin=0, 
                  vmax=1, 
                  cmap='RdYlGn', 
                  edgecolors='#eeeeee')

    ax.set_xticks(range(data_availability.shape[0]))
    ax.set_yticks(range(data_availability.shape[1]))

    xlabels = []
    for ds_name in list(cat[mission][platform]):
        if ds_name in ancillary:
            ds_name += '\n(ancillary)'
        xlabels.append(ds_name)

    ax.set_xticklabels(xlabels, rotation=90)
    # y_labels = [flight_id+', '+flight['date'].strftime('%Y-%m-%d') 
    #             for flight_id, flight in meta[mission][platform].items()]
    y_labels = [flight_id+', '+flight['date'].strftime('%Y-%m-%d') 
                for flight_id, flight in flights_dc.items()]
    ax.set_yticklabels(y_labels)

    ax.tick_params(labeltop=True)
    ax.invert_yaxis()

    available = mpatches.Patch(color='green', label='Data available')
    not_flown = mpatches.Patch(color='red', label='Data missing or\ninstrument failed')

    ax.legend(handles=[available, not_flown], ncol=1,
              bbox_to_anchor=(0, 1.01), loc='lower right')

    plt.show()

Polar 5

data_availability(mission='COMPEX', platform='P5')