C³ONTEXT: A Common Consensus on Convective OrgaNizaTion during the EUREC⁴A eXperimenT

This repository includes the source code for the post-processing of the manual cloud classifications that have been gathered during an online hackathon with international scientists in March 2020.

The overview of the classifications during the EUREC⁴A field campaign gives a good first impression about the dataset and the meso-scale patterns of shallow convection encountered during January - February 2020.

Example usage: get meso-scale organization along trajectory

One of the use cases of this dataset is to retrieve the meso-scale organization of shallow convection at a specific point in time and space. Because the variety and number of platforms during the EUREC⁴A campaign has been enormous, the procedure to retrieve the cloud classifications along a trajectory is shown below for the RV Meteor.

Source code

Please install all requirements before executing the code:

pip install eurec4a dask matplotlib pandas

import numpy as np
import datetime as dt
import dask
import matplotlib.pyplot as plt
import eurec4a
from matplotlib import dates
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

cat = eurec4a.get_intake_catalog()

Loading classifications that are based on the infrared satellite images.

ds = cat.c3ontext.level3_IR_daily.to_dask()

Loading the platform track

platform = 'Meteor'
ds_plat = cat[platform].track.to_dask()

Define standard colors:

color_dict = {'Flowers':'#2281BB',
              'Fish': '#93D2E2',
              'Gravel': '#3EAE47',
              'Sugar': '#A1D791'}

The level 3 data used in this example is a daily average. For simplicity and assuming that both the platform as well as the meso-scale patterns do not change quickly, we calculate the daily mean position of the platform:

ds_plat_rs = ds_plat.resample(time='1D').mean() # Attention, only works as long as the 0 meridian is not crossed

Plot the data:

# Reading the actual data
with dask.config.set(**{'array.slicing.split_large_chunks': False}):
    data = ds.freq.interp(latitude=ds_plat_rs.lat, longitude=ds_plat_rs.lon).sel(date=ds_plat_rs.time)
    data.load()
data=data.fillna(0)*100

# Plotting
fig, ax = plt.subplots(figsize=(8,2))
for d, (time, tdata) in enumerate(data.groupby('time')):
    frequency = 0
    for p in ['Sugar', 'Gravel', 'Flowers', 'Fish', 'Unclassified']:
        ax.bar(dates.date2num(time), float(tdata.sel(pattern=p)), label=p, bottom=frequency, color=color_dict[p])
        hfmt = dates.DateFormatter('%d.%m')
        ax.xaxis.set_major_locator(dates.DayLocator(interval=5))
        ax.xaxis.set_major_formatter(hfmt)
        frequency += tdata.sel(pattern=p)
    if d == 0:
        plt.legend(frameon=False, bbox_to_anchor=(1,1))
plt.xlabel('date')
plt.ylabel('agreement / %')
xlim=plt.xlim(dt.datetime(2020,1,6), dt.datetime(2020,2,23))

Further information on how to use this dataset can also be found on the How to EUREC⁴A-Website