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c3ontext

Meso-scale organization of shallow convection during the EUREC⁴A field campaign based primarily on manual classifications.

https://github.com/observingclouds/c3ontext

Science Score: 39.0%

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eurec4a
Last synced: 10 months ago · JSON representation

Repository

Meso-scale organization of shallow convection during the EUREC⁴A field campaign based primarily on manual classifications.

Basic Info
  • Host: GitHub
  • Owner: observingClouds
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 20 MB
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  • Stars: 3
  • Watchers: 1
  • Forks: 2
  • Open Issues: 0
  • Releases: 3
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eurec4a
Created over 6 years ago · Last pushed almost 4 years ago
Metadata Files
Readme Citation

README.md

CONTEXT: A Common Consensus on Convective OrgaNizaTion during the EURECA eXperimenT

Paper Data Binder

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:

bash pip install eurec4a dask matplotlib pandas

```python 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 registermatplotlibconverters registermatplotlibconverters()

cat = eurec4a.getintakecatalog() ```

Loading classifications that are based on the infrared satellite images. python ds = cat.c3ontext.level3_IR_daily.to_dask()

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

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

Plot the data: ```python

Reading the actual data

with dask.config.set(*{'array.slicing.splitlargechunks': False}): data = ds.freq.interp(latitude=dsplatrs.lat, longitude=dsplatrs.lon).sel(date=dsplatrs.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=colordict[p]) hfmt = dates.DateFormatter('%d.%m') ax.xaxis.setmajorlocator(dates.DayLocator(interval=5)) ax.xaxis.setmajorformatter(hfmt) frequency += tdata.sel(pattern=p) if d == 0: plt.legend(frameon=False, bboxto_anchor=(1,1)) plt.xlabel('date') plt.ylabel('agreement / %') xlim=plt.xlim(dt.datetime(2020,1,6), dt.datetime(2020,2,23)) ```

timeseries

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

Owner

  • Name: Hauke Schulz
  • Login: observingClouds
  • Kind: user
  • Location: Seattle

Climate scientist

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Dependencies

environment.yml conda
  • basemap
  • dask
  • matplotlib
  • netcdf4
  • numpy
  • pandas
  • pip
  • python >=3.7
  • seaborn
  • tqdm
  • xarray
  • zarr