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https://github.com/observingclouds/c3ontext
Meso-scale organization of shallow convection during the EUREC⁴A field campaign based primarily on manual classifications.
https://github.com/observingclouds/c3ontext
eurec4a
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Meso-scale organization of shallow convection during the EUREC⁴A field campaign based primarily on manual classifications.
- Host: GitHub
- URL: https://github.com/observingclouds/c3ontext
- Owner: observingClouds
- Created: 2020-04-08T17:24:12.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-09-26T23:49:35.000Z (over 2 years ago)
- Last Synced: 2023-03-06T03:04:10.323Z (almost 2 years ago)
- Topics: eurec4a
- Language: Jupyter Notebook
- Homepage:
- Size: 20 MB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Citation: CITATION.cff
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README
# C³ONTEXT: A Common Consensus on Convective OrgaNizaTion during the EUREC⁴A eXperimenT
[](https://doi.org/10.5194/essd-14-1233-2022)
[](https://doi.org/10.5281/zenodo.3763351)
[](https://mybinder.org/v2/gh/observingClouds/C3ONTEXT/HEAD?labpath=notebooks%2Ffig5_query_classifications_at_platform.ipynb)
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](classification_overview.md) 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 register_matplotlib_converters
register_matplotlib_converters()
cat = eurec4a.get_intake_catalog()
```
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.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](https://howto.eurec4a.eu/c3ontext.html)