CTyPyTool: Cloud Typing Python Tool

This tools is intended to help weather forecasters in assessing the quality of their cloud forecasts.

A few facts:

It emulates a cloud typing methodology (see https://www.nwcsaf.org/ct2) applied to Meteosat data (see https://www.eumetsat.int/meteosat-second-generation).
It uses standard machine learning techniques, e.g. tree & random forest classifier
It can be applied to so-called synthetic satellite data (observsation-equivalents derived from numerical forecast data).

Schematic

Installation

Use the following command to clone the project to your local machine.

$ git clone https://github.com/fsenf/CTyPyTool

This project comes with a Pipfile specifying all project dependencies. When using pipenv first move into the project folder with:

$ cd cloud_classification

and then use the following command to install all necesarry dependencies into your virtual environment

$ pipenv install

See here to get started with CTyPyTools on the DKRZ Super computer.

There are severeal Jupyter Notebooks explaining the basic steps for training and applying the cloud classifier.

For using an already trained classifier check out this notebook

Your Contribution is very welcome! Yo could either contribute with:

providing pre-trained classifiers for a specifically defined geographical region or for certain sessions
reporting issues, missing features or bugs
improving code

5 Steps for source code developers:

fork the repository with the main branch
branch out into a feature-<something> branch in you own fork
update source code / software parts in your fork
check functionality with example notebooks
make a pull request onto the main branch in the “official” repository under https://github.com/fsenf/CTyPyTool