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

On your Local Computer

Cloning repository

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

$ git clone https://github.com/rudegrass/cloud_classification.git

Installing Dependencies:

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

On the DKRZ Servers

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

Getting Started

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

Contributing

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

4 Steps for source code developers:

1. fork the devel branch
2. update source code / software parts in your fork
3. check functionality with example notebooks
4. make a pull request onto the devel branch in the "official" repository under https://github.com/fsenf/CTyPyTool