Machine Learning in the Earth Systems Sciences

MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.

A launch point for your personal nvim configuration

Notebooks teaching Python for use meteorology and atmospheric and climate sciences.

A cookbook trying to understand the uncertainties of the aerosol-cloud interactions across models with various resolutions and parameterizations.

Reusable workflows used by Project Pythia Cookbooks

Jupyterbook source for the Foundations collection

Benchmarks for the MetPy project

A place to gather and hold code and code-adjacent assets related to blog posts (scripts, notebooks, IDV bundles, etc.).

A framework for querying AWIPS meteorological datasets from an EDEX Data Server.

Siphon - A collection of Python utilities for retrieving atmospheric and oceanic data from remote sources, focusing on being able to retrieve data from Unidata data technologies, such as the THREDD…

Data repository for Project Pythia examples/notebooks

Project Pythia MRMS Cookbook

Using xarray to train deep learning models and use them for inference.

A cookiecutter-based hackathon template

Drew-n-Doug's Daily Dose of Data project

Materials for the 2-day course on MetPy for Quantitative Analysis of Meteorological Data

Unidata Science Gateway on the NSF Jetstream2 Cloud

Website for the Pythia Cook-off June 2024

Repository for the 2023 AMS Radar Short Course