At NSF Unidata, we have successfully implemented and re-used weights from several global AI-NWP (Artificial Intelligence-Numerical Weather Prediction) models (FourCastNet, Pangu) using the NVIDIA earth2mip package. We can confirm that these models are open source and can be reused on high-end, but increasingly standard, HPC hardware. While traditional numerical weather prediction requires massive supercomputing resources, these AI models can potentially deliver similar or better results using standard GPU hardware for inference.

The StatQuest Illustrated Guide to Neural Networks and AI: With hands-on examples in PyTorch!!! strikes an excellent balance between accessibility and technical depth. Josh Starmer, PhD, builds on his previous work while making neural networks approachable for both students and practitioners. This book has a similar feel and vibe to the previous book, The StatQuest guide to Machine Learning.

NOAA's Satellite Products and Services Division is looking for feedback on community use of the GOES Rebroadcast system (GRB).

At NSF Unidata, we have been supporting and developing netCDF standards and packages since the original release of netCDF in 1990. We strongly believe in the usefulness of netCDF Common Data Model for Earth Systems Science data, and for other types of data! NetCDF files can be used efficiently in machine learning modeling applications and can be used as a virtual Zarr datasets.

NSF Unidata has been urged by our community to investigate options to allow netCDF to work more easily with modern cloud-based infrastructure. Based on the strong interest and rapid adoption of Zarr by the community, the netCDF team decided to begin working with the Zarr community to ensure that these two widely used data storage mechanisms can interoperate if necessary.

Convolutional Neural Networks (CNNs) are a powerful class of deep learning models widely applied in Earth science for image analysis, classification, and regression problems. Leveraging the Keras framework in python, CNNs can efficiently process and extract spatial features from 2D and 3D remote sensing, model output, and other Earth Systems Science (ESS) data types.

The Keras package is an open-source library that provides a Python interface for deep learning. Keras is intended to be a user-friendly, modular, and extensible way to enable fast experimentation with deep neural networks. With Keras version 3, the package provides APIs for using three backends: TensorFlow, Jax, and PyTorch.

The NSF Unidata server hosted at the Space Science and Engineering Center (SSEC) at the University of Wisconsin, Madison (unidata3.ssec.wisc.edu) will be permanently decomissioned on April 26, 2024.

Those using services provided by unidata3.ssec.wisc.edu can switch to using the servers described in this article.

NSF Unidata will be shutting down two existing special-purpose THREDDS Data Servers on April 15, 2024. Please read the full article for details.