Improving Data‐Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere

I share with you this interesting Python project. DLWP-CS is a Python project containing data-processing and model-building tools for predicting the gridded atmosphere using deep convolutional neural networks applied to a cubed sphere.

Recent work has begun to explore building global weather prediction models using only machine learning techniques trained on large amounts of atmospheric data. The authors develop a vastly improved machine learning algorithm capable of operating like traditional weather models and predicting several fundamental atmospheric variables, including near‐surface temperature. While this model does not yet compete with the state‐of‐the‐art in numerical weather prediction, it computes realistic forecasts that perform well and execute extremely quickly, offering a potential avenue for future developments in probabilistic weather forecasting.

All the information and code at the following Github:

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