Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning

no code implementations • 4 Apr 2024 • MengMeng Han, Tennessee Leeuwenburg, Brad Murphy

In this study, we investigate the feasibility of optimising forecasts at sites by adopting the popular machine learning model gradient boosting decision tree, supported by the Python version of the XGBoost package.

FourCastNeXt: Optimizing FourCastNet Training for Limited Compute

1 code implementation • 10 Jan 2024 • Edison Guo, Maruf Ahmed, Yue Sun, Rui Yang, Harrison Cook, Tennessee Leeuwenburg, Ben Evans

FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet computational requirements.

Model Optimization Weather Forecasting