LightGBM with surrounding-grid feature selection and Tweedie loss for precipitation yields lower RMSE than raw MSM forecasts, MSMG, and some CNN baselines across many Japanese locations and lead times.
Numerical forecast correction of temperature and wind using a single-station single-time spatial lightgbm method
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Improvements to the post-processing of weather forecasts using machine learning and feature selection
LightGBM with surrounding-grid feature selection and Tweedie loss for precipitation yields lower RMSE than raw MSM forecasts, MSMG, and some CNN baselines across many Japanese locations and lead times.