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Improvements to the post-processing of weather forecasts using machine learning and feature selection
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DOI：10.1016/j.atmosres.2026.109037.png)
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Abstract
En 中文
• ML post-processing models are developed for three variables at 18 sites in Japan. • Surrounding-grid predictors improve accuracy by using spatial context. • Correlation-based feature selection removes redundant surrounding-grid inputs. • LightGBM gives lower RMSE than MSM, MSMG, and tested NN/CNN baselines. • Tweedie weighting improves threshold-based precipitation prediction.
Keywords:
Machine Learning
Weather Forecasting
Feature Selection
LightGBM
Precipitation Prediction
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4.4
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1.1K
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