Estimating Summertime Precipitation from Himawari-8 and Global Forecast System Based on Machine Learning

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has be...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing 2019-05, Vol.57 (5), p.2557-2570
Hauptverfasser:	Min, Min, Bai, Chen, Guo, Jianping, Sun, Fenglin, Liu, Chao, Wang, Fu, Xu, Hui, Tang, Shihao, Li, Bo, Di, Di, Dong, Lixin, Li, Jun
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Algorithms Artificial intelligence Atmospheric models Atmospheric precipitations Brightness temperature Classification Data Detection Forestry Forests Himawari-8 Learning algorithms Machine learning machine learning (Ml) Mathematical models Microwave measurement Model accuracy Ocean surface Optimization Precipitation Prediction algorithms Prediction models quantitative precipitation estimates (QPEs) Radio frequency Rain Rainfall Rainfall area random forests (RFs) Ratios Regression analysis Regression models Robustness (mathematics) Satellites Sea measurements Sensitivity analysis Surface radiation temperature Synchronous satellites Training Weather forecasting
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