Enhancing Quantitative Precipitation Estimation of NWP Model With Fundamental Meteorological Variables and Transformer Based Deep Learning Model
Haolin Liu,
Jimmy C. H. Fung,
Alexis K. H. Lau
et al.
Abstract:Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitati… Show more
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