Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

作     者：Temesgen Gebremariam ASFAW Jing-Jia LUO Temesgen Gebremariam ASFAW;Jing-Jia LUO

作者机构：Institute for Climate and Application Research(ICAR)/CIC-FEMD/KLME/ILCECNanjing University of Information Science and TechnologyNanjing 210044China Institute of Geophysics Space Science and AstronomyAddis Ababa UniversityAddis Ababa 1176Ethiopia 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2024年第41卷第3期

页      面：449-464页

核心收录：

学科分类：07[理学] 0706[理学-大气科学] 

基　　金：supported by the National Key Research and Development Program of China (Grant No.2020YFA0608000) the National Natural Science Foundation of China (Grant No. 42030605) the High-Performance Computing of Nanjing University of Information Science&Technology for their support of this work。 

主　　题：East Africa seasonal precipitation forecasting downscaling deep learning convolutional neural networks(CNNs) 

摘      要：This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.