High quality Quantitative Precipitation Estimation at high spatiotemporal resolution is crucial to many hydrologic/hydro-meteorological designs. Optimal Quantitative Precipitation Estimation of rainfall improves the accuracy of river and flash flood forecasts. In this study, we aim to merge multiple rainfall estimates including rain gauge, radar, Inverse Distance Weighting, Ordinary Co-Kriging, and Adaptive Conditional Bias Penalized Co-Kriging through two most common model selection techniques known as Least Absolute Shrinkage and Selection Operator and Bayesian Model Averaging. The methods were applied to the entire United States for a certain period. Statistical measures such as RMSE, ME, NSE, and Correlation Coefficient are used to investigate the accuracy and reliability of the estimation models. It is shown that both BMA and LASSO improve the precipitation estimation considering all ranges of rainfall observation included. However, OCK and CBPCK technique outperforms other methods in rainfall more than 10 mm. The IDW estimates show small bias, which results in a poor estimation, which is due to the limitation in using secondary variable radar. However, OCK and CBPCK address this problem by adding radar rainfall estimates as the second variable.
Improving Post-Processing for Quantitative Precipitation Forecasting Using Deep Learning: Learning Precipitation Physics from High-Resolution Observations
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Technical University Munich（慕尼黑工业大学）；Eberhard Karls Universität Tübingen（蒂宾根大学）；University of Exeter（埃克塞特大学）
Three-Dimensional Variational Data Assimilation with Rapid Update Cycling for Short-Range Precipitation Forecasting: A Case Study of Heavy Rainfall in Bali, Indonesia
Bandung Institute of Technology (ITB)（万隆理工学院）；Ronin Institute for Independent Scholarship（龙人独立学术研究学院）；School of Systems Science and Industrial Engineering, State University of New York (SUNY)（纽约州立大学系统科学与工业工程学院）
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas
Instituto de Física de Cantabria (IFCA), CSIC-UC（坎塔布里亚物理研究所（CSIC-UC联合机构））；MicroStep-MIS（MicroStep-MIS公司）；Scientific Computing Center (SCC), Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院科学计算中心）
Generating realistic global precipitation fields from modelled atmospheric circulation
Technical University of Munich（慕尼黑技术大学）；Munich Climate Center（慕尼黑气候中心）；TUM School of Engineering and Design（TUM工程与设计学院）；Department of Aerospace and Geodesy（航空航天与大地测量系）；Earth System Modelling Group（地球系统建模组）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Global Systems Institute（全球系统研究所）；Department of Mathematics（数学系）；University of Exeter（埃克塞特大学）
WRF model parameter calibration to improve the prediction of tropical cyclones over the Bay of Bengal using Machine Learning-based Multiobjective Optimization
DPSformer: A long-tail-aware model for improving heavy rainfall prediction
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University（福建省智慧城市传感与计算重点实验室，信息学院，厦门大学）
