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Full-Text Articles in Meteorology
Relative Importance Of Radar Variables For Nowcasting Heavy Rainfall: A Machine Learning Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Relative Importance Of Radar Variables For Nowcasting Heavy Rainfall: A Machine Learning Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos
Institute for ECHO Articles and Research
Highly short-term forecasting, or nowcasting, of heavy rainfall due to rapidly evolving mesoscale convective systems (MCSs) is particularly challenging for traditional numerical weather prediction models. To overcome such a challenge, a growing number of studies have shown significant advantages of using machine learning (ML) modeling techniques with remote sensing data, especially weather radar data, for high-resolution rainfall nowcasting. To improve ML model performance, it is essential first and foremost to quantify the importance of radar variables and identify pertinent predictors of rainfall that can also be associated with domain knowledge. In this study, a set of MCS types consisting of …
Evaluation Of Satellite-Retrieved Extreme Precipitation Rates Across The Central United States, A. Aghakoucak, A. Behrangi, S. Sorooshian, K. Hsu, Eyal Amitai
Evaluation Of Satellite-Retrieved Extreme Precipitation Rates Across The Central United States, A. Aghakoucak, A. Behrangi, S. Sorooshian, K. Hsu, Eyal Amitai
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite-retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite-based estimates are not well integrated into operational and decision-making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite-derived precipitation products (CMORPH, PERSIANN, TMPA-RT, and TMPA-V6) are evaluated with …