This precipitation session focuses on precipitation observation and process understanding, modeling, estimation across spatiotemporal scales, advanced statistical techniques including artificial intelligence (AI)-based methods used to produce, analyze, and/or visualize precipitation data, and applications of in-situ and remotely sensed precipitation products. Process understanding and observational development topics include, but are not limited to (1) precipitation processes and modeling in coupled or uncoupled model systems; (2) improved assimilation algorithms leveraging precipitation process understanding; (3) recent development pertaining to fusion and downscaling of precipitation products; while applications could include: (4) assimilation of precipitation and precipitation-related variables in weather or water models; (5) impact of uncertainties associated with precipitation observations on hydrologic design and modeling or other modeling applications which ingest precipitation data, (6) assessment of precipitation variability, across spatiotemporal scales.
Submitters: Andrew J. Newman, NCAR, Boulder, CO; Viviana Maggioni, Civil, Environmental Engineering, George Mason University, Fairfax, VA; Youcun Qi, IGSNRR, Chinese Academy of Sciences, Beijing, China; Samantha Hartke, Research Applications Laboratory, NCAR, Boulder, CO and Zhe Zhang, Shenzhen National Climate Observatory, Shenzhen, Guangdong Province, China
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