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Webinar: Second Generation CMORPH for Real-Time Monitoring
Wednesday, 17 August 2022, 3:00
Wednesday, August 17, 2022. 3:00 PM. Webinar: Second Generation CMORPH for Real-Time Monitoring. Pingping Xie, NOAA Climate Prediction Center. Sponsored by NOAA and ACCAP. More information here. Register here.
Abstract: The second generation CMORPH (CMORPH2) has started real-time production of high-quality, high-resolution pole-to-pole global precipitation estimates since April 2017. Inputs to the CMORPH2 include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecasts from the NCEP operational global forecast system (GFS). A processing system has been constructed at NOAA/NWS/NCEP to produce the CMORPH2 satellite precipitation estimates on a real-time mode. CMORPH2 precipitation estimates generated at various latency levels show very good quantitative consistencies, while comparison against gauge and radar observations indicates pattern correlation improving with the production latency. The most important purpose of this webinar is to get feedback from our colleagues in the field offices on how we may improve the CMORPH2 to better serve operational applications in weather, water, and climate.
Bio(s): Pingping Xie is a meteorologist at the Development Branch of the NOAA Climate Prediction Center (CPC). His main duty at CPC is to perform research and development to enhance real-time monitoring and assessment activities in CPC. In particular, recently he has been coordinating a CPC working group to generate a suite of unified global and regional precipitation products for various applications both inside and outside NOAA/CPC. In addition, Dr. Xie is also involved in diagnostic studies of the cloud, precipitation and global water cycle and their representations in the NOAA operational global models (e.g. the Climate Forecast Model, CFS).