Improving the Monitoring of Weather and Climate with the JPSS Infused CMORPH2

Abstract:

The second generation CMORPH (CMORPH2) has been developed at NOAA Climate Prediction Center (CPC) to construct estimates of 30-min precipitation on a 0.05^olat/lon grid over the entire globe. Inputs to the system include rainfall and snowfall rate retrievals from passive microwave (PMW) measurements from sensors aboard JPSS and other low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, as well as model precipitation forecast from the NCEP operational global forecast system (GFS). The processing is carried with the algorithm of Xie and Joyce (2015). After inter-calibration against a common reference standard (GPM retrievals) through PDF matching, these inputs of precipitation estimates from various sources are then propagated from their respective measurement time to the target analysis time to get the CMORPH2 precipitation estimates. In addition to the total precipitation, fraction of solid precipitation is computed from the surface air temperature and other surface meteorological variables using the algorithm of Sims and Liu (2015).

An operational system has been constructed at NOAA/NWS/NCEP to produce the CMORPH2 satellite precipitation estimates on a real-time mode. CMORPH2 precipitation estimates are generated at a very short latency of one hour and then updated with any newly available inputs. These real-time production of CMORPH2 precipitation estimates are pushed into the NWS/AWIPS system for operational applications at field offices. CMORPH2 precipitation estimates generated at various latency levels show very good quantitative consistencies, while comparison against gauge and radar observations indicating pattern correlation improving with the production latency. Inter-comparison studies demonstrated superior performance of the second generation CMORPH upon its predecessor the first generation CMORPH and the NASA/IMERG, especially in representing cold season precipitation. Detailed results of performance examination and application case studies will be reported at the AGU Meetings.