H200-0024
Validation of Real Time Second Generation Pole to Pole CMORPH
Validation of Real Time Second Generation Pole to Pole CMORPH
Wednesday, 16 December 2020
Poster
Abstract:
The second generation CMORPH (CMORPH2) has been developed to produce satellite
derived 30-minute precipitation estimates on a 0.05 o lat/lon grid over the entire globe,
pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm
of Joyce and Xie (2011). Inputs to the system include liquid and frozen precipitation rate
retrievals from passive microwave (PMW) measurements aboard all available low earth
orbit (LEO) satellites, infrared (IR) observations of geostationary (GEO) and LEO
platforms, and model precipitation forecast from the NCEP operational global forecast
system (GFS).
Inputs from the various sources are inter-calibrated to ensure quantitative consistencies in
representing precipitation events of different intensities through PDF calibration against
collocated precipitation retrievals from the Global Precipitation Measurement (GPM)
GMI PMW sensor. The inter-calibrated precipitation estimates are then propagated
forward and backward in time from their respective observation times to target analysis
time, using motion vectors initially derived separately from satellite IR based
precipitation estimates and GFS precipitation fields, then subsequently 2D-VAR
combined to form global analyzed fields of precipitation motion vectors. Propagated
precipitation estimates are finally integrated through the Kalman Filter framework.
A processing system has been constructed to generate the CMORPH2 on a quasi real-
time basis. CMORPH2 estimates are globally compared against CPC daily gauge
analysis, and over CONUS using Stage IV radar precipitation and Multi-Radar/Multi-
Sensor (MRMS), and numerical model forecasts to discover potential shortcomings and
to quantify improvements against first generation CMORPH. Special attention will focus
on CMORPH2 over high-latitude regions beyond the 60N/60S restriction of first
generation CMORPH and NASA IMERG. At the 2020 Fall AGU, performance of
CMORPH2 concerning the quantification of both precipitation detection and amount will
be discussed.
derived 30-minute precipitation estimates on a 0.05 o lat/lon grid over the entire globe,
pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm
of Joyce and Xie (2011). Inputs to the system include liquid and frozen precipitation rate
retrievals from passive microwave (PMW) measurements aboard all available low earth
orbit (LEO) satellites, infrared (IR) observations of geostationary (GEO) and LEO
platforms, and model precipitation forecast from the NCEP operational global forecast
system (GFS).
Inputs from the various sources are inter-calibrated to ensure quantitative consistencies in
representing precipitation events of different intensities through PDF calibration against
collocated precipitation retrievals from the Global Precipitation Measurement (GPM)
GMI PMW sensor. The inter-calibrated precipitation estimates are then propagated
forward and backward in time from their respective observation times to target analysis
time, using motion vectors initially derived separately from satellite IR based
precipitation estimates and GFS precipitation fields, then subsequently 2D-VAR
combined to form global analyzed fields of precipitation motion vectors. Propagated
precipitation estimates are finally integrated through the Kalman Filter framework.
A processing system has been constructed to generate the CMORPH2 on a quasi real-
time basis. CMORPH2 estimates are globally compared against CPC daily gauge
analysis, and over CONUS using Stage IV radar precipitation and Multi-Radar/Multi-
Sensor (MRMS), and numerical model forecasts to discover potential shortcomings and
to quantify improvements against first generation CMORPH. Special attention will focus
on CMORPH2 over high-latitude regions beyond the 60N/60S restriction of first
generation CMORPH and NASA IMERG. At the 2020 Fall AGU, performance of
CMORPH2 concerning the quantification of both precipitation detection and amount will
be discussed.