Infusing Information from SNPP and GOES-R Observations for Improved Monitoring of Weather, Water and Climate
The first part of our CMORPH development, supported by the NESDIS JPSS program, aims to produce CMORPH satellite estimates for both rainfall and snowfall on a 0.05deg lat/lon grid over the entire globe from the south pole to the north pole through integrating information from SNPP and other satellites as well as CFS reanalysis (CFSR) precipitation fields. A prototype system has been developed to produce the pole-to-pole CMORPH through the Kalman filter technique. First, motion vectors of precipitating clouds are derived from the CFSR precipitation fields and the GEO IR based precipitation estimates. Retrievals of instantaneous precipitation rates from individual LEO platforms including the SNPP are then propagated from their respective observation times to the target analysis time using the motion vectors. The propagated PMW retrievals are then blended with the precipitation estimates derived from the GEO IR data through the Kalman filter framework, in which the propagated PMW and the GEO-IR based estimates are utilized as the prediction and observation, respectively. The above mentioned processing is performed for rainfall and snowfall retrievals, respectively, to improve the representation of snowfall. Experiments are underway to fine tune the system and to perform sensitivity tests to quantify the contributions from the SNPP PMW retrievals.
The second part of our CMORPH improvements, supported by the GOES-R program, intends to improve the CMORPH satellite precipitation estimates through infusing information from the GOES-R observations over the western hemisphere. The GOES-R enhanced CMORPH will be produced on a refined resolution of 2km/15-min at a reduced latency of ~30-min to better satisfy operational requirements. A prototype system has been developed to construct regional CMORPH using the currently operational GOES observations as a placeholder for the GOES-R. GEO IR precipitation estimates are first derived for each satellite pixel. They are then integrated with PMW retrievals from LEO satellites through the Kalman filter framework to form estimates of 15-min mean precipitation. Weights of the PMW retrievals are set as a function of propagation time so that the influence of the PMW observations decreases gradually with the propagation time. Preliminary experiments and tests demonstrated the effectiveness of the GOES-R enhanced CMORPH technique in producing qualitatively consistent precipitation analyses with different input LEO data available at various latency levels. Further work is underway to optimize the regional CMORPH algorithm and to further take advantage of the GEO-based precipitation estimates.
Detailed results will be reported at the AMS meetings.