ESRL/PSD Seminar Series

CMORPH global high-resolution satellite precipitation estimates have been reprocessed and bias-corrected for a 15-year period from January 1998 to the present to cover the entire TRMM/GPM era. As the first step of this project, the CMORPH estimates have been extended backward in time from the December 2002 operational initiation to January 1998 and reprocessed from 2003 to the present using the most recent passive microwave (PMW) retrieval algorithm version from all available low earth orbiters and infrared (IR) observations from geostationary platforms.

Bias correction is then performed for the raw CMORPH over the entire data period from 1998 to the present. Over land, the bias in the raw CMORPH is removed by matching the probability density function (PDF) of the CMORPH with that of the CPC unified daily gauge analysis in two sequential steps. Over ocean, the raw CMORPH satellite estimates are calibrated against a long-term precipitation analysis (pentad GPCP) to ensure temporal homogeneity for climate applications. The reprocessed, bias-corrected CMORPH estimates present a 15-year homogeneous record of high-resolution precipitation on an 8kmx8km and 30-min resolution covering the globe from 60oS-60oN.

Bias corrected CMORPH is applied to examine the large-scale precipitation variations over the globe. In particular, diurnal variations of precipitation is investigated and compared with that captured by the three new generation reanalyses (CFSR, MERRA, and ERA-Interim). While the reanalyses tend to over-estimate the total precipitation, the magnitude of the diurnal cycle is smaller than that in the bias-corrected CMORPH over most of the global regions. Detailed results will be illustrated at the seminar.

At the end of the seminar, new developments in the area of creating a regional CMORPH with refined resolution and reduced latency through enhancement from GOES-R and constructing high-resolution precipitation analyses through fusion with gauge measurements, radar estimates and numerical model forecasts will be introduced to discussions with our colleagues in Boulder.