Abstract Author: Pingping Xie, Jian-Yin Liang, Anyuan Xiong, Yan Shen, Mingyue Chen, Robert J. Joyce, John E. Janowiak, and Phillip A. Arkin
Abstract Title: A Prototype Gauge-Satellite Merged Analysis of Daily Precipitation for Improved Real-Time Climate Monitoring
Abstract: A new technique has been developed to create high-resolution, high-quality precipitation analyses on a 0.25olat/lon grid over land by merging gauge-based analysis and satellite estimates. The gauge-based analysis used here is created operationally on a real-time basis at CMA National Meteorological Information Center (NMIC) by interpolating reports of daily precipitation from over 2,400 stations over mainland China using the algorithm of Xie et al. (2006) which takes into account orographic effects on precipitation. The satellite estimates are those generated by the CPC Morphing technique (CMORPH, Joyce et al. 2004) which defines high-resolution precipitation estimates over the globe by morphing the instantaneous precipitation fields (estimated from all available PMW satellite observations ) propagated by the advection vectors derived from consecutive IR images.
A two-step strategy is applied to remove the bias inherent in the CMORPH satellite precipitation estimates and to combine the bias-corrected satellite estimates with the gauge analysis, respectively. First, bias correction is performed for the CMORPH estimates by matching the probability density function (PDF) of the satellite data with that of the gauge analysis. Matching pairs of the gauge and satellite data are collected over grid boxes with at least one gauge over a spatial domain of 10olat/lon centering at the target grid box and over a time period of 30-days ending at the target date. Cumulative PDF functions are then defined for the satellite and gauge data, respectively. Bias in the satellite estimates is finally identified and removed by matching the cumulative PDF of the satellite estimates with that of the gauge analysis.
The bias corrected CMORPH satellite estimates are then combined with the gauge analysis through the optimal interpolation (OI) technique, in which the bias-corrected CMORPH is used as the first guess while the gauge data is used as the observations. Error statistics are computed for the input gauge and satellite data to maximize the performance of the high-resolution merged analysis of daily precipitation.
An automated system is constructed to create merged analysis of daily precipitation on a 0.25olat/lon grid over China on a real-time basis using the algorithm described above. Preliminary inspection of the new analysis showed substantial improvements in the quantitative quality compared to its original inputs. A comprehensive examination is underway. Detailed results will be reported at the conference.