Use of TRMM precipitation radar for calibrating overland passive microwave rain retrieval

The precipitation radar (PR) on board the TRMM satellite provides definitive measurements of the 3D structure of precipitation. However, its narrow swath (215km) limits the use of this dataset. On the other hand, TMI and SSM/I provide wider swath coverage (760 km for TMI and 1400 km SSM/I) and higher sampling frequency. Thus, combining the higher accuracy of PR with the better spatial coverage and sampling frequency of TMI and SSM/I would be of great value in a number of applications in meteorology, hydrology, and water resources. One approach to do this is using PR to calibrate the passive microwaves (PM) data. Relationship between PR and PM may vary from one rainfall regime to another and from one season to another within the same regime. The goal of this research is to develop a PR-calibrated TMI (PR-TMI) overland rain retrieval algorithm, investigate its regional and seasonal differences, and explore possibilities of extending the PR-TMI algorithm to SSM/I calibration. Application of the PR-TMI algorithm to SSM/I is particularly important because SSM/I has long historical data going back to 1987 and better sampling due to the more frequent overpasses and wider swath. The PR-TMI algorithm developed here consists of rain screening, convective/stratiform rain classification, and non-linear (linear) regressions for stratiform (convective) rain retrievals. Four geographic regions from Central Africa (AFC), Amazon (AMZ), continental US (USA), and South Asia (SAS) are selected for these investigations. The algorithm developed here outperformed the TRMM-2A12 V6 product with significant decrease in both random and systematic errors. Regional calibration performs slightly better than the global calibration. However, the differences are not significant particularly for AFC, AMZ and USA regions. Comparisons of individual season calibrations with the annual calibration did not show significant differences either. However, it was observed that the performance of the PM algorithm varies among the different seasons. Comparison of the current SSM/I rain estimates with that of GPROF has shown that the current algorithm performs better with very significant reduction in bias and random errors.