Origins of Heavy Precipitation Biases in the TRMM PR and TMI Products Assessed with CloudSat and Reanalysis Data

Abstract

This study aims to characterize the background physical processes in the development of those heavy precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events from the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and the background environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated precipitation clouds for relatively drier and unstable environments, while PR identifies more organized systems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organization and clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture the eastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments are reversed over tropical land.

Difference Between Cloud Top Height and Storm Height for Heavy Rainfall Using TRMM Measurements

“…In addition, the CAPE and CTH−SH of extreme heavy rain clouds in the Eastern Pacific Ocean are much lower than those in the Western Pacific Ocean. These results are consistent with the relatively suppressed deep convection in the Eastern Pacific in Sekaranom and Masunaga (2019), and can vary with interannual changes of the El Niño Southern Oscillation (Henderson et al 2018). However, the mean CTH−SH of extreme heavy rain clouds in the Eastern Pacific is still higher than that in Korea/Japan because of relatively high CTH in the EP (Fig.…”

“…The regional differences between heavy rain clouds, revealed in previous studies, can be related to problems with estimating satellite precipitation. For example, Sekaranom and Masunaga (2019) showed that the regional difference of PR and TMI rain products (focusing on oceanic and continental convection) occur because PR tends to detect more organized heavy rain systems under humid environments, whereas TMI rainfall is sensitive to deep convective clouds under relatively dry and unstable conditions. Figure 1a shows the average value of TMPA summer precipitation.…”

Inter-product biases in global precipitation extremes

“…Yet, knowledge related to mean inter-product differences are not necessarily applicable to extremes, given that satellite-derived extreme precipitation is often associated with some cloud property (e.g. cloud top height or precipitation water content) rather than the intensity of precipitation itself [9].…”

Comparative Evaluation of the Performances of TRMM-3B42 and Climate Prediction Centre Morphing Technique (CMORPH) Precipitation Estimates over Thailand

“…5a), whereas in other months it mainly underestimated the precipitation intensity with a mean RE of approximately −10%. The types of major rain clouds in different seasons affect the performance of TRMM-3B42v7 precipitation estimate notably: studies have shown that the organized stratiform rain may cause TRMM Microwave Imager (TMI) to overestimate precipitation, while deep-isolated rain may result in underestimation (Sekaranom and Masunaga 2018). Meanwhile, CMORPH underestimated the monthly precipitation in all 12 months (Fig.…”