Understanding the Sources of Satellite Passive Microwave Rainfall Retrieval Systematic Errors Over Land

Abstract

Analyses of the Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates reveal a substantial disagreement between its active [Precipitation Radar (PR)] and passive [TRMM Microwave Imager (TMI)] sensors over certain regions. This study focuses on understanding the role of the synoptic state of atmosphere in these discrepancies over land regions where passive microwave (PMW) retrievals are limited to scattering signals. As such the variability in the relationship between the ice-induced scattering signal and the surface rainfall is examined. Using the Amazon River and central Africa regions as a test bed, it is found that the systematic difference seen between PR and TMI rainfall estimates is well correlated with both the precipitating system structure and the level of its organization. Relying on a clustering technique to group raining scenes into three broad but distinct organizational categories, it is found that, relative to the PR, deep-organized systems are typically overestimated by TMI while the shallower ones are underestimated. Results suggest that the storm organization level can explain up to 50% of the regional systematic difference between the two sensors. Because of its potential for retrieval improvement, the ability to forecast the level of systems organization is tested. The state of the atmosphere is found to favor certain storm types when constrained by CAPE, wind shear, dewpoint depression, and vertical humidity distribution. Among other findings, the observations reveal that the ratio between boundary layer and midtropospheric moisture correlates well with the organization level of convection. If adjusted by the observed PR-to-TMI ratio under a given environment, the differences between PMW and PR rainfall estimates are diminished, at maximum, by 30% in RMSE and by 40% in the mean.

Can the GPM IMERG Final Product Accurately Represent MCSs’ Precipitation Characteristics over the Central and Eastern United States?

“…For satellite-based precipitation estimation, passive microwave (PMW) and IR sensors are widely used. IR temperature is used to estimate rainfall based on the indirect relationship between cloud top temperature/height and surface precipitation (Joyce et al 2004) that is rather ambiguous (Petković and Kummerow 2017). PMW sensors at different wavelengths (e.g., 19 and 85 GHz) retrieve rainfall by employing both absorption and scattering properties of hydrometeors to the observed radiances at the top of the atmosphere (Cui et al 2016;Petković and Kummerow 2017).…”

“…IR temperature is used to estimate rainfall based on the indirect relationship between cloud top temperature/height and surface precipitation (Joyce et al 2004) that is rather ambiguous (Petković and Kummerow 2017). PMW sensors at different wavelengths (e.g., 19 and 85 GHz) retrieve rainfall by employing both absorption and scattering properties of hydrometeors to the observed radiances at the top of the atmosphere (Cui et al 2016;Petković and Kummerow 2017). Nevertheless, precipitation studies related to mesoscale processes, particularly those associated with MCSs, have rarely been performed using the passive satellite-only precipitation products due to various issues including but not limited to coarse spatial and temporal resolutions, inconsistency in data availability, and errors in raw measurements.…”

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

“…Over land, Zagrodnik and Jiang (2013) showed that TMI has an issue with higher rain-rate retrieval. Petković and Kummerow (2017) found that the frequency of occurrence for specific rainfall regimes containing different profiles of ice particles was essential for describing TMI-PR rainfall differences. The TMI estimation depends mostly on 85-GHz channels over land, which is more sensitive to ice particles (Gopalan et al 2010;Liu and Zipser 2014).…”

Tropical Cyclone Rainfall Estimates from FY-3B MWRI Brightness Temperatures Using the WS Algorithm

“…Consequently, based on the WS algorithm, an algorithm for retrieving rain rates inside TCs by using MWRI 19 and 37 GHz Tbs is proposed. Since the upwelling radiation at higher frequencies is strongly affected by ice scattering, the retrieval uses lower frequencies where radiation is absorbed and re-emitted by liquid hydrometeors to obtain the information of the column-integrated liquid water [47]. High rain intensities not only change the atmospheric attenuation but also influence the sea surface dielectric properties in a complicated manner [48].…”