Improving the Accuracy of Satellite Rainfall Estimates via Large-scale Environment

Satellite rainfall estimates are invaluable in assessing global precipitation. As a part of the Global Precipitation Measurement (GPM) mission, a constellation of orbiting sensors, dominated by passive microwave imagers, provides a full coverage of the planet approximately every 2-3 hours. Several decades of development have resulted in passive microwave rainfall retrievals that are indispensable in addressing global precipitation climatology. However, this prominent achievement is often overshadowed by the retrieval’s performance at finer spatial and temporal scales, where large variability in clouds morphology poses an obstacle for accurate rainfall measurements. This is especially the case over land, where rainfall estimates are based on an observed mean relationship between high frequency (e.g., 89 GHz) brightness temperature (Tb) depression (i.e., ice scattering signature) and rainfall rate.

To address this issue, in its first part, this study utilizes TRMM radar (PR) and radiometer (TMI) observations to first confirm that Tb-to-rain-rate relationship is governed by the amount of ice in the atmospheric column. Next, using the Amazon and Central African regions as a testbed, it demonstrates that amount of ice aloft is strongly linked to a precipitation regime. A correlation found between the large-scale environment and precipitation regimes is then further examined. Variables such as CAPE, wind shear, and vertical humidity profiles are found to be capable of predicting a precipitation regime and explaining up to 40% of climatological biases. Dry over moist air conditions show as favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel. These systems are characterized by strong Tb depressions and above the average amounts of ice aloft. As a consequence, microwave retrieval algorithms misinterpret these non-typical systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates.

Based on these findings, in the second part of the study, GPM operational retrieval (GPROF) for GMI sensor is modified to allow for both observed vector and a priori database to carry information on atmospheric conditions. When forming an estimate, the algorithm uses this information to filter out a priori content not relevant to the observation and reduce the bias. The results are compared to the ground multi-radar multi-sensor (MRMS) network over the US at various spatial and temporal scales, with the goal to demonstrate potentials in improving the accuracy of rainfall estimates from satellite-borne passive microwave sensors over land.