Bridging the Sensitivity Gap in Precipitation Estimates from Spaceborne Radars using Passive Microwave Observations

TL;DR

This paper presents a new passive microwave precipitation retrieval method that combines radar data to improve high-latitude and frozen precipitation estimates, addressing sensitivity limitations of current spaceborne radars.

Contribution

It introduces GPROF-NN XPR, a fusion-based retrieval that leverages cloud and precipitation radar data to enhance precipitation estimates across regimes.

Findings

26% improvement in high-latitude precipitation detection

Over 50% reduction in underestimation of frozen precipitation

Fusion scheme provides more consistent estimates across regimes

Abstract

Current global precipitation estimates from spaceborne precipitation radars are limited by their sensitivity to light and frozen precipitation, leading to systematic underestimation of precipitation at high latitudes. Because passive microwave retrievals (PMW) are commonly trained using these radar observations as reference data, this limitation is propagated into PMW This study introduces a novel PMW oceanic precipitation retrieval, GPROF-NN eXtended Precipitation Regime (XPR), that combines reference estimates from a cloud radar and a precipitation radar to overcome the sensitivity limitations of current spaceborne precipitation radars. The retrieval is trained to estimate light precipitation from CloudSat observations and moderate-to-heavy precipitation using observations from the GPM Dual-Frequency Precipitation Radar. The two estimates are combined using a fusion scheme to obtain a consistent precipitation estimate across precipitation regimes. Validation against in situ measurements from shipborne disdrometers shows a 26% improvement in the detection skill for high-latitude precipitation in terms of the critical success index and a reduction in the underestimation of high-latitude and frozen precipitation by more than 50% compared to retrievals constrained only by precipitation radar data. However, the fused retrieval does not improve the precision of instantaneous precipitation estimates, which is likely due to significant random errors in the CloudSat-based reference estimates of liquid precipitation. These results demonstrate that PMW retrievals can leverage the complementary sensitivities of cloud and precipitation radars to provide more consistent precipitation estimates across precipitation regimes than either reference instrument alone. The proposed retrieval provides a pathway to improve the representation of oceanic precipitation in future GPM precipitation products.