Improving Satellite Quantitative Precipitation Estimation Using GOES-Retrieved Cloud Optical Depth

Abstract

o address gaps in ground-based radar coverage and rain gauge networks in the United States, geostationary satellite quantitative precipitation estimation (QPE) such as the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) can be used to fill in both spatial and temporal gaps of ground-based measurements. Additionally, with the launch of Geostationary Operational Environmental Satellite R series (GOES-R), the temporal resolution of satellite QPEs may be comparable to Weather Surveillance Radar-1988 Doppler (WSR-88D) volume scans as GOES images will be available every 5 min. However, while satellite QPEs have strengths in spatial coverage and temporal resolution, they face limitations, particularly during convective events. Deep convective systems (DCSs) have large cloud shields with similar brightness temperatures (BTs) over nearly the entire system, but widely varying precipitation rates beneath these clouds. Geostationary satellite QPEs relying on the indirect relationship between BTs and precipitation rates often suffer from large errors because anvil regions (little or no precipitation) cannot be distinguished from rain cores (heavy precipitation) using only BTs. However, a combination of BTs and optical depth τ has been found to reduce overestimates of precipitation in anvil regions. A new rain mask algorithm incorporating both τ and BTs has been developed, and its application to the existing SCaMPR algorithm was evaluated. The performance of the modified SCaMPR was evaluated using traditional skill scores and a more detailed analysis of performance in individual DCS components by utilizing the Feng et al. classification algorithm. SCaMPR estimates with the new rain mask benefited from significantly reduced overestimates of precipitation in anvil regions and overall improvements in skill scores.