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

Abstract

To 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.

A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF

“…Feng et al (2012) also found that the CR rain rate is almost an order of magnitude higher than SR, causing a surge in accumulated precipitation within a short time period and possibly resulting in flooding events. Differences in statistical characteristics of CR and SR have been investigated through a variety of datasets, including space-borne satellite observations [e.g., Tropical Rainfall Measuring Mission (Yang and Smith 2006); GOES (Behrangi et al 2009)], groundbased radar observations [e.g., National Mosaic and Multi-Sensor Quantitative Precipitation Estimation (Stenz et al 2014(Stenz et al , 2016Feng et al 2011Feng et al , 2012], direct surface rain gauge measurements (Giangrande et al 2014;Wu et al 2013;Tao et al 2013), and aircraft in situ measurements (Beard et al 1986;Wang et al 2015Wang et al , 2016Wang et al , 2018.…”

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

“…IR imagery is associated with cloud top temperatures and cloud growth rates can be obtained via the thermal emissions during both night and day; generally, heavier rainfall tends to be associated with larger, taller clouds with colder cloud tops [5]. Many researchers have developed different methods to retrieve precipitation based on VIR/IR data from geosynchronous earth orbit (GEO) and low-earth orbit (LEO) satellites, including the 3-hourly and monthly mean rainfalls [6][7][8][9][10]. MW sensors can detect rain clouds directly and can provide information about the atmospheric constituents and hydro-meteorological profiles, which are more directly related to the ground precipitation rate [11,12].…”

Investigation of Liquid Cloud Microphysical Properties of Deep Convective Systems: 2. Parameterization of Raindrop Size Distribution and its Application for Convective Rain Estimation

“…The lack of large spatial coverage and continuous CR observation can be complemented by satellite remote sensing. However, the uncertainties of satellite‐retrieved precipitation properties (e.g., precipitation type and intensity) remain large especially for the optically thick clouds (Stenz et al, , ) because the passive satellite retrievals are more or less representative of cloud top properties, not entire column information.…”