Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

Abstract

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003-2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service) operations) over two regions: (I) Inside radar effective coverage and (II) immediately outside radar coverage. The outcomes of the evaluation indicate (a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage.

A Dynamical Z-R Relationship for Precipitation Estimation Based on Radar Echo-Top Height Classification

“…e tests of the three cases (two in summer and one in winter) and two statistics (one in summer and the other one in winter) show that the new Z-R relationship has well performed to derive RQPE. However, the merging results of multiple sources of rainfall rates can provide a better quantity than any single source [38]. Although satellite QPE is limited by a lack of robust correlation between cloud-top brightness temperature and surface rainfall, it is more spatially coherent than radar QPE and is not subject to terrain-based blockages or discontinuities due to lack of data and instrumentation differences [38].…”

“…However, the merging results of multiple sources of rainfall rates can provide a better quantity than any single source [38]. Although satellite QPE is limited by a lack of robust correlation between cloud-top brightness temperature and surface rainfall, it is more spatially coherent than radar QPE and is not subject to terrain-based blockages or discontinuities due to lack of data and instrumentation differences [38]. erefore, future work will focus on the blending of RQPE developed in this paper, satellite rainfall QPE, and rain gauge estimate.…”

Hydrologic Evaluation of Multi-Source Satellite Precipitation Products for the Upper Huaihe River Basin, China

“…To minimize the limitations of individual precipitation products, many researchers focused on merging different precipitation datasets to obtain a higher-quality gridded precipitation product [27][28][29][30][31]. Recently, the global gridded precipitation dataset Multi-Source Weighted-Ensemble Precipitation (MSWEP) that optimally merges gauge, satellite, and reanalysis data has been produced by Beck et al [1,28].…”

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

“…However, WRN-derived rain rates are not always available because radar networks do not supply global coverage in contrast to remote sensing techniques. In order to get around lacks and inadequacies in the radar and gauges coverage, a recent study [16] proposes an algorithm that blends the radar, gauges, and satellite quantitative precipitation estimation over areas immediately outside the effective radar coverage. Moreover, the importance of the fusion between WRN and satellite observations in supporting now-casting is investigated in [17], where measurements from the Chinese operational geostationary satellite Fengyun-2E, providing cloud top information at frequent intervals (<1 h), and ground-based weather radars, have been combined to conduct a study of the isolated convection initiation over central eastern China.…”