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

Office of Water Prediction (OWP), National Weather Service (NWS), NOAA, Silver Spring, MD 20910, USA
University Corporation for Atmospheric Research (UCAR), Boulder, CO 80307, USA
University of Texas at Arlington, Arlington, Texas 76019, USA
NOAA/NESDIS/Center for Satellite Applications and Research, College Park, MD 20740, USA
Physical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, CO 80305, USA
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 106;
Received: 25 September 2017 / Revised: 21 November 2017 / Accepted: 10 January 2018 / Published: 13 January 2018
PDF [9090 KB, uploaded 16 January 2018]


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. View Full-Text
Keywords: multi-sensor fusion; satellite; radar; precipitation multi-sensor fusion; satellite; radar; precipitation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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He, Y.; Zhang, Y.; Kuligowski, R.; Cifelli, R.; Kitzmiller, D. Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm. Remote Sens. 2018, 10, 106.

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