Detecting Beam Blockage in Radar-Based Precipitation Estimates

7.2 Radar Precipitation Estimates in Mountainous Regions: Corrections for Partial Beam Blockage and General Radar Coverage Limitations

Generating Multi-Sensor Precipitation Estimates Over Radar Gap Areas

Generating a multi-sensor precipitation product over radar gap area is the objective of the present study. A merging approach is developed to improve Satellite-based Precipitation Estimates (SPE) by merging with ground-based Radar Rainfall (RR) estimates because remote satellites are the only source that can collect information from areas where are inaccessible by ground-based radar and/or rain...

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

Yuxiang He 1,2,* ID , Yu Zhang 3, Robert Kuligowski 4, Robert Cifelli 5 and David Kitzmiller 1 1 Office of Water Prediction (OWP), National Weather Service (NWS), NOAA, Silver Spring, MD 20910, USA; [email protected] 2 University Corporation for Atmospheric Research (UCAR), Boulder, CO 80307, USA 3 University of Texas at Arlington, Arlington, Texas 76019, USA; [email protected] 4 NOAA/NE...

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

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 estimate...

Intercomparison of Rain Gauge, Radar, and Satellite-Based Precipitation Estimates with Emphasis on Hydrologic Forecasting

This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellitebased system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven o...