contributor author | Ramesh S. V. Teegavarapu | |
contributor author | Aneesh Goly | |
contributor author | Qinglong Wu | |
date accessioned | 2017-05-08T22:25:24Z | |
date available | 2017-05-08T22:25:24Z | |
date copyright | May 2017 | |
date issued | 2017 | |
identifier other | 44399079.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/80355 | |
description abstract | Assessment of radar-based precipitation estimates using rain gauge observations is a critical exercise in evaluating pre-and postcorrected (gauge-adjusted) radar-based precipitation data. A comprehensive assessment framework combining several visual, quantitative, and statistical measures, indexes, and skill scores is proposed and developed for evaluation of radar-based precipitation estimates in space and time. Contingency measures, skill scores, and a few new metrics are proposed and are evaluated along with several indexes. Visual measures provide a quick check of agreement between radar and rain gauge data sets. Quantitative measures provide information about errors, and skill scores assess the quality of radar data for dichotomous (rain and no-rain) events. Summary statistics and hypothesis tests in statistical categories provide insights into distributional aspects of the rain gauge and radar data sets. The framework is used for evaluation of 15-min radar-based precipitation data obtained from the South Florida Water Management District (SFWMD). Four years of radar and rain gauge data available at 189 sites are used for analysis. Results suggest that radar data in the SFWMD region have progressively improved during the period of analysis. All indexes and skill scores used in the current study suggest that radar data are of good quality at different temporal resolutions and in agreement with rain gauge data. However, spatial bias evaluation suggests that radar data underestimate precipitation amounts in two areas of the SFWMD region. | |
publisher | American Society of Civil Engineers | |
title | Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates | |
type | Journal Paper | |
journal volume | 22 | |
journal issue | 5 | |
journal title | Journal of Hydrologic Engineering | |
identifier doi | 10.1061/(ASCE)HE.1943-5584.0001277 | |
tree | Journal of Hydrologic Engineering:;2017:;Volume ( 022 ):;issue: 005 | |
contenttype | Fulltext | |