A platform for research: civil engineering, architecture and urbanism
Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates
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.
Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates
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.
Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates
Teegavarapu, Ramesh S. V. (author) / Goly, Aneesh (author) / Wu, Qinglong (author)
2015-07-22
Article (Journal)
Electronic Resource
Unknown
Comprehensive Framework for Assessment of Radar-Based Precipitation Data Estimates
Online Contents | 2017
|Quality assessment of radar-based precipitation estimates with the example of a small catchment
Online Contents | 2009
|Strategy for Utilizing Radar-Based Precipitation Estimates for River Forecasting
British Library Conference Proceedings | 1993
|Adjustment of Radar-Based Precipitation Estimates for Great Lakes Hydrologic Modeling
Online Contents | 2007
|Adjustment of Radar-Based Precipitation Estimates for Great Lakes Hydrologic Modeling
British Library Online Contents | 2007
|