Open AccessTuning Extreme NEXRAD and CMORPH Precipitation Estimates
Author(s) -
Jonathan Woody,
Robert Lund,
Mekonnen Gebremichael
Publication year - 2014
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm-d-13-0146.1
Subject(s) - environmental science , precipitation , meteorology , quantitative precipitation estimation , quantile , hydrometeorology , generalized pareto distribution , radar , climatology , extreme value theory , computer science , geology , mathematics , geography , statistics , telecommunications
High-resolution satellite precipitation estimates, such as the Climate Prediction Center morphing technique (CMORPH), provide alternative sources of precipitation data for hydrological applications, especially in regions where adequate ground-based instruments are unavailable. These estimates are, however, subject to large errors, especially at times of heavy precipitation. This paper presents a method to distributionally convert a set of CMORPH estimates into ground-based Next Generation Weather Radar (NEXRAD) estimates. As our concern lies with floods and extreme precipitation events, a peaks-over-threshold extreme value approach is adopted that fits a generalized Pareto distribution to the large precipitation estimates. A quantile matching transformation is then used to convert CMORPH values into NEXRAD values. The methods are applied in the analysis of 6 yr of precipitation observations from 625 pixels centered around eastern Oklahoma.