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Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones

Author(s): Luitel, Beda; Villarini, Gabriele; Vecchi, Gabriel A

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dc.contributor.authorLuitel, Beda-
dc.contributor.authorVillarini, Gabriele-
dc.contributor.authorVecchi, Gabriel A-
dc.date.accessioned2023-12-11T18:28:15Z-
dc.date.available2023-12-11T18:28:15Z-
dc.date.issued2018-01en_US
dc.identifier.citationLuitel, Beda, Gabriele Villarini, and Gabriel A. Vecchi. "Verification of the skill of numerical weather prediction models in forecasting rainfall from US landfalling tropical cyclones." Journal of Hydrology 556 (2018): 1026-1037. doi:10.1016/j.jhydrol.2016.09.019.en_US
dc.identifier.issn0022-1694-
dc.identifier.urihttps://reader.elsevier.com/reader/sd/pii/S0022169416305704?token=C5D1C41D344834F772B34518968A879AA67EAD489880D6B4F8937D2D9E4A6A9F0C6A479B4A6A4874EC7F25E2EA79C1CD-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr14t6f320-
dc.description.abstractThe goal of this study is the evaluation of the skill of five state-of-the-art numerical weather prediction (NWP) systems [European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UKMO), National Centers for Environmental Prediction (NCEP), China Meteorological Administration (CMA), and Canadian Meteorological Center (CMC)] in forecasting rainfall from North Atlantic tropical cyclones (TCs). Analyses focus on 15 North Atlantic TCs that made landfall along the U.S. coast over the 2007–2012 period. As reference data we use gridded rainfall provided by the Climate Prediction Center (CPC). We consider forecast lead-times up to five days. To benchmark the skill of these models, we consider rainfall estimates from one radar-based (Stage IV) and four satellite-based [Tropical Rainfall Measuring Mission - Multi-satellite Precipitation Analysis (TMPA, both real-time and research version); Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN); the CPC MORPHing Technique (CMORPH)] rainfall products. Daily and storm total rainfall fields from each of these remote sensing products are compared to the reference data to obtain information about the range of errors we can expect from “observational data.” The skill of the NWP models is quantified: (1) by visual examination of the distribution of the errors in storm total rainfall for the different lead-times, and numerical examination of the first three moments of the error distribution; (2) relative to climatology at the daily scale. Considering these skill metrics, we conclude that the NWP models can provide skillful forecasts of TC rainfall with lead-times up to 48 h, without a consistently best or worst NWP model.en_US
dc.format.extent1026 - 1037en_US
dc.language.isoen_USen_US
dc.relation.ispartofJournal of Hydrologyen_US
dc.rightsAuthor's manuscripten_US
dc.titleVerification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclonesen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1016/j.jhydrol.2016.09.019-
dc.date.eissued2016-09-09en_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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