Journal article
Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones
Journal of hydrology (Amsterdam), Vol.556, pp.1026-1037
01/2018
DOI: 10.1016/j.jhydrol.2016.09.019
Abstract
•The skill of NWP model in forecasting tropical cyclone (TC) rainfall is assessed.•The focus is on 15 North Atlantic TCs.•Five remote-sensing products are used to aid in the forecast evaluation.•NWP models can skillfully forecast TC rainfall with lead-times up to 48h.
The 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 48h, without a consistently best or worst NWP model.
Details
- Title: Subtitle
- Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones
- Creators
- Beda Luitel - University of IowaGabriele Villarini - University of IowaGabriel A Vecchi - NOAA Geophysical Fluid Dynamics Laboratory
- Resource Type
- Journal article
- Publication Details
- Journal of hydrology (Amsterdam), Vol.556, pp.1026-1037
- DOI
- 10.1016/j.jhydrol.2016.09.019
- ISSN
- 0022-1694
- eISSN
- 1879-2707
- Publisher
- Elsevier B.V
- Grant note
- DOI: 10.13039/100000001, name: National Science Foundation, award: AGS-1262091, AGS-1262099; DOI: 10.13039/100000192, name: National Oceanic and Atmospheric Administration Award, award: NA14OAR4830101; name: USACE Institute for Water Resources
- Language
- English
- Date published
- 01/2018
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984197436502771
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