Journal
JOURNAL OF HYDROLOGY
Volume 399, Issue 3-4, Pages 281-298Publisher
ELSEVIER
DOI: 10.1016/j.jhydrol.2011.01.013
Keywords
Ensemble forecast; Precipitation; Bivariate meta-Gaussian distribution; Normal quantile transform; Statistical analysis
Funding
- National Weather Service (NWS)
- Climate Program Office (CPO)
- National Oceanic and Atmospheric Administration (NOAA)
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Reliable and skillful precipitation ensemble forecasts are necessary to produce reliable and skilful hydrologic ensemble forecasts. It is well known that, in general, raw precipitation ensemble forecasts from the numerical weather prediction (NWP) models are not very reliable and that, for short-range prediction, human forecasters add significant skill to the NWP-generated single-valued quantitative precipitation forecasts (QPF). In this paper, we describe and evaluate a statistical procedure for producing precipitation ensemble forecasts from single-valued QPFs. The procedure is based on the bivariate probability distribution between the observed precipitation and the single-valued QPF. The distribution is modeled as a mixed-type in which the relationship between the positive observed precipitation and positive forecast precipitation is assumed to be bivariate meta-Gaussian. We also describe and comparatively evaluate a generalized meta-Gaussian model in which the model parameter is optimized by minimizing the mean Continuous Ranked Probability Score. The performance of these procedures is assessed through dependent and cross validation using data for selected river basins in the service areas of the Arkansas-Red Basin, California-Nevada and Middle-Atlantic River Forecast Centers of the National Weather Service. The validation results show that, overall, the precipitation ensembles generated by the proposed procedures are reliable and capture the skill in the conditioning single-valued forecasts very well. (C) 2011 Elsevier B.V. All rights reserved.
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