Journal article
Modeling and predicting complex space–time structures and patterns of coastal wind fields
Environmetrics, Vol.16(5), pp.449-449
08/01/2005
DOI: 10.1002/env.714
Abstract
A statistical technique is developed for wind field mapping that can be used to improve either the assimilation of surface wind observations into a model initial field or the accuracy of post‐processing algorithms run on meteorological model output. The observed wind field at any particular location is treated as a function of the true (but unknown) wind and measurement error. The wind field from numerical weather prediction models is treated as a function of a linear and multiplicative bias and a term which represents random deviations with respect to the true wind process. A Bayesian approach is taken to provide information about the true underlying wind field, which is modeled as a stochastic process with a non‐stationary and non‐separable covariance. The method is applied to forecast wind fields from a widely used mesoscale numerical weather prediction (NWP) model (MM5). The statistical model tests are carried out for the wind speed over the Chesapeake Bay and the surrounding region for 21 July 2002. Coastal wind observations that have not been used in the MM5 initial conditions or forecasts are used in conjunction with the MM5 forecast wind field (valid at the same time that the observations were available) in a post‐processing technique that combined these two sources of information to predict the true wind field. Based on the mean square error, this procedure provides a substantial correction to the MM5 wind field forecast over the Chesapeake Bay region. Copyright © 2005 John Wiley & Sons, Ltd.
Details
- Title: Subtitle
- Modeling and predicting complex space–time structures and patterns of coastal wind fields
- Creators
- Montserrat Fuentes - North Carolina State UniversityLi ChenJerry M DavisGary M Lackmann
- Resource Type
- Journal article
- Publication Details
- Environmetrics, Vol.16(5), pp.449-449
- Publisher
- John Wiley & Sons, Ltd; Chichester, UK
- DOI
- 10.1002/env.714
- ISSN
- 1180-4009
- eISSN
- 1099-095X
- Number of pages
- 16
- Grant note
- National Science Foundation (DMS 0002790; DMS 0353029) U.S. Defense Threat Reduction Agency
- Language
- English
- Date published
- 08/01/2005
- Academic Unit
- Biostatistics; Provost Office Administration; Statistics and Actuarial Science
- Record Identifier
- 9983763497502771
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