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 (c) 2005 John Wiley & Sons, Ltd.
|Translated title of the contribution||Modeling and predicting complex space-time structures and patterns of coastal wind fields|
|Pages (from-to)||449 - 464|
|Number of pages||16|
|Publication status||Published - Aug 2005|