Open AccessDevelopment of a geophysic model output statistics module for improving short‐term numerical wind predictions over complex sites
Author(s) -
Bédard Joël,
Yu Wei,
Gag Yves,
Masson Christian
Publication year - 2013
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.1538
Subject(s) - meteorology , numerical weather prediction , terrain , term (time) , grid , mean squared error , global forecast system , weather research and forecasting model , environmental science , forecast skill , forecast verification , model output statistics , representation (politics) , climatology , statistics , mathematics , geography , geology , geodesy , cartography , physics , quantum mechanics , politics , political science , law
Developed for short‐term (0–48 h) wind power forecasting purposes, high‐resolution meteorological forecasts for Eastern Canada are available from Environment Canada's Numerical Weather Prediction (NWP) model configured on a limited area (GEM‐LAM). This paper uses 3 years of forecast data from this model for the region of North Cape (Prince Edward Island, Canada). Although the model resolution is relatively high (2.5 km), statistical analysis and site inspection reveal that the model does not have a sufficiently refined grid to properly represent the meteorological phenomena over this complex coastal site. To cope with such representation error, a generalized Geophysic Model Output Statistics (GMOS) module is developed and applied to reduce the forecast error of the NWP forecasts. GMOS differs from other Model Output Statistics (MOS) that are widely used by meteorological centres in the following aspects: (i) GMOS takes into account the surrounding geophysical parameters such as surface roughness, terrain height, etc., along with wind direction; (ii) GMOS can be directly applied for model output correction without any training. Compared with other methods, GMOS using a multiple grid point approach improves the GEM‐LAM predictions root mean squared error by 1–5% for all time horizons and most meteorological conditions. Also, the topographic signature of the forecast error (uneven directional distribution of the forecast error related to the surface characteristics) due to misrepresentation issues is significantly reduced. The NWP forecasts combined with GMOS outperform the persistence model from a 2 h horizon, instead of 3 h using MOS. Finally, GMOS is applied and validated at two other sites located in New Brunswick, Canada. Similar improvements on the forecasts were observed, thus showing the general applicability of GMOS. Copyright © 2012 John Wiley & Sons, Ltd.