Open AccessBenefits of a multimodel ensemble for hub‐height wind prediction in mountainous terrain
Author(s) -
Siuta David M.,
Stull Roland B.
Publication year - 2018
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.2195
Subject(s) - meteorology , north american mesoscale model , terrain , global forecast system , probabilistic logic , environmental science , mesoscale meteorology , numerical weather prediction , forecast verification , wind power forecasting , wind speed , ensemble forecasting , quantitative precipitation forecast , forecast skill , probabilistic forecasting , forecast error , climatology , statistics , econometrics , geography , mathematics , electric power system , precipitation , power (physics) , geology , physics , quantum mechanics , cartography
While numerical weather prediction models can be used to estimate future wind power, no single model is perfect. A better approach is to run many models (an ensemble) and use the average to estimate future wind speeds. The goal of this manuscript is to demonstrate the benefits of using a multimodel ensemble to predict wind speeds at wind‐turbine hub heights. We do this for a 1‐year period at 4 wind farms in mountainous terrain. The ensemble‐mean forecast has higher accuracy than the climatology forecast until a forecast horizon of 6.5 days. The ensemble‐mean forecast has higher correlation to the observations than the climatology forecast has to the observations through the 7‐day forecast horizon tested. Use of the ensemble‐mean forecast results in at least a 1‐ to 2‐day skill advantage (increase in time that a forecast remains more skilled than climatology) over use of a single, deterministic ensemble member for both forecast accuracy and correlation. For probabilistic forecasts, use of the multimodel ensemble mean is most beneficial to improvements in probabilistic sharpness (narrowing of uncertainty). A comparison of Weather Research and Forecasting model forecasts initialized by the National Centers for Environmental Prediction Global Forecast System and North American Mesoscale models, the Canadian Meteorological Centre Global Deterministic Prediction System, and Fleet Numerical Meteorology and Oceanography Center Navy Global Environmental Model showed that the Canadian Meteorological Centre Global Deterministic Prediction System provided the best initial conditions for the locations tested.