Abstract
Abstract
This study demonstrates a framework to improve the skill of raw 10 m wind speed forecasts from numerical models at the subseasonal to seasonal (S2S) time scales. Monthly mean 10 m wind speeds from the ECMWF‐SEAS5 are calibrated using JRA‐55 as reference by employing three statistical methods, bias‐adjustment, quantile‐mapping, and ratio of predictable components (RPC), and four decision tree‐based ML methods, random forest (RF) and light gradient boosting machine (LGBM), RF and LGBM with past observations that is, RF_lags and LGBM_lags respectively, for all 12 months of the year at 1, 2, 3, 4, and 5 months lead time over the homogenous climate zones of India. The quality and skill of raw and calibrated forecasts are evaluated using root mean squared error (RMSE), ratio of standard deviation, and continuous ranked probability skill score (CRPSS). The raw forecasts have large RMSE values, often >1 m/s and mostly do not have any skill. The calibrated forecasts have an RMSE of ∼0.5 m/s, CRPSS ∼0.4, and RMSE ∼0.3 m/s and CRPSS ∼0.7 from statistical and ML‐based methods respectively. The ML‐based methods therefore produce better S2S wind speed forecasts than the statistical methods. This is a timely study with broad impacts especially for the wind energy industry that requires skillful S2S forecasts for financial planning and decision‐making.