Exploiting the predictability of global teleconnections to forecast subseasonal to seasonal scale wind speeds over India

This study explores the potential of slow-varying components of the earth system to predict the monthly mean wind speeds over the seven homogenous climate zones of India at subseasonal to seasonal time-scales. The following set of predictors are selected for that purpose: sea-surface temperature, mean sea-level pressure, 10 m wind speed, wind speed at 850 hPa, and geopotential height at 850 hPa. With the exception of sea-surface temperature which is obtained from HadISST, the rest of the variables are obtained from the JRA55. Besides, the popular indices such as the Nino 3.4 index and the Dipole mode index are also used as predictors. The forecasts are made at 1, 2, 3, 4, and 5 months of leadtime for the monsoon months of June, July, August, and September when the wind speeds are the highest throughout the country. The regions of significant correlations of the predictor fields with the spatially-averaged wind speeds of each homogenous region are determined using the past 6 month lagged composites. Once identified, the variables over these regions are spatially averaged and are mapped to the 10 m wind speeds from JRA55, since it is the closest representation of observed wind speeds over India. This predictor-based forecasting is carried out using the following approaches: multi-linear regression, decision tree based regression, and K nearest neighbours regression. The models use data from 1958-2018 for training and 2019-2021 for testing. The deterministic predictions are evaluated using mean absolute error (MAE) and the skill compared to a climatological forecast is estimated using the root mean squared error skill score (RMSESS). Results show that different sets of predictor combinations are responsible for giving the best forecasts for individual months and leadtimes. These forecasts have MAE of  around 0.2 m/s and RMSESS values ranging from 0.5-0.7. Although we are looking at deterministic predictions here, a combination of multiple models and predictors used above can lead to the production of ensemble forecasts as well, which will be of further added value to the wind energy sector.