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. This study evaluates subseasonal to seasonal scale (S2S)forecasts of meteorological variables relevant for the renewable energy (RE)sector of India from six ocean-atmosphere coupled models: ECMWF SEAS5, DWDGCFS 2.0, Météo-France's System 6, NCEP CFSv2, UKMO GloSea5 GC2-LI,and CMCC SPS3. The variables include 10 m wind speed, incoming solarradiation, 2 m temperature, and 2 m relative humidity because they arecritical for estimating the supply and demand of renewable energy. The studyis conducted over seven homogenous regions of India for 1994–2016. Thetarget months are April and May when the electricity demand is the highestand June–September when the renewable resources outstrip the demand. Theevaluation is done by comparing the forecasts at 1, 2, 3, 4, and 5-monthslead-times with the ERA5 reanalysis spatially averaged over each region. Thefair continuous ranked probability skill score (FCRPSS) is used toquantitatively assess the forecast skill. Results show that incoming surfacesolar radiation predictions are the best, while 2 m relative humidity is theworst. Overall SEAS5 is the best performing model for all variables, for alltarget months in all regions at all lead times while GCFS 2.0 performs theworst. Predictability is higher over the southern regions of the countrycompared to the north and north-eastern parts. Overall, the quality of theraw S2S forecasts from numerical models over India are not good. Theseforecasts require calibration for further skill improvement before beingdeployed for applications in the RE sector.

Evaluation of subseasonal to seasonal forecasts over India for renewable energy applications