Seven adaptive approaches to post‐processing wind speed forecasts are discussed and compared. Forecasts of the wind speed over 48 h are run at horizontal resolutions of 7 and 3 km for a domain centred over Ireland. Forecast wind speeds over a 2 year period are compared to observed wind speeds at seven synoptic stations around Ireland and skill scores calculated. Two automatic methods for combining forecast streams are applied. The forecasts produced by the combined methods give bias and root mean squared errors that are better than the numerical weather prediction forecasts at all station locations. One of the combined forecast methods results in skill scores that are equal to or better than all of its component forecast streams. This method is straightforward to apply and should prove beneficial in operational wind forecasting. Copyright © 2011 Royal Meteorological Society

Attention should always be given to which reanalysis dataset to use when preparing analysis for a project. The accuracies of three reanalysis datasets, two global (ERA5 and MERRA-2) and one high-resolution regional reanalysis (MÉRA), are assessed by comparison with observations at seven weather observing stations around Ireland. Skill scores are calculated for the weather variables at these stations that are most relevant to the renewable energy sector: 10 m wind for wind power; surface shortwave radiation (SW) and 2 m temperature for photovoltaic power generation. The choice of which reanalysis dataset to use is important when future planning depends on this data. The newer ERA5 generally outperforms the other two reanalyses. However, this is not always true, and the best performing reanalysis dataset often depends on the variable of interest and location. As errors are significant for these reanalysis datasets, consideration should also be given to datasets specifically tailored to renewable energy resource modelling.