Wind speed maps for Austria: An artificial-intelligence approach part 2

The crucial role of wind power in the future energy generation demands to investigate the influence of climate change on wind speed and the associated consequences for wind power. Especially in areas with complex terrain, there are still open research needs for wind energy in future climate (Clifton et al., 2022). To assess future wind climate, one requirement is the availability of a climatological dataset of wind speed with high spatial and temporal resolution to downscale wind speed from global and regional climate models (GCMs, RCMs) to a local scale. So far, no such dataset has been available for Austria. To fill this gap, a wind speed atlas for ideally the past 30 years (1991 – 2020) is compiled for the diverse landscape of Austria, having flat and mountainous terrain, using a set of artificial intelligence (AI) methods.

A comparison of gridded wind speed data considering three baseline methodologies will be presented: The ‘Integrated Nowcasting through Comprehensive Analysis’ (INCA) model (Haiden et al., 2011) combining ERA5 and station observations, a generalized additive regression model (GAM) and a deep neural network (DNN) model with adapted loss function. All approaches are used to create a wind speed analysis for the years 1991 to 2020 with a spatial resolution of 1 km to 1 km for the territory of Austria. Furthermore, they consider the same station-observation dataset of 10-m wind speed measurements of the national weather service in Austria. Last year’s shown first results of the GAM and DNN baseline were promising. The uncertainty in interpolation given through the methodologies is, so far, within the expected range. This year, preliminary final results of the gridding and validation of the different methods used will be presented.

The availability of a wind atlas for Austria allows downscaling of climate projections and thereby the investigation of the climate change impact on future wind speeds and future wind power potential in Austria.

References:

Clifton, A., Barber, S., Stökl, A., Frank, H., and Karlsson, T.: Research challenges and needs for the deployment of wind energy in hilly and mountainous regions, Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, 2022.

Haiden T, Kann A, Wittmann C, Pistotnik G, Bica B, Gruber C. 2011. The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region. Weather and Forecasting, 26/2, 166-183, doi: 10.1175/2010WAF2222451.1