Integrating Analog Methods with Other Machine Learning Techniques to Further Enhance Wind Speed Forecasting

NWP models have been crucial for modern weather forecasting for a long time. Although their skill is improving, the errors they exhibit can still be substantial. The additional forecast improvement is often obtained by applying statistical and machine learning (ML) post-processing techniques, especially for the locations where the measurements are available. Post-processing of wind speed using the analog-based method has been successfully implemented and analyzed at DHMZ for a long time. But considering the relatively recent surge of various other machine learning techniques, the next logical step is to combine  the analog method  with them to bring further improvements. In this work, we are trying to determine whether the output of the analog method can successfully be used as an input of deep-learning and gradient-boosting methods in order to benefit from both approaches.

The raw NWP model used in this work is the ALADIN model with 2 and 4 km horizontal resolutions. The analog-based method is first applied to raw NWP, where the method includes weight optimization and the correction for more extreme values, as well as the novel variation with varying ensemble sizes. Then, the deep-learning and gradient-boosting methods are fed with both analog ensemble and raw NWP. Besides using all the analog ensemble members, the analog ensemble can also be characterized by the descriptive values, which therefore reduces the number of machine learning predictors. The forecasts are verified against the wind speed measurements across the Republic of Croatia.

The continuous and categorical approaches are used for the verification of the hourly wind speed forecasts. In the categorical approach, verification is also performed for both common and more extreme events. Additionally, verification is also performed for different types of stations. Results show a clear benefit of using the analog method as a generator of additional ML predictors to raw NWP.  The exact improvement is dependent on the type of predictors used in the process. Finally, the results can be fine-tuned, depending on the main goal. Since the extreme events are particularly hard to predict, the variation which is able to further improve the performance for such events is emphasized.

In conclusion, this work demonstrates the potential of integrating analog methods with machine learning techniques to improve wind speed forecasting. By combining the strengths of both approaches, the enhancements in forecast accuracy are achieved, even for extreme events. These findings underscore the importance of exploring novel methodologies to advance weather prediction capabilities and mitigate the impact of severe weather events.