EMS Annual Meeting Abstracts
Vol. 18, EMS2021-331, 2021
EMS Annual Meeting 2021
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Nowcasting of surface wind speed using probabilistic, explainable Deep Learning

Francesco Zanetta1,2 and Daniele Nerini2
Francesco Zanetta and Daniele Nerini
  • 1ETH Zürich, Atmospheric and Climate Sciences, Environmental Systems Sciences, Switzerland (zanettaf@ethz.ch)
  • 2Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland

Surface wind is an extremely difficult parameter to predict, particularly in the complex topography of the Alps. Due to several important processes happening at sub-kilometer scale, even high resolution Numerical Weather Prediction models such as COSMO-1 still present substantial biases. To address this, a wide range of statistical post-processing methods are used. Recently, methods based on Deep Learning have emerged as a new solution and are now actively developed at many weather services, including MeteoSwiss. At the same time, efforts are made to obtain accurate representations of surface wind speed up to a few hours ahead by integrating all available information in real-time, an approach known as nowcasting.

With the aim of seamlessly combining nowcasting and post-processing approaches for surface wind speed predictions, we developed a Deep Learning probabilistic post-processing model that is also able to integrate real time observations, and developed a new metric, the Similarity Index, for this purpose. The Similarity Index is a way to estimate the correlation of surface wind speed between two locations, based on their position and geomorphological setting, and can be used to choose the best available observation to be used at any point in space at any given time, and weigh that observation in a way that mimics geostatistical interpolation methods. The proposed methodology yields improved forecasts of wind speed where both systematic and random errors are reduced, thanks to the post-processing and nowcasting components respectively. In a second phase, we implemented a state- of-the-art explainability framework for machine learning, SHAP, and presented how it can be used to get insights into the model and build trust in the results.

How to cite: Zanetta, F. and Nerini, D.: Nowcasting of surface wind speed using probabilistic, explainable Deep Learning, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-331, https://doi.org/10.5194/ems2021-331, 2021.


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