EGU2020-16849
https://doi.org/10.5194/egusphere-egu2020-16849
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Statistical post-processing of wind speed forecasts using convolutional neural networks

Maurice Schmeits1, Simon Veldkamp1,2, and Kirien Whan1
Maurice Schmeits et al.
  • 1KNMI, R&D Weather and climate modelling, De Bilt, Netherlands (schmeits@knmi.nl)
  • 2Mathematical Institute, Utrecht University, Utrecht, Netherlands

Current statistical post-processing methods for providing a probabilistic forecast are not capable of using full spatial patterns from the numerical weather prediction (NWP) model output. Recent developments in deep learning (notably convolutional neural networks) have made it possible to use large gridded input data sets. This could potentially be useful in statistical post-processing, since it allows us to use more spatial information.

In this study we consider wind speed forecasts for 48 hours ahead, as provided by KNMI's Harmonie-Arome model. Convolutional neural networks, fully connected neural networks and quantile regression forests are used to obtain probabilistic wind speed forecasts. Comparing these methods shows that convolutional neural networks are more skillful than the other methods, especially for medium to higher wind speeds.

How to cite: Schmeits, M., Veldkamp, S., and Whan, K.: Statistical post-processing of wind speed forecasts using convolutional neural networks, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16849, https://doi.org/10.5194/egusphere-egu2020-16849, 2020

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  • CC1: Comment on EGU2020-16849, Wentao Li, 08 May 2020

    Hi, the results of CNN look interesting. I have two questions as follows,

    (a)what is the output of CNN look like? The probability of wind exceeding different thresholds?

    (b)why CNN is worse than QRF for the wind of low speed?