Enhancing member-by-member post-processing with neural networks

Using post-processing methods to correct systematic errors of ensemble forecasts has become standard practice in research and operations. During recent years, a new focal point of research interest has been the use of modern machine learning methods to allow for more flexible post-processing methods that incorporate additional input predictors. In particular, neural network (NN) models have been shown superior predictive performance in various case studies [1-3].

In contrast, the member-by-member (MBM) post-processing approach [4] adjusts each ensemble member individually using a relatively simple statistical model. This has the advantage that the post-processed ensemble forecasts are not only calibrated, but physically consistent over time, space and different weather variables. Therefore, multivariate dependencies are preserved even if MBM is applied separately for each component. The drawback is that MBM has no straightforward way of incorporating additional input variables (beyond ensemble predictions of the target variable) and therefore typically fails to perform as well as NN-based post-processing approaches [3].

To address this shortcoming, we propose a novel NN-enhanced MBM post-processing approach (“MBM-NN”), which combines the basic idea of MBM with a neural network for incorporating additional predictors to leverage advantages of both approaches. In case studies on probabilistic wind gust forecasting over Germany and on the EUPPBench dataset [5], we demonstrate that the MBM-NN model achieves significant improvements over the standard MBM approach, and reaches comparable performance to state-of-the-art NN-based post-processing models, while retaining multivariate dependencies.

References

[1] Rasp, S. and Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146, 3885-3900

[2] Vannitsem, S., Bremnes, J.B., Demaeyer, J., Evans, G.R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., et al. (2021). Statistical Postprocessing for Weather Forecasts - Review, Challenges and Avenues in a Big Data World. Bulletin of the American Meteorological Society, 102, E681-E699

[3] Schulz, B. and Lerch, S. (2022). Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison. Monthly Weather Review, 150, 235-257

[4] Van Schaeybroeck, B. and Vannitsem, S. (2015). Ensemble post‐processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society 141, 807-818

[5] Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A. Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., et al. (2023). The EUPPBench postprocessing benchmark dataset v1.0. Earth System Science Data, 15, 2635-2653