Spatial representation learning for ensemble weather simulations using invariant variational autoencoders

Weather forecasts today are typically issued in the form of ensemble simulations based on multiple runs of numerical weather prediction models with different perturbations in the initial states and the model physics. In light of the continuously increasing spatial resolutions of operational weather models, this results in large, high-dimensional datasets that nonetheless contain relevant spatial and temporal structure, as well as information about the predictive uncertainty. We propose invariant variational autoencoder (iVAE) models based on convolutional neural network architectures to learn low-dimensional representations of the spatial forecast fields. We specifically aim to account for the ensemble character of the input data and discuss methodological questions about the optimal design of suitable dimensionality reduction methods in this setting. Thereby, our iVAE models extend previous work where low-dimensional representations of single, deterministic forecast fields were learned and utilized for incorporating spatial information into localized ensemble post-processing methods based on neural networks [1], which were able to improve upon model utilizing location-specific inputs only [2]. By additionally incorporating the ensemble dimension and learning representation for probability distributions of spatial fields, we aim to enable a more flexible modeling of relevant predictive information contained in the full forecast ensemble. Additional potential applications include data compression and the generation of forecast ensembles of arbitrary size.

We illustrate our methodological developments based on a 10-year dataset of gridded ensemble forecasts from the European Centre for Medium-Range Weather Forecasts of several meteorological variables over Europe. Specifically, we investigate alternative model architectures and highlight the importance of tailoring the loss function to the specific problem at hand.

References:

[1] Lerch, S. & Polsterer, K.L. (2022). Convolutional autoencoders for spatially-informed ensemble post-processing. ICLR 2022 AI for Earth and Space Science Workshop, https://arxiv.org/abs/2204.05102.

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