text: 491,105,1513811805906382853,1214911964356452353,Sebastian Lerch,"New paper ""Convolutional autoencoders for spatially-informed ensemble post-processing"" accepted at the AI for Earth and Space Science Workshop at #ICLR2022 - available at <LINK>. Joint work with @AstroInformatix 🧵 Motivation: Station-based post-processing models require localized predictors interpolated from the NWP model's spatial forecast fields to the target locations. Predictability information contained in large-scale spatial structures is potentially lost in this interpolation step. <LINK> We propose the use of convolutional autoencoders to learn compact representations of spatial input fields which can then be used to augment location-specific information as additional inputs to post-processing models. <LINK> Convolutional autoencoders are applied to spatial forecast fields of different variables, and generally do a good job at reconstructing the mean forecast fields. <LINK> Using AE representations as additional inputs improves the performance of post-processing models, but the results vary substantially across variables and dimensionality of the latent representations. <LINK> @vitusbenson Thanks! This was done by others for example here: <LINK>. We mainly wanted to try an alternative approach that appeared to be computationally more affordable.",https://arxiv.org/abs/2204.05102,"Ensemble weather predictions typically show systematic errors that have to be corrected via post-processing. Even state-of-the-art post-processing methods based on neural networks often solely rely on location-specific predictors that require an interpolation of the physical weather model's spatial forecast fields to the target locations. However, potentially useful predictability information contained in large-scale spatial structures within the input fields is potentially lost in this interpolation step. Therefore, we propose the use of convolutional autoencoders to learn compact representations of spatial input fields which can then be used to augment location-specific information as additional inputs to post-processing models. The benefits of including this spatial information is demonstrated in a case study of 2-m temperature forecasts at surface stations in Germany. ","Convolutional autoencoders for spatially-informed ensemble