text:   post-processing",6,"['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. https://t.co/yPk5wg9wVK"", '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. https://t.co/en6AmO8pMj', 'Convolutional autoencoders are applied to spatial forecast fields of different variables, and generally do a good job at reconstructing the mean forecast fields. https://t.co/8aHbXTeQrI', '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. https://t.co/AMVxnnpdES', '@vitusbenson Thanks! This was done by others for example here: https://t.co/NP46aanoao. We mainly wanted to try an alternative approach that appeared to be computationally more affordable.']",22,04,1273