AtmoRep: large scale representation learning for atmospheric dynamics
Authors: Luise Ilaria , Lessig Christian , Schultz Martin , Langguth Michael 
Published: March 2024

The atmosphere affects humans in a multitude of ways, from loss of lives due to adverse weather effects to long-term social and economic impacts. Very recently, AI-based models have shown tremendous potential in reducing the computational costs for numerical weather prediction. However, they lack the versatility of conventional models. The team has therefore recently introduced AtmoRep, a first probabilistic foundation model of atmospheric dynamics for multi-purpose applications [Lessig 2023].&#160; Through large-scale representation learning, AtmoRep encapsulates a general description of the atmosphere dynamics based on the ERA5 reanalysis. Following the principles of in-context learning from natural language processing, adapted here to Earth system science, domain applications like e.g. forecasting and downscaling can be performed without any task-specific training. The model has therefore been applied as the backbone for several tasks, from weather forecasting to downscaling, spatio-temporal interpolations and data driven precipitation forecasting. After fine-tuning AtmoRep achieves skill competitive with Pangu-Weather [Bi 2023] for short-term forecasting and substantially exceeds the AI-based competitor [Stengel 2021] for downscaling.&#160;&#160;The model has been conceived as a flexible stack of Transformers, one for each field, coupled through cross attention to ensure a plug-and-play architecture and allow the dynamical integration of new fields without the need of retraining from scratch. The main innovation consists of a newly developed statistical loss, which generalises from the concept of cross-entropy in classification problems. The model is therefore fully probabilistic, and each application comes with a well calibrated set of ensemble members with spread correlated to the variability of the system, as demonstrated for e.g. in forecasting by inspecting the CRPS score or the error to spread ratios (see [Lessig 2023]).&#160;&#160;In addition, the flexible nature of the model allows to perform model fine-tuning on different data-types. To demonstrate that, the precipitation forecasting skill of AtmoRep has been fine-tuned on real radar data using the Radklim dataset as a proxy for accurate total precipitation rates. Using Radklim as ground truth, the diagnostic scores e.g. the RMSE or the FBI (Frequency Bias Indicator), indicate univocally that after fine-tuning the AtmoRep model outperforms ERA5, both in terms of accuracy in spatial coverage and intensity.&#160;&#160;In terms of future plans, we are currently working to extend the model to longer term weather forecasts, up to medium range forecasting. Furthermore, we are integrating the downscaling and forecasting steps using the CERRA 5km resolution reanalysis over Europe, so to achieve multi-resolution coarse-to-fine predictions beyond quarter degree resolution in the next few months.&#160;AtmoRep represents a step forward in the direction of building solid and skilful multi-purpose approaches and the present work is, in our opinion, only a first step towards the possibilities that are enabled by the methodology.&#160;[Lessig 2023] Lessig et. al. AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning. arXiv:2308.13280, 2023.[Bi 2023]&#160;K. Bi et al., &#8220;Accurate medium-range global weather forecasting with 3d neural networks,&#8221; Nature, 2023.[Stengel 2021]&#160;K. Stengel et al., &#8220;Adversarial super-resolution of climatological wind and solar data,&#8221; Proceed- ings of the National Academy of Sciences, vol. 117, 2020.