Réunion

Johannes Bransdetter (Johannes Kepler University Linz)

"Embracing the next wave of scientific breakthroughs"

Abstract: In the era of scaling and LLMs, one gets notoriously confronted with the question of where we stand with applicability of such powerful techniques within scientific or engineering domains. The discussion starts by reiterating on recent triumphs in weather and climate modeling, making connections to computer vision, physics-informed learning and neural operators. Secondly, we discuss challenges and conceptual barriers which need to be overcome for the next wave of disruption in science and engineering. We showcase recent breakthroughs in multi-physics modeling, molecular dynamics, computational fluid dynamics, nuclear fusion and related fields.

Guillaume Couairon (Inria Paris)

"Skillful and compute-efficient probabilistic weather forecasting with ArchesWeatherGen"

Abstract: Deep learning weather forecasting models trained with the next state prediction objective on ERA5 have shown great success compared to numerical global circulation models. However, for a wide range of applications, being able to provide representative samples from the distribution of possible future weather states is critical. In this talk, we will present ArchesWeatherGen, a methodology to leverage deterministic weather models in the design of probabilistic weather models, leading to improved performance and reduced computing costs. ArchesWeatherGen is based on flow matching, a modern variant of diffusion models, that is trained to project ArchesWeather's predictions to the distribution of ERA5 weather states. It is a true stochastic emulator of ERA5 and surpasses IFS ENS and NeuralGCM on all WeatherBench headline variables (except for NeuralGCM's geopotential). Our work also aims to democratize the use of deterministic and generative machine learning models in weather forecasting research, with academic computing resources.

Laure Raynaud (CNRM Toulouse, Météo France)

"Large-scale deep-learning for weather and climate prediction" 

Abstract: A new paradigm for weather and climate prediction has emerged recently : data-driven prediction models have become competitive with standard physics-based models on many aspects, thanks to an accurate encoding of the data distribution. Most models have been developed for task-specific purposes and are trained on a single type of data (such as the ERA5 reanalysis). The next challenge to expand the capabilities of data-driven modeling is to fully exploit the vast range of atmospheric observations, characterized by spatio-temporal variations and heterogeneous outputs (point or spatial time series, vertical profiles, vertically integrated data, … ). This naturally leads to the development of foundation models that learn a task-agnostic representation of the atmosphere. An overview of the most advanced models will be presented, as well as early results for integrating heterogeneous data sources. How physically-consistent and explainable these new models are is still an open question, that will be discussed in the presentation.