MAR.ia: How diffusion-based approaches can reproduce extreme weather events

MAR is a Regional Climate Model (RCM) used over Belgium that provides deterministic downscaling of reanalyses and Earth System Models (ESMs) at 5-km resolution (Doutreloup et al., 2019). These high-resolution fields are computationally expensive to produce as they require solving complex physical equations. Combined with its deterministic nature, this limits the use of MAR for assessing the frequency and intensity of extreme events and their future changes.

To address this limitation, we have developed MAR.ia, a diffusion-based emulator of MAR which provides probabilistic estimates of downscaled fields at a lower computational cost (from 0.25° and 1° ERA5 fields). This allows the direct generation of ensembles from which we can derive a range of possible weather outcomes and estimate their corresponding likelihood.

However, the reproduction of extreme events is expected to be more challenging for diffusion models because these events might be scattered or absent from the training set. This is due to the fact that they are rare, and also to climate change which induces a shift between the training and testing distributions.

We evaluate the MAR.ia reconstruction of extreme heatwaves, storms and heavy rainfall associated with several daily historical events in Belgium and compare these results  with those obtained on average over the testing period.

This evaluation enables us to critically assess the ability of  deep generative models, and more precisely diffusion models approaches, to faithfully reconstruct out-of-distribution events. 

 

Doutreloup, S., Wyard, C., Amory, C., Kittel, C., Erpicum, M., and Fettweis, X. (2019). Sensitivity to Convective Schemes on Precipitation Simulated by the Regional Climate Model MAR over Belgium (1987–2017), Atmosphere, 10, 34. https://doi.org/10.3390/atmos10010034.