Partial differential equations for oceanic artificial intelligence

Jules Guillot (1) , Guillaume Koenig (2) , Kadi Minbashian (3) , Emmanuel Frénod (1, 4) , Héléne Flourent (1, 4) , Julien Brajard (5, 6)

1 LMBA - Laboratoire de Mathématiques de Bretagne Atlantique
2 MIO - Institut méditerranéen d'océanologie
3 Numerical Analysis and Scientific Computing, Department of Mathematics, Technical University of Darmstadt
4 See-d, Vannes
5 NERSC - Nansen Environmental and Remote Sensing Center [Bergen]
6 LOCEAN - Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques

The Sea Surface Temperature (SST) plays a significant role in analyzing and assessing the dynamics of weather and also biological systems. It has various applications such as weather forecasting or planning of coastal activities. On the one hand, standard physical methods for forecasting SST use coupled ocean- atmosphere prediction systems, based on the Navier-Stokes equations. These models rely on multiple physical hypotheses and do not optimally exploit the information available in the data. On the other hand, despite the availability of large amounts of data, direct applications of machine learning methods do not always lead to competitive state of the art results. Another approach is to combine these two methods: this is data-model coupling. The aim of this paper is to use a model in another domain. This model is based on a data-model coupling approach to simulate and predict SST. We first introduce the original model. Then, the modified model is described, to finish with some numerical results.

Domaines: Mathématiques [math]; Physique [physics]; Sciences de l'environnement

Jules Guillot, Guillaume Koenig, Kadi Minbashian, Emmanuel Frénod, Héléne Flourent, et al.. Partial differential equations for oceanic artificial intelligence. ESAIM: Proceedings and Surveys, 2021, 70, pp.137-146. ⟨10.1051/proc/202107009⟩. ⟨hal-03265258⟩