Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Centre National de Recherches Météorologiques
Durée : 15 months
Contact : laure.raynaud@meteo.fr
Date limite de publication : 2024-06-20

Contexte :
This position is part of the DestinE Tender ‘DE_371’. Destination Earth (DestinE) is an initiative of the European Commission under the EU Digital Europe programme, alongside with ESA and EUMETSAT as partners. DestinE aims to deploy several highly accurate thematic digital replicas of the Earth, called Digital Twins (DTs). The Digital Twins will help monitor and predict environmental change and human impact, in order to develop and test scenarios that would support sustainable development and corresponding European policies for the Green Deal. Artificial Intelligence (AI) and, more precisely, Machine and Deep Learning (ML and DL) are important for DestinE on many different levels, in particular for uncertainty quantification. The aim of DE_371 is to demonstrate that ML/DL based methodologies can augment DestinE datasets and products with the purpose of better capturing uncertainty.

Sujet :
Currently operational weather forecasts rely on physically-based modelling approaches, and Numerical Weather Prediction (NWP) models are operated to determine atmospheric conditions for the next hours and days. In particular, Ensemble Prediction Systems (EPSs) aim at sampling the probability distribution of future atmospheric states, by running several NWP forecasts in order to account for the different sources of uncertainty. However, the design of EPSs is strongly constrained by available computational resources, and is often limited to O(50) forecasts. The goal of the position is to use generative ML techniques to increase the ensemble size by creating additional physically-consistent ensemble members tethered to a small ensemble, or single member deterministic input. Building on the innovative works of Brochet et al. (2023) with a GAN framework, several avenues for improvement will be explored, including the generation of temporal sequences, the production of a wider set of variables, and the comparison to other generative approaches such as diffusion models. A specific attention will be paid to the evaluation of the physical consistency of generated forecasts and of their capacity to significantly improve the statistical properties of the existing ensemble, including for instance the spread-error relationship, probabilistic skill scores and representation of extreme events.

Profil du candidat :
The ideal candidate would have the following qualifications :
– A PhD degree in atmospheric sciences, statistics or artificial intelligence
– A strong background in deep learning algorithms, in particular convolutional neural networks and deep generative models
– Experience in geophysical problems would be appreciated, at least a strong interest for applied research in atmopsheric physics is highly recommended
– Proficiency with Python programming and AI librairies (tensorflow, PyTorch)
– Experience with processing large volumes of data
– Experience of working in a Linux-based environment
– Aptitude for scientific work, written and oral communication in English, meetings abroad possible
– A scientific curiosity, autonomy, rigor in the interpretation of the results

Formation et compétences requises :
PhD degree.

Adresse d’emploi :
This work will be carried on in the Assimilation and Forecasting group of the Météo-France research department (CNRM), in Toulouse, France.