A post doctoral position is opening at the Laboratoire de Météorologie Dynamique to work on the dynamics of the internal waves in the atmosphere. These waves have scales that place them in the “grey zone” of most climate and weather prediction model. A central issue is to determine to which extent some of them are well resolved and some others need to parameterized.

The candidate will use high resolution global simulations issued from the DYAMOND project, and diagnose the waves in them. A particular focus will be on a comparison between the waves existing in the models and their potential detection by balloon measurements. In doing so, the candidate will analyse if the models represent well gravity waves, mountain waves, and some of the equatorial waves seen during campaigns (Loon and Strateole 2 stratospheric balloon observations). The candidate will also test if state of the art parameterizations of gravity waves (Lott and Miller 1997, Lott and Guez 2013, de la Camara and Lott 2016) do a fair job in representing the waves present in the simulations.

Adaptation of the schemes to improve their realism will have to be done, this will include emulation of the schemes using machine-learning techniques and test against the Loon stratospheric balloon observations post processed during the ongoing “VESRI-datawave” project. In this context, the candidate will actively interact with the international consortium (USA, UK, Germany, France) involved in VESRI-datawave.

Responsible: François Lott in collaboration with R. Plougonven, H. Hertzog and A. Podglagen from LMD.

Salaries according to the local french standards (around 2500€ net/month but this depends on experience and includes social insurances).

The position opens for one year, with possible extension over the following years.

Contact: François Lott, Laboratoire de Météorologie Dynamique, École Normale Supérieure, 24, rue Lhomond, 75231 PARIS cedex 05, FRANCE
T +33 1 44 32 22 23 • • https://www.lmd.jussieu.fr/~flott/homepage.html

PHD in atmospheric sciences, geophysical fluid dynamics, climate dynamics, data sciences including machine learning or artificial intelligence.
Publication(s) in peer-reviewed international journals.