Anglais

     
Recruteur
Parution
Lieu de travailToulouse, Midi-Pyrénées, France
Catégorie
Fonction

Description

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PhD position on the assimilation of IASI observations for the atmospheric composition

This offer is available in the following languages:
Français - Anglais

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General information

Reference : UMR5318-EMAEMI-001
Workplace : TOULOUSE
Date of publication : Thursday, November 08, 2018
Scientific Responsible name : Emanuele Emili
Type of Contract : PhD candidate contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 31 January 2019
Proportion of work : Full time
Remuneration : 1 768,55 ¤ gross monthly

Description of the thesis topic

Summer ozone pics and dust transport episodes represent a major health and environmental issue over the Mediterranean region and Europe. This type of episodes might increase in the next decades due to climate change and desertification. Real-time monitoring and forecasting the atmospheric composition at the continental scale becomes an important activity in this regard.
The French aerospace agency (CNES) develops since more than a decade a family of infrared sounders named IASI, onboard European MetOP satellites. Each sounder measures the Earth’s emitted radiation between 650 and 2700 cm-1 with an horizontal resolution of 12 km at nadir.
IASI main mission is to measure the temperature and water vapour profile for meteorological applications. However, its high spectral resolution and good instrumental stability encouraged a large number of application in atmospheric chemistry, including ozone and desert dust retrievals. The family of IASI instruments and its successor (IASI-NG) are conceived and built by Airbus Defense and Space and CNES and are meant to operate for several decades.
Numerical models used for forecasting the atmospheric composition, also named chemistry-transport models, can integrate satellite observations through a mathematical procedure called data assimilation. Data assimilation allows to complete sparse satellite observations (e.g. due to clouds) and obtain 3D fields of atmospheric constituents at hourly frequency (monitoring). Second, the model can be initiated with assimilation results to provide improved short-term forecasts of ozone or dust. The high spatial resolution and wide swath of the three IASI instruments that will be flying at the end of 2018 provides a great constraint for chemical-transport models.
The CECI unit at CERFACS develops since 2003 the data assimilation system for the chemical transport model MOCAGE (CNRM/Météo France), which is used for climate and air-quality applications at both global and regional scales. This system provides real-time forecasts and retrospective reanalyses for the European atmospheric composition service (https://atmosphere.copernicus.eu). We conducted research on the assimilation of IASI Level 2 retrievals (chemical concentrations) since 2008, aerosols since 2012 and we have recently introduced the possibility of assimilating directly satellite radiances (Level 1) in the model. The latter is meant to improve the results with respect to the assimilation of Level 2 retrievals, especially when multiple chemical compounds contribute to the spectral window used for the retrievals. Dust and ozone both produce a spectral signature in the 1025-1075 cm-1 region; neglecting dust variability in ozone retrievals can provide degraded results for ozone and viceversa, and thus affect further the data assimilation and the quality of model forecasts.
Within this PhD we propose to develop a coupled system that assimilates IASI radiances to correct both ozone and dust concentration at once, and analyse the benefits of such novel approach. The work will be based on the existing variational assimilation system and the operational radiative transfer code RTTOV (ECMWF, Météo-France, MetOffice). The work concerning the radiative transfer will benefit from a collaboration with Météo-France Lannion for the inclusion of modeled aerosols in RTTOV and with the Laboratoire d’Aerologie (OMP Toulouse), who develops since a long time IASI O3 and CO retrievals based on RTTOV. The main goal of this work is to evaluate the potential of a new synergistic approach for the monitoring and forecasting of ozone and desert dust. The developed methodology will also be of interest for other present and future infrared sounders (e.g. IASI-NG) or employed for the design of new instruments.
The topic sits at the interface between atmospheric modelling, satellite data processing and numerical methods. The PhD candidate will develop strong skills in atmospheric chemistry, high performance scientific computing (HPC) and satellite data.

Work Context

CERFACS is a basic and applied research center, specialized in modeling and numerical simulation. Trough its facilities and expertise in High Performance Computing, CERFACS deals with major scientific and technical research problems of public and industrial interest.
CERFACS hosts interdisciplinary researchers such as physicians, applied mathematicians, numerical analysts, software engineers who design and develop innovative methods and software solutions to meet the needs of the aeronautics, space, climate, energy and environmental fields.
CERFACS is involved in major national and international projects and is strongly interacting with its seven shareholders : Airbus Group, Cnes, EDF, Météo France, Onera, Safran et Total. It is also associated with partners like CNRS (Associated Research Unit), Irit (common laboratory), CEA and Inria (cooperation agreements).
The PhD candidate will join the Global Change (GlobC) team at CERFACS, which forms the CECI CNRS research unit.

Additional Information

The PhD candidate must possess a master’s degree in Earth’s Sciences, Physics, Meteorology, Mathematics applied to Geosciences or a similar field. Good skills in computer science (Fortran, Unix, Python) represent an asset, as well as previous experience within Atmospheric Sciences.
A CV, one or more recommendation letters and a motivation letter are required for considering the application.

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— EmploiCNRS ( EmploiCNRS) Thursday, 08 November, 18

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