We are pleased to announce the availability of one PhD studentship at LSCE funded by the French ANR project ARGONAUT.
Assimilation of satellite images of atmospheric concentrations for the monitoring of the CO, NOx and CO2 emissions in France
Due to the large industrialization and urbanization, air quality has been degraded worldwide, leading to more than 7 million premature deaths annually [Lelieveld et al, 2019; WMO 2016] and climate change is becoming a reality with the five warmest years measured in the 2010s. The society is facing a major environmental challenge: developing coordinated monitoring and mitigation strategies leading to optimal reduction of both air quality and climate change impacts. In this context, the French-funded ARGONAUT project (PollutAnts and gReenhouse Gases emissiOns moNitoring from spAce at high ResolUTion) aims at providing new estimates of French anthropogenic emissions of the main gaseous pollutants (nitrogen oxides - NOx, carbon monoxide - CO and non-methane volatile organic compounds - NMVOCs) and carbon dioxide (CO2) at high resolution. The approach chosen to tackle this issue is the atmospheric inversion of sources and sinks using data assimilation. Based on the past developments of a range of atmospheric inversion systems covering the global to the local scales, the French Laboratoire des Sciences du Climat et de l'Environnement (LSCE) and its partners of the ARGONAUT project will implement a sophisticated inverse modeling system, able to benefit from the high-resolution imaging of the last generation of satellites within the European Copernicus program (Sentinel-5P/TROPOMI and CO2M) to monitor the emissions at the national level.
In this context, the LSCE is looking for a motivated PhD student to exploit the multiple-species high resolution imaging for CO, NOx, and CO2. The main objective is to analyze the local correlations between the various species and co-assimilate them in order to better constrain their emission estimates. Making a step forward in the joint assimilation of various species and addressing the correlation between them will improve the emission inventories and their consistency across species. More generally, it should help addressing air quality and climate change related emissions at the national to subnational scales.
The PhD student will join the SATINV inversion team (~ 25 scientists) of LSCE, located 25km South of Paris, in Gif-sur-Yvette. His/her responsibilities and tasks will include:
·Evaluating a ~10km2 configuration of the regional chemistry-transport model CHIMERE [Menut et al., 2013] to simulate CO, NO2 and CO2 over France,
·Setting-up and using the variational inverse modelling system [Fortems-Cheiney et al. 2019] to assimilate space-borne images of CO, NO2 and CO2 (real data from TROPOMI [Borsdorff et al., 2019; Lama et al., 2019] and synthetic data from CO2M [Kulhmann et al., 2019]) first independently in mono-species inversions and then together in multi-species inversions,
·Evaluating the estimated emissions, in interaction with inventory agencies,
·Interact regularly with the partners of the ARGONAUT project: at LSCE, but also at the Laboratoire Interuniversitaire des Sciences Atmosphériques (LISA), at the Institut National de l’Environnement industriel et des RISques (INERIS) and at the Centre d'Enseignement et de Recherche en Environnement Atmosphérique (CEREA), to ensure that the inverse modelling developments and experiments are well integrated within the project,
·Lead and contribute to the writing of peer-reviewed publications with the results from ARGONAUT.
Expected skills and experience:
·Programming (ideally in Python and Fortran),
·Knowledge in atmospheric sciences and in applied mathematics, § Fluent French or English (oral and written),
·Ability to work collaboratively with a team of researchers.
Education: Master in climate, environmental or atmospheric sciences, or in applied mathematics
Location: Laboratoire des Science du Climat et de l’Environnement (https://www.lsce.ipsl.fr)
Contract duration: 3 years.
Starting date: ideally before October 2020.
How to apply: Applicants should submit a complete application package by email to firstname.lastname@example.org. The application package should include (1) a curriculum vitae, (2) statement of motivation and (3) names, addresses, phone numbers, and email addresses of at least two references.
Borsdorff, T., aan de Brugh, J., Pandey, S., Hasekamp, O., Aben, I., Houweling, S., and Landgraf, J.: Carbon monoxide air pollution on sub-city scales and along arterial roads detected by the Tropospheric Monitoring Instrument, Atmos. Chem. Phys., 19, 3579–3588, https://doi.org/10.5194/acp-19-3579-2019, 2019.
Fortems-Cheiney, A., Pison, I., Dufour, G., Broquet, G., Berchet, A., Potier, E., Coman, A., Siour, G., and Costantino, L.: Variational regional inverse modeling of reactive species emissions with PYVAR-CHIMERE, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-186, in review, 2019.
Kuhlmann, G., Broquet, G., Marshall, J., Clément, V., Löscher, A., Meijer, Y., and Brunner, D.: Detectability of CO2 emission plumes of cities and power plants with the Copernicus Anthropogenic CO2 Monitoring (CO2M) mission, Atmos. Meas. Tech., 12, 6695–6719, https://doi.org/10.5194/amt-12-6695-2019, 2019.
Lama, S., Houweling, S., Boersma, K. F., Aben, I., van der Gon, H. A. C. D., Krol, M. C., Dolman, A. J., Borsdorff, T., and Lorente, A.: Quantifying burning efficiency in Megacities using NO2 / CO ratio from the Tropospheric Monitoring Instrument (TROPOMI), Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-1112, in review, 2019.
Lelieveld, J. et al: Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions, European Heart Journal, ehz135, https://doi.org/10.1093/eurheartj/ehz135, 2019.
Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N., Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux, F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard, R., and Vivanco, M. G.: CHIMERE 2013: a model for regional atmospheric composition modelling, Geosci. Model Dev., 6, 981-1028, https://doi.org/10.5194/gmd-6-981-2013, 2013.
WMO World Health Organization: Ambient air pollution: A global assessment of exposure and burden of disease, http://www.who.int/phe/publications/air-pollution-global-assessment/en/, 2016.