Scientists Atmospheric Composition Data Assimilation

**The role**:
One of the core components of the CAMS service portfolio is the operational forecasting of atmospheric composition using ECMWF’s Integrated Forecasting System (IFS). The IFS is today one of the most comprehensive and advanced systems in the world for data assimilation and forecasting of global atmospheric composition, with operational outputs serving hundreds of users daily and being seen by millions (for instance through CNN, Windy, and other platforms).

With the recent launches of the Meteosat Third Generation Sounder (MTG-S) and the Metop Second Generation (Metop-SG) satellite platforms hosting instruments such as Sentinel-4, Sentinel-5, 3MI, IRS, and IASI-NG, there is a wealth of new-generation satellite observations available to use in the global CAMS forecasting system. Particularly exciting is the advent of atmospheric composition data from geostationary satellites, such as Sentinel-4, that give hourly air quality information during daytime.

We are looking for two data assimilation scientists, who will contribute to the development of the atmospheric composition data assimilation aspects in the IFS with a specific focus on the implementation of these new satellite observations. The IFS includes specific schemes for representing processes related to aerosol, chemical species, and greenhouse gases and uses 4-dimensional variational (4D-Var) data assimilation to merge the model fields with satellite observations. One of the positions will focus on the data assimilation for chemical species, such as ozone, carbon monoxide, nitrogen dioxide, and others; the other position will focus on the data assimilation for aerosol. The post-holders will work with internal ECMWF staff and externally contracted experts to introduce and evaluate the new observational data streams.

The positions are in the Atmospheric Composition Section of the Research Department, with a close collaboration with relevant teams in the Research and Forecast Departments. The postholder will also collaborate with relevant space agencies, such as EUMETSAT and ESA, as well as with externally contracted entities that directly support the further development of the IFS.

**Your responsibilities**:

- To introduce and evaluate new observational data streams for aerosol and/or reactive gases in the CAMS global data assimilation and forecasting system, including their use for monitoring emissions
- To maintain and further improve the 4D-Var data assimilation aspects of atmospheric composition in the IFS with a specific focus on aerosols and/or reactive gases
- To contribute to the monitoring of the assimilated satellite data and the assessment of their impact in the IFS
- To assist with technical tasks needed for running atmospheric composition in the IFS
- To assist the CAMS user support for responding to specialised user queries
- To provide technical input into the contractual management of external CAMS contracts that are within the expertise of the successful applicant

**The team**:
The Atmospheric Composition Section oversees the development of the atmospheric composition aspects in the IFS. The role will require significant engagement with Section members and other IFS developers, externally contracted research scientists, and colleagues throughout ECMWF.

**What we are looking for**:

- Excellent interpersonal and communication skills
- Collaborative approach to the work
- Dedication, passion and enthusiasm to succeed both individually and as a fully integrated member of the team
- Self-motivated, and able to work with mínimal supervision.
- Excellent analytical and problem-solving skills with a proactive and constructive approach
- Flexibility, with the ability to adapt to changing priorities.
- Highly organised with the capacity to work on a diverse range of tasks to tight deadlines.
- Ability and willingness to collaborate with internal and external experts on related aspects of IFS development

**Your profile**

**Education**

**Experience**
- Demonstrated experience with atmospheric data assimilation methodologies, such as variational methods (4D-Var) or Ensemble Kalman Filters (EnKF).
- Experience of working with satellite retrievals of atmospheric composition would be an advantage
- Experience with ML approaches for data assimilation would be an advantage
- Experience with developing and maintaining large scientific codes in Fortran90, Python and software lifecycle maintenance in High Performance Computing environments.

**Knowledge, Skills**
- Ability to work efficiently in a Linux environment is essential
- Knowledge of tools and processes for software management and software quality is highly desirable

**About ECMWF**:
The **European Centre for Medium-Range Weather Forecasts (ECMWF)** is a world leader in Numerical Weather Predictions providing high-quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation we collaborate internationally to serve our members and the wi