Vincent  Huijnen

Vincent Huijnen

R&D Weather and Climate Modeling

About me

Within the section R&D Weather and Climate modeling I am a scientist working in the field of atmospheric composition modeling. Specific research interests are present day atmospheric composition modeling and assessment on a global scale. Such forecasts provide information of large-scale pollution events, and serve as boundary conditions from regional air quality models. The development of such a system involves work on the interface of chemical composition and aerosol modeling, embedded within a state-of-the-art global meteorological model and further constrained by satellite retrievals of atmospheric composition.


In a series of projects (GEMS, MACC, MACC-II and MACC-III), a model for atmospheric composition on a global scale has been developed, by introducing a module for tropospheric chemistry into ECMWF's Integrated Forecast System (IFS), also referred to as C-IFS. This system is now providing operational analyses and forecasts as part of the Copernicus Atmosphere Monitoring Service (CAMS). I am currently coordinating the atmospheric chemistry modeling tender (CAMS_42), in close collaboration with colleagues at ECMWF and other European research institutes. In the past I have been closely involved in the validation activities of the CAMS system and its predecessor.


My research activities are centred around the development and use of the MACC/CAMS system for analysis and forecasts of atmospheric composition, with the development and benchmarking of the chemistry transport model TM5 (Huijnen et al., ACP 2010), the evaluation of global and regional air quality models against NO2 satellite observations (Huijnen et al., ACP 2010), the evaluation of the MACC/CAMS system during extreme wildfire episodes over Indonesia (Huijnen et al., Nature Sci. Rep. 2016) and Western Russia (Huijnen et al., ACP 2012). More recent research also involves the further development of C-IFS for troposphere (Flemming et al., GMD 2015) and stratosphere (Huijnen et al., GMD 2016), and its application for a new reanalysis of atmospheric composition (Flemming et al., 2017).