Sebastien Massart, ECMWF
Can the assimilation of atmospheric tracer improve the weather forecast?
Abstract: The operational data assimilation system of the European Centre for Medium-Range Weather Forecasts (ECMWF) is based on a 4D Var algorithm. As part of the Copernicus Atmosphere Monitoring Service (CAMS) implemented by ECMWF, the ability to assimilate atmospheric tracers were included in the ECMWF 4D Var. This allows CAMS to produce daily analyses and forecast of aerosols, reactive gases and greenhouse gases relying on the assimilation of data from various satellites. The transport of the atmospheric tracers benefits from the meteorological analysis but there is currently no direct coupling in the assimilation between the atmospheric tracers and the meteorological variables.
An experimental ensemble Kalman filter (EnKF) is in development at ECMWF. In the EnKF framework, the coupling between the atmospheric tracers and the meteorological variables is accounted for by the covariances of the background error. We will present how the assimilation of retrieval products of two of the most important greenhouse gases, i.e. carbon dioxide (CO2) and methane (CH4), modifies the meteorological analysis. We will analyse the covariances between CO2, CH4 and the meteorological variables (wind, temperature, humidity) computed by the EnKF. We will present the differences between two EnKF analyses, respectively with and without the assimilation of greenhouse gases retrieval products from the Greenhouse Gases Observing Satellite (GOSAT) and from the Infrared Atmospheric Sounding Interferometer (IASI). We will finally conclude whether there is room for the assimilation of atmospheric tracers to improve the meteorological analysis and forecast.