A geostationary thermal infrared sensor to monitor the lowermost troposphere : O3 and CO retrieval studies
Claeyman, M. ; Attié, J.-L. ; Peuch, V.-H. ; El Amraoui, L. ; Lahoz, W. A. ; Josse, B. ; Ricaud, P. ; Clarmann, T. von ; Höpfner, M. ; Orphal, J. ; Flaud, J.-M. ; Edwards, D. P. ; Chance, K. ; Liu, X. ; Pasternak, F. ; Cantié, R.
Année de publication
This paper describes the capabilities of a nadir thermal infrared (TIR) sensor proposed for deployment onboard a geostationary platform to monitor ozone (O3) and carbon monoxide (CO) for air quality (AQ) purposes. To assess the capabilities of this sensor we perform idealized retrieval studies considering typical atmospheric profiles of O3 and CO over Europe with different instrument configuration (signal to noise ratio, SNR, and spectral sampling interval, SSI) using the KOPRA forward model and the KOPRA-fit retrieval scheme. We then select a configuration, referred to as GEO-TIR, optimized for providing information in the lowermost troposphere (LmT; 03 km in height). For the GEO-TIR configuration we obtain ~1.5 degrees of freedom for O3 and ~2 for CO at altitudes between 0 and 15 km. The error budget of GEO-TIR, calculated using the principal contributions to the error (namely, temperature, measurement error, smoothing error) shows that information in the LmT can be achieved by GEO-TIR. We also retrieve analogous profiles from another geostationary infrared instrument with SNR and SSI similar to the Meteosat Third Generation Infrared Sounder (MTG-IRS) which is dedicated to numerical weather prediction,referred to as GEO-TIR2. We quantify the added value of GEO-TIR over GEO-TIR2 for a realistic atmosphere, simulated using the chemistry transport model MOCAGE (MOdèle de Chimie Atmospherique à Grande Echelle). Results show that GEO-TIR is able to capture well the spatial and temporal variability in the LmT for both O3 and CO. These results also provide evidence of the significant added value in the LmT of GEO-TIR compared to GEO-TIR2 by showing GEO-TIR is closer to MOCAGE than GEO-TIR2 for various statistical parameters (correlation, bias, standard deviation).