An optimal interpolation scheme for surface and atmospheric parameters: applications to SEVIRI and IASI

In this paper, we present a 2-Dimensional (2D) Optimal Interpolation (OI) technique for spatially scattered infrared satellite observations, from which level 2 products have been obtained, in order to yield level 3, regularly gridded, data. The scheme derives from a Bayesian predictor-corrector scheme used in data assimilation and is based on the Kalman filter estimation. It has been applied to 15-minutes temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and temperature products and to Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia (NH3) retrievals, a gas affecting the air quality. Results have been exemplified for target areas over Italy. In particular temperature retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. Our findings show that the proposed strategy is quite effective to fill gaps because of data voids due, e.g., to clouds, gains more efficiency in capturing the daily cycle for surface parameters and provides valuable information on NH3 concentration and variability in regions not yet covered by ground-based instruments