Evaluation of a dimension-reduction-based statistical technique for Temperature, Water Vapour and Ozone retrievals from IASI radiance

Remote sensing of atmosphere is changing rapidly thanks to the development of high spectral resolution infrared space-borne sensors. The aim is to provide more and more accurate information on the lower atmosphere, as requested by the World Meteorological Organization (WMO), to improve reliability and time span of weather forecasts plus Earth's monitoring. In this paper we show the results we have obtained on a set of Infrared Atmospheric Sounding Interferometer (IASI) observations using a new statistical strategy based on dimension reduction. Retrievals have been compared to time-space colocated ECMWF analysis for temperature, water vapor and ozone.