Kalman filter physical retrieval of geophysical parameters from high temporal resolution geostationary infrared radiances: the case of surface emissivity and temperature

The high temporal resolution of the data acquisition by geostationary satellites and their capability to resolve the diurnal cycle are a precious source of information which could be suitably used to retrieve geophysical parameters. Currently this information is for the most part considered as uncorrelated, both in space and time: each pixel is treated independently from its neighbors and the present events are not linked to past or future ones. In this paper we develop a Kalman filter approach to apply spatial and temporal constraints to estimate the geophysical parameters from radiance measurements made from geostationary platforms. We apply the new strategy to a particular case study, i.e. the retrieval of emissivity and surface temperature from SEVIRI (Spinning Enhanced Visible and InfraRed Imager) observations over a target area encompassing the Iberian Peninsula and Northwestern Africa. The retrievals are then compared with in situ data, and other similar satellite products.