Abstract
Abstract. The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allows for the retrieval of a valuable source of information about geophysical parameters. In this paper, we implement a Kalman filter approach to apply temporal constraints on the retrieval of surface emissivity and temperature from radiance measurements made from geostationary platforms. Although we consider a case study in which we apply a strictly temporal constraint alone, the methodology will be presented in its general four-dimensional, i.e., space-time, setting. The case study we consider is 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. Our findings show that the Kalman filter strategy can simultaneously retrieve surface emissivity and temperature with an accuracy of ± 0.005 and ± 0.2 K, respectively.
Highlights
Infrared instrumentation on geostationary satellites is rapidly approaching the spectral quality and accuracy of modern sensors on board polar platforms
EUMETSAT is preparing for Meteosat Third Generation (MTG), which will carry the Flexible Combined Imager (FCI) with a spatial resolution of 1–2 km at the sub-satellite point and 16 channels (8 in the thermal band), and an infrared sounder (IRS) that will be able to provide unprecedented information on horizontally, vertically, and temporally resolved water vapor and temperature structures of the atmosphere
The present paper addresses the capability of the Kalman filter to convey temporal constraint in the retrieval of surface parameters though time series of geostationary satellite data
Summary
Infrared instrumentation on geostationary satellites is rapidly approaching the spectral quality and accuracy of modern sensors on board polar platforms. It will be exemplified for a case study in which we consider a strictly temporal constraint alone As said, this is the problem of surface temperature (Ts) and emissivity ( ) separation, that is, the simultaneous retrieval of (Ts, ) from SEVIRI infrared channels. While in Li et al (2011) the observations are accumulated for a prescribed time slot (normally six hours), we pursue a genuine dynamical strategy which exploits the sequential approach of the Kalman filter This results in an algorithm which does not need to increase the dimensionality of the data space because of time accumulation, while preserving the highest time resolution prescribed by the repeat time of the geostationary instrumentation (15 min for SEVIRI).
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