Rain area delineation by means of multispectral cloud characterization from satellite

Abstract

The identification of precipitation areas by microwave based rain algorithms can be improved by means of cloud classification schemes based on multispectral observations. Several recent studies have demonstrated the potential of cloud microphysical and optical characterization for the improvement of passive microwave rain estimates, especially in detecting likely precipitating pixels over land. The multispectral sensing capabilities of MODIS onboard Aqua are exploited to characterize the cloudy scenario, using a twofold approach: a) an RGB technique to qualitatively identify the different cloud systems on the basis of the combination of radiances measured in three selected channels, and b) a quantitative description of cloud top in terms of optical thickness (τ), effective radius (R_e) and top temperature (T_c). The information gathered by the multispectral analysis of the cloud field from MODIS is contrasted with the rain intensity at the ground as derived from the AMSR-E operational algorithm, to assess the statistical relationships between microphysical parameters and the rain intensity for such nearly simultaneous and co-located observations.