Operational discrimination of raining from non-raining clouds in mid-latitudes using multispectral satellite data

Abstract

The detection of rainfall by geostationary (GEO) weather satellites has a long tradition as they provide area-wide information about the distribution of this key parameter of the water cycle in a very high temporal and high spatial resolution (e.g., Adler and Negri 1988). Most retrieval techniques developed so far for GEO systems are based on the relationship between cloud top temperature in the infrared channel and rainfall probability. Such retrievals which are often referred to as IR retrievals are appropriate for the tropics where precipitation is generally linked with deep convective clouds that can be easily identified in the infrared and/or water vapor channels (e.g., Levizzani et al. 2001; Levizzani 2003) but show considerable drawbacks in the mid-latitudes (e.g., Ebert et al. 2007; Frh et al. 2007) where great parts of the precipitation originates from clouds preferably formed by spatially extended frontal lifting processes in extra-tropical cyclones (hereafter denoted as advective/stratiform precipitation). To overcome this drawback, some authors have suggested to use the effective cloud droplet radius (aef) defined as the ratio of the third to the second power of the cloud droplet spectrum (Hansen and Travis 1974) which can be retrieved from multispectral satellite data. They propose to use values of aef of around 14 ?m as a fixed threshold value (THV) for precipitating clouds (e.g., Rosenfeld and Gutman 1994; Lensky and Rosenfeld 1997; Ba and Gruber 2001) but these studies have mainly focused on convective systems and a fixed THV seems to be not applicable for a reliable differentiation between frontal induced raining and non-raining stratiform clouds over large parts of Europe. In this context, Nauss and Kokhanovsky (2006, 2007) recently proposed a new scheme for the discrimination of raining and non-raining cloud areas applicable to mid-latitudes using daytime multispectral satellite data. Similarly, Thies et al. (2008) introduced a new technique for rain area delineation in the mid-latitudes using night-time multispectral satellite data. In the following sections, the conceptual model of this new approach as well as its application to geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data will be presented. Since the final technique is different for day- and night-time scenes, the two algorithms will be presented separately.