Two Automated Methods to Derive Probability of Precipitation Fields over Oceanic Areas from Satellite Imagery

Abstract

Two methods of deriving probability of precipitation fields (PP) over oceanic areas are presented and compared. The cloud fields are analyzed at the scale of ?130?150 km from satellite visible and infrared imagery and collocated with ship observations of present weather. Method 1 is based on a detailed cloud classification scheme in 20 classes: a mean PP is determined for each cloud class. Method 2 assigns a PP based on cloud top temperature and mean cloud albedo only. For both methods, a normalization with respect to cloud fraction is applied. Method 1 involves more cloud field descriptors than Method 2, but the latter is simpler to implement and much faster. The PPs assigned to individual cloud fields vary between 0% and 65%. The importance of the visible sensor is clearly demonstrated, i.e., infrared-only techniques will be much less accurate. For real-time applications, the two methods provide similar results except for some specific cloud classes where maximum differences reach 13%, due to the lower level of classification used in Method 2. On a monthly time scale, the absolute accuracy of both methods is about 1.2% rms, based on independent data taken during the winters of 1984 (1064 samples) and 1986 (673 samples) over the northwestern Atlantic. From the 3 months for which PP maps are produced, the average local variability between months is 4.8% rms. It follows that the PP variance between months is typically 16 times larger than the error variance of the satellite estimates. Thus both methods provide a reliable precipitation indicator.