Validation of automated cloud top phase algorithms: distinguishing between cirrus clouds and snow in <italic>a priori</italic> analyses of AVHRR imagery

Abstract

Quantitative assessments on the performance of automated cloud analysis algorithms require the creation of highly accurate, manual cloud, no cloud (CNC) images from multispectral meteorological satellite data. In general, the methodology to create these a priori analyses for the evaluation of cloud detection algorithms is relatively straightforward, although the task becomes more complicated when little spectral signature is evident between a cloud and its background, as appears to be the case in advanced very high resolution radiometer (AVHRR) imagery when thin cirrus is present over snow-covered surfaces. In addition, complex procedures are needed to help the analyst distinguish between water and ice cloud tops to construct the manual cloud top phase analyses and to ensure that inaccuracies in automated cloud detection are not propagated into the results of the cloud classification algorithm. Procedures are described that enhance the researcher’s ability to (1) distinguish between thin cirrus clouds and snow-covered surfaces in daytime AVHRR imagery, (2) construct accurate a priori cloud top phase manual analyses, and (3) quantitatively validate the performance of both automated cloud detection and cloud top phase classification algorithms. The methodology uses all AVHRR spectral bands, including a band derived from the daytime 3.7-?m channel, which has proven most valuable for discriminating between thin cirrus clouds and snow. It is concluded that while the 1.6-?m band is needed to distinguish between snow and water clouds in daytime data, the 3.7-?m channel remains essential during the daytime to differentiate between thin ice clouds and snow. Unfortunately this capability that may be lost if the 3.7-?m data switches to a nighttimeonly transmission with the launch of future National Oceanographic and Atmospheric Administration (NOAA) satellites.