The Visible Infrared Imager Radiometer Suite (VIIRS) Cloud Mask (VCM) determines, on a pixel-by-pixel basis, whether or not a given location contains cloud. The VCM serves as an intermediate product (IP) between the production of VIIRS sensor data records and 22 downstream Environmental Data Records that each depends upon the VCM output. As such, the validation of the VCM IP is critical to the success of the Suomi National Polar-orbiting Partnership (S-NPP) product suite. The methods used to validate the VCM and the current results are presented in this paper. Detailed analyses of golden granules along with tools providing deep insights into granule performance, and specific cloud detection tests reveal the details behind a given granule's performance. Matchup results with CALIPSO, in turn, indicate the large-scale performance of the VCM and whether or not it is meeting its specifications. Comparisons with other cloud masks indicate comparable performance for the determination of clear pixels. As of September 2013 the VCM is either meeting or within 2% of all of its documented requirements.
How much can be saved by using existing software components when developing new software systems? With the increasing adoption of reuse methods and technologies, this question becomes critical. However, directly tracking the actual cost savings due to reuse is di cult. A worthy goal would be to develop a method of measuring the savings indirectly by analyzing the code for reuse of components. The focus of this paper is to evaluate how well several published software reuse metrics measure the \time, money and quality" bene ts of software reuse. We conduct this evaluation both analytically and empirically. On the analytic front, we introduce some properties that should arguably hold of any measure of \time, money and quality" bene t due to reuse. We assess several existing software reuse metrics using these properties. Empirically, we constructed a toolset (using GEN++) to gather data on all published reuse metrics from C++ code; then, using some productivity and quality data from \nearly replicated" student projects at the University of Maryland, we evaluate the relationship the known metrics and the process data. Our empirical study sheds some light the applicability of our di erent analytic properties, and has raised some practical issues to be addressed as we undertake broader study of reuse metrics in industrial projects.
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