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For the past twenty years Army field intelligence analysts and staff weather officers assigned to combat weather teams have utilized a decision support tool called the Integrated Weather Effects Decision Aid (IWEDA). The IWEDA system ingests weather forecast model grids, applies a rules/thresholds database to the grids, and produces color-coded overlays for terrain map backgrounds. These map overlays quickly indicate the severity of the weather impacts on Army weapon and support systems, and have provided a valuable tool for mission commanders to plan battlefield operations. Although it is a useful tool, the basic IWEDA concept has not been updated since its inception, and its capabilities have fallen behind the vastly improved numerical weather prediction models and computing platforms that are available today. Its coarse color-coded map indicators are a simplistic green/amber/red scheme, all weather parameters are treated with equal weight, and there is no means of accounting for how many model parameters are contributing to the adverse weather effects. The current research describes a new composite scoring system by which weather parameters can be assigned different weights; an accounting is made for the number of parameters contributing to the adverse weather, and much greater color granularity can be applied to the IWEDA map overlays. The higher color granularity and adjustable parameter weights are expected to afford much greater flexibility to commanders and intelligence analysts as weather effects are incorporated into planning battlefield operations. The new capability also is suitable for use in comparable operational civilian weather impacts technologies.

Meteorological models need to be compared to long-term, routinely collected meteorological data. Whenever numerical forecast models are validated and compared, verification winds are normally interpolated to individual model grid points. To be statistically significant, differences between model and verification data must exceed the uncertainty of verification winds due to instrument error, sampling, and interpolation. This paper will describe an approach to examine the uncertainty of interpolated boundary layer winds and illustrate its practical effects on model validation and intercomparison efforts. This effort is part of a joint model validation project undertaken by the Environmental Verification and Analysis Center at the University of Oklahoma (http://www.evac.ou.edu) and the Battlefield Environment Directorate of the Army Research Laboratory. The main result of this study is to illustrate that it is crucial to recognize the errors inherent in gridding verification winds when conducting model validation and intercomparison work. Defendable model intercomparison results may rely on proper scheduling of model tests with regard to seasonal wind climatology and choosing instrument networks and variogram functions capable of providing adequately small errors due to sampling and imperfect modeling. Thus, it is important to quantify verification wind uncertainty when stating forecast errors or differences in the accuracy of forecast models.

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