Methods for estimating air traffic capacity reductions due to convective weather for verification

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Monday, 18 January 2010: 1:45 PM
B314 (GWCC)
Geary J. Layne, CIRES/Univ. of Colorado, Boulder, CO; and S. A. Lack

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As the United States is moving toward automated air traffic decision tools (Next Generation Air Transportation System—NextGen), evaluating forecasts for potential operational use is of primary importance. Air traffic management needs reliable and accurate convective forecast information from 0 to 8 h to be used to route air traffic between convective weather and through aviation centers, sectors, and jetways. An important criterion in evaluating the potential usefulness of a particular forecast for convective forecasting is structure. Measuring the accuracy of convective structure can be accomplished in a few ways, including: bias measurements, grid-to-grid skill scores, and also convective orientation measures. Orientation measurements become of great importance as convection within a sector from a forecast may have the same coverage as the actual observation (bias=1); however, the forecast indicates scattered air mass-type thunderstorms whereas the observations show a strong linear system. Clearly, the solid linear feature of the observation field would cause a major disruption to air traffic flow whereas the scattered convection from the forecast may indicate the possibility to fly between the convective cells. Two orientation measures are introduced herein: the Mincut Bottleneck and Euclidean Distance approaches. Both methods currently utilize the overlay of significant convective hazards (combination of radar reflectivity and echo top height estimations) onto high altitude sector geometry for estimations of sector porosity and subsequently sector capacity reduction estimations. In addition to deterministic forecasts, both methods support the evaluation of probabilistic forecasts (such as LAMP and RCPF). These methods can be changed for future realizations of air traffic management space. This paper describes the above algorithms and their impact on evaluating forecasts for NextGen applications.