8.1 The use of statistical relationships to produce probabilistic nowcasts of storms

Wednesday, 3 August 2011: 9:00 AM
Imperial Suite ABC (Los Angeles Airport Marriott)
John K. Williams, NCAR, Boulder, Colorado; and D. A. Ahijevych, C. P. Kalb, C. L. Phillips, J. O. Pinto, and M. Steiner

Efforts to create probabilistic weather nowcast and forecast products for the NextGen Initial Operating Capability have focused on developing displays that depict probabilities of storms over broad local regions and time windows. These products (e.g., the Localized Aviation MOS Product and HRRR Convective Probability Forecast) have relatively high probabilities thought suitable for display and interpretation by human users via automated decision support tools. In the NextGen free flight era, decision support tools may provide airline dispatchers and air traffic managers with suggested routes and en-route tactical adjustments that minimize fuel use while maintaining safety. This paper presents an approach to probabilistic convective nowcasting that may be well-suited to this application. The approach is based on learning statistical relationships between observations, NWP model fields and the likelihood of convection at individual pixels and precise times, which yields lower probabilities but much higher resolution. The paper describes recent improvements to the technique, including online tuning of the statistical model and incorporation of new input fields aimed at better capturing both environmental conditions and triggering mechanisms for convective initiation. In an attempt to weigh the benefit of these higher-resolution forecasts for route planning, a dynamic programming algorithm is used to determine an optimal route based on a series of probabilistic forecasts both before and during a simulated flight. This process is repeated over a number of scenarios for both high-resolution and low-resolution forecasts. The authors argue that route changes required during flight (due to forecast updates) may be used to help measure the forecasts' suitability for automated route planning in the NextGen era.

This research is in response to requirements and funding by the Federal Aviation Administration (FAA). The views expressed are those of the authors and do not necessarily represent the official policy or position of the FAA.

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