Jamie T. Braid, Jennifer L. Mahoney , Tressa L. Fowler3, and Barbara G. Brown

Research Applications Program National Center for Atmospheric Research Boulder, Colorado

Author Contact Information: Jamie T. Braid, NCAR, P.O. Box 3000, Boulder, CO 80307, Phone: (303) 497-8395; Fax: (303) 497-8401; e-mail: braid@rap.ucar.edu

Abstract

In non-fatal aviation accidents and incidents, in-flight turbulence is the leading cause of injuries to airline passengers and flight attendants. Turbulence is air movement that usually cannot be seen. This has led to a big push to find ways to give pilots an idea of where turbulence is going to occur. Human forecasters have been the first line of defense for pilots. With their forecasts, pilots are able to either avoid possible areas of turbulence, or at least inform the passengers so that they are safely belted into their seats. Along with the human forecasters, model-based algorithms also are used to help the human forecaster make a more accurate forecast. New algorithms, intended to provide improved forecasts, are under development.

From 16 February to 22 April 2001 a subjective assessment of model-based forecasts of clear air turbulence (CAT) was conducted. This was the second season for this assessment and included forecasters from both The National Weather Service's Aviation Weather Center (AWC) and Delta Airlines. In these assessments the forecasters were asked a series of weather and algorithm related questions. From their responses, data were collected on how the different algorithms performed in many types of conditions. Seven different algorithms included in the study were Ellrod-1, Horizontal Shear, ITFA (Integrated Turbulence Detection and Forecasting Algorithm), Richardson Number, Temperature Gradient, Ulturb (Upper-Level Turbulence), and Vertical Wind Shear.

In this poster the following will be accomplished. Background weather information on the main causes of clear air turbulence will be provided. Some examples of the questionnaires that were filled out by the forecasters at AWC and Delta will be presented. Each of the algorithms used in the assessment will be defined. Two cases where the algorithms performed well and two cases where the algorithm performed poorly will be displayed along with some general statistical data on the subjective and objective performance of the algorithms (Mahoney et al. 2001 Brown et al 2000 ). Lastly, enhancements, planned for the next subjective turbulence assessment, such as adding another forecast group and streamlining the questionnaire, will be described.

1 Abstract submitted to the First Student Conference and Career Fair, American Meteorological Society, 12-13 January 2002, Orlando, Florida. 2 Forecast Systems Laboratory, NOAA, Boulder, CO 3 Research Applications Program, NCAR, Boulder, CO 4 Mahoney, J.L., B.G. Brown, R. Bullock, T. Folwer, C. Fischer, J. Henderson, B. Sigren: 2001, Turbulence algorithm intercomparison: Winter 2001 results.(Report available from author; mahoney@fsl.noaa.gov)

5 Brown, B.G., J.L. Mahoney, J. Henderson, T.L. Kane, R. Bullock, and J. E. Hart, 2000: The Turbulence algorithm inter comparison exercise: statistical verification results. Preprints: 9th Conference on Aviation, Range and Aerospace Meteorology, 11-15 September, Orlando, Florida.