Key Parameters in Forecasting IFR Conditions: Two Case Studies

In consultation with forecasters at the NWS forecast office in Pleasant Hill, Missouri, an objective forecast tool is being developed in an attempt to improve the accuracy of terminal area forecasts (TAFs) of ceiling and visibility. TAFs are vitally important to the aviation community and the accuracy of these forecasts has a direct impact on flight safety. The technique is based on multilinear regression of a large group of predictor variables often consulted by forecasters while generating a TAF. These predictor variables come from observations, satellite, and model data. Using more than 50 events in which ceiling and visibility conditions were under instrument flight rule (IFR) or low instrument flight rule (LIFR) conditions at Kansas City International Airport (MCI), optimum predictor variables were identified and regression coefficients were calculated by the multilinear regression algorithm. Using the optimum predictor variables and the regression coefficients, an objective technique to generate ceiling and visibility forecasts for TAFs is currently being developed.

We provide a detailed look at two cases of IFR and LIFR conditions at MCI during December 1999 to illustrate what can be learned from taking a statistical approach to improve the accuracy of TAFs. These cases occurred on December 4 and December 19, 1999. Both cases persisted for more than ten hours and involved IFR or LIFR conditions for both ceiling height and visibility. Optimum predictor variables for these cases were found to include satellite data and upwind observations.