Statistical Contrail Forecasting

Abstract

The current operational Air Force Weather Agency condensation trail (contrail) forecast technique is based on the Appleman algorithm, developed in the 1950s, with minor modifications. The Appleman algorithm requires accurate measurements or forecasts of ambient flight-level temperature, relative humidity, and pressure, as well as the amount of heat and water vapor added by an aircraft to its exhaust to determine accurately the critical temperature for contrail formation. Several factors contribute to the relatively poor contrail forecasts produced by the Air Force Weather Agency contrail forecast technique, including insufficiently accurate atmospheric measurements and numerical weather prediction forecasts of temperature and humidity at flight level, as well as some of the procedures used in the Air Force Weather Agency?s implementation of the Appleman algorithm. The Contrail Field Program was conducted in eastern Massachusetts during a 10-day period in September 1995. Radiosonde data and aircraft observations were collected from a five-station network. Radiosondes were launched every 3 h, and aircraft observations included aircraft type, aircraft speed, aircraft altitude, and whether the aircraft produced a contrail. This dataset of nearly coincident (in time and space) radiosonde and aircraft observations was used to develop a new statistical regression contrail prediction model and to compare the results of the new statistical model with nowcasts produced by the Schrader algorithm, which is very similar to the Air Force Weather Agency contrail prediction technique, known as ?JETRAX.? The Statistical Contrail Forecast Model makes use of logistic regression techniques to relate the presence or absence of observed contrails with nearly coincident radiosonde measurements. The statistical model produced a correct diagnosis of contrail occurrence or nonoccurrence for 85% of the observations, as compared with 58% correct for the Schrader technique, for this particular dataset.