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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.
This dataset of nearly coincident radiosonde observations and aircraft observations was used to test the Schrader (1997) contrail forecast algorithm, which is very similar to the operational AFWA (Air Force Weather Agency) contrail forecast technique (JETRAX), and to develop a new statistical regression-based contrail forecast algorithm. The Statistical Contrail Forecast Model was developed, which makes use of logistic regression techniques to relate contrail-yes/contrail-no observations with nearly coincident radiosonde measurements. In this study,flight-level temperature and relative humidity parameters were found to be the most important predictors. The Statistical Contrail Forecast Model provides results that are superior to the Schrader (1997) contrail prediction algorithm. 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 40/70/10 algorithm. The probability of detection of contrail occurrences (POD/Y) was 0.91 for the statistical model and 0.35 for the Schrader 40/70/10 algorithm. The results for the Schrader (1997) algorithm presented here are consistent with past Air Force contrail studies that document the tendency of past and present AFWA techniques to underpredict the occurrence of contrails and overpredict the nonoccurrence of contrails.