Accurate contrail forecasts allow pilots to avoid levels of the atmosphere which are conducive to contrail formation, reducing their likelihood of being visually detected by enemy forces. The primary objective of this thesis is to evaluate the performance of the JETRAX contrail forecast algorithm currently used by the Air Force Weather Agency to support military air operations. A total of 397 ground based contrail observations were collected at Wright-Patterson Air Force Base on 27 different days. Observations were collected with the aid of air traffic control radar, which greatly facilitated the positive identification of overflying aircraft and provided necessary information such as aircraft type and flight level. This data set was used to validate corresponding contrail forecasts disseminated to operational users via the Air Force Weather Information Network (AFWlN). All forecast products derived from the JETRAX algorithm demonstrated greater skill than persistence, climatology, or other algorithms tested with real time radiosonde data. An 84.4 percent accuracy rate was observed. Based on this research, the Air Force Weather Agency is providing excellent contrail forecasts to their operational users, and while there is still room for improvement, no immediate changes to the JETRAX algorithm are warranted.

Accurate severe thunderstorm forecasts are critical to providing sufficient leadtime to protect lives and property. The Air Force Weather Agency has developed a 3-Element Severe Weather Forecast Algorithm that when applied to model forecasts gives and outlook region for severe thunderstorms. Improvements were made in this study to enhance the algorithm's forecast skill, reduce its "false alarm" rate, and thereby increase the amount of lead-time for installation commanders to take decisive action to protect personnel and resources. This paper discusses the performance of the 3-Element Algorithm in its original form, and the adjustments made to overcome some of its limitations. The 3-Element Algorithm techniques and results of a performance evaluation are presented. Based on the amount of forecast improvement, eight configurations were retained for analysis across the entire dataset containing six severe weather cases. A new stability proxy, the Elevated Total-Totals Index, was developed and integrated into the algorithm to improve severe weather forecasts over high-elevation regions where some traditional severe weather indices cannot be accurately computed. Additionally, the horizontal gradient of convective available potential energy was studied as a new indicator to the presence of dynamic forcing. It is hoped that improvements discussed in this paper will make the 3-Element Algorithm an effective tool in the early forecasting of severe weather, increasing lead-time to safeguard lives and resources.

This report documents the results of a study requested by the Strategic Air Command Deputy Chief of Staff for Operations (SAC/DO) to update previous contrail forecasting research done by Herbert Appleman for HQ Air Weather Service in 1953. Advancements in aircraft power plants, especially the development of bypass turbofan engines, made the new study necessary. This attempt to update and improve current contrail forecasting methods was performed by the SAC Directorate of Weather (SAC/DOW). It describes the development of new contrail forecast algorithms for several types of engines used in high-flying aircraft. It also provides contrail forecasting rules that correlate synoptic- scale upward vertical motion with contrail formation. The results indicate significant improvement in contrail forecasting accuracy over the Appleman technique now in use at the Air Force Global Weather Central. Weather, Climatology, Clouds, Cirrus, Clouds, Forecasting, Algorithms, Condensations trails, Contrails, Exhaust trails, Vapor trails.

Accurate forecasts of contrail occurrence are essential to military aircrews. Although classical forecast methods have been reasonably successful predicting contrails, there is need for improvement at low ambient relative humidity. This thesis examines the performance of the Hanson method, which was developed to provide better contrail forecasts under drier atmospheric conditions. As a secondary objective, the forecast methods of Schumann and Hanson are compared to the algorithm currently in use by the Air Force Global Weather Central. Data used to validate the algorithms were collected at Wright- Patterson AFB, OH and Edwards AFB, CA. Theoretical contrail forecasts were made for each observation, using the flight level pressure, ambient temperature, and relative humidity. Comparisons were then made between the forecast and actual observation of contrail conditions. Forecast and occurrence data were then statistically analyzed to gauge each method's performance. All methods detected roughly 75 percent of observed contrails under moist atmospheric conditions. However, the Hanson method's performance decreased when drier atmospheric observations were tested. Schumann's method performed as well as the AFGWC algorithm under all atmospheric conditions. Based on this research the Hanson method is not recommended for operational use.

Air Force Global Weather Center's (AFGWC) Relocatable Window Model (RWM) total cloud forecasts were validated using data for selected days in May, June, and July, 1996. Forecasts were generated twice daily (00 UTC and 12 UTC) to determine the RWM's ability to accurately forecast total cloud cover during the late spring and early summer. The RWM forecasts were post-processed using the Slingo cloud forecast algorithm and compared against AFGWC's operational real time nephanalysis (RTNEPH) cloud analysis model. As a minimal-skill baseline comparison to the RWM's total cloud forecast, the RTNEPH initial analysis hour was persisted and evaluated against the same RTNEPH analysis as the RWM forecasts. The results indicate RWM total cloud forecasts did not show improved skill, sharpness, accuracy or bias when compared against RTNEPH persistence through the 36-hour forecast period. The results also suggest the Slingo algorithm, as tested, is not appropriate for use in the RWM as an accurate total cloud forecast method for the late spring and early summer months. The RWM's total cloud forecast performance during the late spring and early summer over the North American Window should be improved in the short term by incorporating convective parameterization within the Slingo algorithm or replacing the Slingo algorithm with an alternative algorithm designed for more accurate and skillful total cloud forecasts. While the suggested short-term improvements are incorporated into the RWM, the results of this and other related studies must be carefully communicated to the operational users of the RWM products to be useful. In the long term, the RWM should be replaced with a state of the art forecast model capable of forecasting clouds deterministically, rather than diagnostically.