An examination of objective forecast techniques using sensor equivalent visibility to forecast fine scale variations in visibility was conducted with approximately two yr of radiation fog data collected in the AFCRL mesonet-work. Using stepwise screening regression and Regression Estimation of Event Probability (REEP) techniques, the results demonstrated the usefulness of a network of remote sensors around an airfield in objectively predicting short time scale fluctuations of airfield runway visibility. The REEP technique in particular generated exceedance probabilities that display positive skill relative to a conditional climatology model (Markov) of proven validity. Skill by subjective forecasters was also demonstrated using the REEP output as guidance in the generation of short-range forecasts out to one hr. The necessity for additional network stations away from the runway was demonstrated since statistical techniques utilizing runway visibility observations only, when applied to independent data, showed large negative skill relative to the Markov model in contrast to positive skill achieved using 8 network stations of varying distances out to 16 km from the runway.

As part of a program to improve short-range forecasts of weather conditions at aircraft terminals, a digital radar system was established at Air Force Geophysics Laboratory, Bedford Mass. The system, consisting of AN/FPS-77, digital interface, microwave transmitter-receiver, video integrator and computer, was installed in late 1972. Since that time the system has been used in conjunction with a network of 26 automated weather stations to make experimental forecasts of visibility and severe-weather conditions. The radar output of digital maps of radar intensity was found to be very convenient, but the inability of the radar to detect small water droplets limits the use in low visibility forecasting primarily to heavy rain storms and snow storms. In severe storms modest success was attained forecasting gusts, using digital maps. The large amounts of weather information from the network and radar frequently saturated the forecaster making forecasts at 15-min intervals, and relief was sought in the form of objective aids. Preliminary relationships between radar intensity, extinction coefficient (visibility) and rainfall rate have been formulated. In addition, a technique was developed using digital radar maps to obtain motion vectors and make probability forecasts of severe weather conditions. The calibration procedure relies on intensity of ground targets (hills and towers) for day-to-day relative calibration, and absolute calibration has been limited to Z-R relations. (Author).

At the Air Force Geophysics Laboratory (AFGL) Weather Test Facility (WTF) at Otis AFB, MA, a network of cloud base height, visibility, and wind measuring instruments were used to explore techniques for the short range prediction of low cloud ceiling. AFGL developed this system in response to the USAF Air Weather Service's requirements to modernize its basic weather support capabilities. This system allowed AFGL to evaluate the ability of statistical forecasting techniques to provide decision assistance significantly improved over the decision assistance currently provided by climatology and persistence. The approach relies upon the use of a hierarchical clustering algorithm to transform the raw cloud base height data into an automated low cloud observation. Four prediction techniques (Regression Estimation of Event Probabilities, Equivalent Markov, climatology, and persistence) yielding probability estimates of low cloud ceiling were evaluated and comparisons made to determine their respective accuracy and reliability. In addition, thresholding techniques were used to convert probability forecasts (unit bias, maximum probability, iterative, and persistence). Analysis of the data collected at the AFGL WTF demonstrates the accuracy and reliability of the automated low cloud prediction system. Regression estimation of event probabilities provided accurate, reliable, high resolution probability forecasts with results superior to climatology, persistence, and Equivalent Markov.

A microprocessor-based automated airfield weather observing and forecasting system called MAWS (Modular Automated Weather System) was developed to demonstrate the feasibility of modernizing many of the observing and forecasting functions performed in operational base weather stations. Scott Air Force Base, Illinois, was chosen as the demonstration site and operations were conducted from January 1977 through January 1979. Weather sensors at five observation sites around the airfields were polled several times each minute, the data transmitted over commercial, voice-grade telephone lines to a central supervisory microprocessor where the data were suitably collated, averaged and formatted for display on alpha-numeric display devices at key locations and for magnetic tape archiving for post analysis. The demonstration confirmed that modernized weather support can be largely achieved with state-of-the-art, commercially available hardware/software. Such a system would be compatible with other automation efforts in civilian weather services and other C-cubed efforts in the DOD. The advantage of spatially and temporally detailed weather information in marginal and adverse situations was documented. Sensor siting considerations were addressed in relation to specific weather elements and observational requirements. The contributions of automated met watch procedures and short-range guidance forecasts of RVR landing minima were demonstrated. Feedback on system performance and acceptability was obtained from cognizant AWS offices.

The Hanscom AFB mesonetwork was established to examine the potential forecasting benefits to be derived from automated mesoscale observations. Experiments were set up to evaluate three objective procedures for prediction short-period (15 to 60 min) fluctuations in sensor equivalent visibility against a specialized conditional climatology model. The procedures evaluated were: a translation technique based on vectors determined by tracking mesoscale disturbances objectively, an advection technique which utilized forecast wind fields from a limited-area fine-mesh model, and a simple area-averaged trend technique. Data from 24 episodes of reduced visibility gathered during the 1972-74 period of mesonetwork operations were used to evaluate the techniques. The translation and area-averaged trend technique yielded positive skill relative to the conditional climatology model, while the advection technique did not. Although there were episodes in which the spatial selectivity of the translation technique yielded significant improvements, the trend technique was an overall better forecast indicator, especially when one considers its relative simplicity. (Author).