Ceiling and Visibility Forecasts via Neural Networks

Abstract

Statistical postprocessing of numerical model output can improve forecast quality, especially when model output is combined with surface observations. In this article, the development of nonlinear postprocessors for the prediction of ceiling and visibility is discussed. The forecast period is approximately 2001?05, involving data from hourly surface observations, and from the fifth-generation Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model. The statistical model for mapping these data to ceiling and visibility is a neural network. A total of 39 such neural networks are developed for each of 39 terminal aerodrome forecast stations in the northwest United States. These postprocessors are compared with a number of alternatives, including logistic regression, and model output statistics (MOS) derived from the Aviation Model/Global Forecast System. It is found that the performance of the neural networks is generally superior to logistic regression and MOS. Depending on the comparison, different measures of performance are examined, including the Heidke skill statistic, cross-entropy, relative operating characteristic curves, discrimination plots, and attributes diagrams. The extent of the improvement brought about by the neural network depends on the measure of performance, and the specific station.