Generalized additive models versus linear regression in generating probabilistic MOS forecasts of aviation weather parameters

Abstract

The skill of probabilistic Model Output Statistics forecasts generated from Generalized Additive Models (GAM) is compared to that of traditional multiple linear regression techniques. GAM is a nonparametric tool that makes use of the data to automatically estimate the appropriate functional (curvative) relationship for each predictor term. Forecast equations for each statistical technique are developed for nine regions encompassing a total of 90 stations in the northeastern US. Three parameters (cloud amount, ceiling height, and visibility) are forecast for eight thresholds and two lead times (12 h and 24 h). The developmental dataset consists of limited-area fine-mesh numerical model output and surface observations for the period 1984-1989. Verification on 3 yr (1990-1992) of independent data indicates a clear and consistent superiority of the GAM model over linear regression, with mean square errors generally 3%-4% lower and lead time gains of 2-9 h.