The Use of Routine Weather Observations to Calculate Liquid Water Content in Summertime High-Elevation Fog

Abstract

This paper represents a stage within a larger project to estimate acid ion deposition from cloud impacting on high-elevation forests. Acid ion deposition depends principally on three factors: the liquid water content (LWC), the ion concentration(s) in fog or cloud water, and the efficiency of the deposition process. In the present paper, the objective is to estimate LWC on Roundtop Mountain in southern Quebec from routine meteorological measurements at the Sherbrooke weather station. After describing preliminary efforts, the methodology that was found to work best is presented. This scheme was a hybrid of applications of two statistical nonlinear regression schemes. First, the classification and regression trees (CART) algorithm was applied to predict the occurrence or nonoccurrence of fog at Roundtop. The algorithm produced by this application permitted the elimination of a large proportion of the data records for which fog was very unlikely to occur at Roundtop. The remaining data were then processed by a second application of CART to determine the predictors that are important for estimating LWC at Roundtop. Finally, these same remaining data were processed by the neuro-fuzzy inference systems (NFIS) algorithm to derive the final prediction algorithm. This hybrid method (CART?CART?NFIS) achieved a correlation coefficient of 0.810, with accuracies of 0.962 and 0.664 for the no-fog and fog events, respectively. (Corresponding threat scores were 0.916 and 0.530, respectively.) These measures of skill were significantly better than those obtained from initial estimates or from schemes that used CART alone. Although optical cloud detector and LWC data are necessary for derivation of the fog-occurrence and LWC prediction algorithms, in the end those algorithms are applied to only the predictor data. Fog-occurrence and LWC data are not required, except for verification purposes. The algorithms and list of predictors still need to be tested to determine how widely applicable they are.