The use of routine weather observations to calculate liquid water content in summertime high-elevation fog

This paper represents a stage within a larger project to estimate acid ion deposition from cloud impacting on high-elevation forests. Acid ion deposit ion depends principally on three factors: the liquid water content (LWC), t he ion concentration(s) in fog or cloud water, and the efficiency of the de position process, in the present paper, the objective is to estimate LWC on Roundtop Mountain in southern Quebec from routine meteorological measureme nts at the Sherbrooke weather station. After describing preliminary efforts, the methodology that was found to wor k best is presented. This scheme was a hybrid of applications of two statis tical nonlinear regression schemes. First, the classification and regressio n trees (CART) algorithm was applied to predict the occurrence or nonoccurr ence of fog at Roundtop. The algorithm produced by this application permitt ed 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 proces sed by a second application of CART to determine the predictors that are im portant for estimating LWC at Roundtop. Finally, these same remaining data were processed by the neuro-fuzzy inference systems (NFIS) algorithm to der ive the final prediction algorithm. This hybrid method (CART-CART-NFIS) ach ieved a correlation coefficient of 0.810, with accuracies of 0.962 and 0.66 4 for the no-fog and fog events, respectively. (Corresponding threat scores were 0.916 and 0.530, respectively.) These measures of skill were signific antly better than those obtained from initial estimates or from schemes tha t used CART alone. Although optical cloud detector and LWC data are necessary for derivation o f the fog-occurrence and LWC prediction algorithms, in the end those algori thms are applied to only the predictor data. Fog-occurrence and LWC data ar e not required, except for verification purposes. The algorithms and list o f predictors still need to be tested to determine how widely applicable the y are.