Empirical-statistical reconstruction of surface marine winds along the western coast of Canada

CANFIS, an empirical-statistical technique, is used to reconstruct continuo us daily surface marine winds at 6-hourly intervals at 13 Canadian buoy sit es along the western coast of Canada for the 40 yr period 1958-1997. CANFIS combines Classification and Regression Trees (CART) and the Neuro-Fuzzy In ference System (NFIS) in a 2-step procedure. CART is a tree-based algorithm used to optimize the process of selecting relevant predictors from a large pool of potential predictors. Using the selected predictors, NFIS builds a model for continuous output of the predictand. In this project we used CAN FIS to link large-scale atmospheric predictors with regional wind observati ons during a learning phase from 1990 to 1995 in order to generate empirica l-statistical relationships between the predictors and buoy winds. The larg e-scale predictors are derived from the NCAR/NCEP 40 yr reanalysis project while the buoy winds come from the Canadian Atmospheric Environment Service buoy network. Validation results with independent buoy wind data show a go od performance of CANFIS. The CANFIS winds reproduce the independent buoy w inds with greater accuracy than winds reconstructed with a stepwise multiva riate linear regression technique. In addition, they are better than the NC EP reanalyzed winds interpolated to the buoy locations. The reconstructed s tatistical winds recover more than 60% of the observed wind variance during an independent verification period. In particular, correlation coefficient s between independent buoy wind time series and CANFIS wind time series var y between 0.61 and 0.98. Our results suggest that CANFIS is a successful do wnscaling method. It is able to recover a substantial fraction of the varia tion of surface marine winds, especially along coastal regions where ageost rophic effects are relatively important.