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

Abstract

CANFIS, an empirical-statistical technique, is used to reconstruct continuous daily surface marine winds at 6-hourly intervals at 13 Canadian buoy sites along the western coast of Canada for the 40 yr period 1958-1997. CANFIS combines Classification and Regression Trees (CART) and the Neuro- Fuzzy Inference 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 observations during a learning phase from 1990 to 1995 in order to generate empirical-statistical relationships between the predictors and buoy winds. The large-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. Vali- dation results with independent buoy wind data show a good performance of CANFIS. The CANFIS winds reproduce the independent buoy winds with greater accuracy than winds reconstructed with a stepwise multivariate linear regression technique. In addition, they are better than the NCEP reana- lyzed winds interpolated to the buoy locations. The reconstructed statistical winds recover more than 60% of the observed wind variance during an independent verification period. In particular, correlation coefficients between independent buoy wind time series and CANFIS wind time series vary between 0.61 and 0.98. Our results suggest that CANFIS is a successful downscaling method. It is able to recover a substantial fraction of the variation of surface marine winds, especially along coastal regions where ageostrophic effects are relatively important.