Publisher's Synopsis
This report describes an optimal ensemble forecasting model for seasonal precipitation and its error estimation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. This new CCA model includes the following features: (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States precipitation field. The predictor is the sea surface temperature.Shen, Samuel S. P. and Lau, William K. M. and Kim, Kyu-Myong and Li, GuilongGoddard Space Flight CenterLONG RANGE WEATHER FORECASTING; SPECTRAL CORRELATION; ANNUAL VARIATIONS; CANONICAL FORMS; MEAN SQUARE VALUES; ORTHOGONAL FUNCTIONS; SEA SURFACE TEMPERATURE; ERROR ANALYSIS; MATHEMATICAL MODELS; PRECIPITATION (METEOROLOGY); WEIGHTING FUNCTIONS...
