An application of stochastic forecasting to monthly averaged 700 mb heights.

Abstract

One month dependent and independent forecasts of the standardized height anomalies are made and compared to similar forecasts made climatologically. The multivariate autoregressive model is found to perform better on the average than climatology. Several month forecasts are also made and examined for retrogression of anomalies. For the single case studied, the model is found to move features both eastward and westward.

The statistical forecast method of multivariate autoregression is applied to a 27 year record of monthly averaged 700 mb heights. According to the theorem of predictive decomposition, a stationary series can be represented as the sum of deterministic and stochastic parts that are uncorrelated. Hence the deterministic annual cycle is removed and the statistical model applied to standardized anomalies of the 700 mb heights.

A complementary study to the numerical modeling of climate is the statistical modeling of existing climatic records. Such statistical models can be used to forecast climate variations without the physical understanding needed in the dynamical approach. Since most climatic records cover only several decades, these statistical methods are feasible for forecast periods of weeks or months.

The field of height anomalies is represented by 504 gridded values, which is far too large a dimension for multivariate autoregressive modeling. Therefore, 95 of these values representing equal areas are selected for modeling. Principal component analysis is then used to further reduce the 95 values to twenty. These twenty principal components are modeled by an objectively selected, third order multivariate autoregressive model.