Statistical Analysis of Interrelationships between Objective and Subjective Temperature Forecasts

Abstract

In this paper we investigate the interrelationships between objective and subjective temperature forecasts. An information-content approach is adopted within the overall context of a general framework for forecast verification. This approach can be used to address questions such as whether the subjective forecasts contain information regarding the corresponding observed temperatures that is not included in the objective forecasts. Two methods of analysis are employed: 1) ordinary least squares regression analysis and 2) a Bayesian information-content analysis. Maximum and minimum temperature forecasts formulated operationally for six National Weather Service offices during the period 1980?86 are analyzed. Results produced by the two methods are quite consistent and can be summarized as follows: 1) the subjective forecasts contain information not included in the objective forecasts for all cases (i.e., stratifications) considered and 2) the objective forecasts contain information not included in the subjective forecasts in a substantial majority of these cases. Generally, the incremental information content in the subjective forecasts considerably exceeds the incremental information content in the objective forecasts. The implications of these results for operational short-range temperature forecasting are briefly discussed.