Published online by Cambridge University Press: 03 December 2009
Reliability and resolution are the two main attributes of forecast systems. These attributes statistically relate the performance of a forecast system to verifying data in an abstract sense. Forecast attributes have been separately defined in the literature for systems that generate forecasts of particular formats or types. In this chapter, statistical reliability and resolution are defined in a general sense, irrespective of the type or format of a forecast. Statistical reliability is concerned only with the form of forecasts, whereas statistical resolution is concerned only with the predictive capability of a forecast system, related to the time evolution of the system that is being forecast.
The two main attributes are independent characteristics of a forecast system and can be quantitatively assessed by a host of different verification measures. The general definition of forecast attributes allows a systematic discussion of the relationship between the verification and calibration of forecasts. Calibration as defined here is an adjustment of the form of the forecasts, to match the distribution of verifying observations that follow the issuance of forecasts of a particular form.
Resolution, as the inherent predictive value of forecast systems, is the attribute most sought after by developers of forecast systems. Reliability, however, is equally important in real world applications. That calls for the generation of a long enough record of hindcasts to allow for a good calibration of forecasts, or, preferably, for improvements in forecast systems that directly lead to better reliability.
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