Potgieter, A.B. and Everingham, Y.L. and Hammer, G.L. (2003) On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts. International Journal of Climatology, 23 (10). pp. 1195-1210.
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Article Link(s): http://dx.doi.org/10.1002/joc.932
Publisher URL: http://www3.interscience.wiley.com/cgi-bin/jhome/4735
Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts.
|Corporate Creators:||Department of Employment, Economic Development and Innovation (DEEDI), Agri-Science, Crop and Food Science, Emerging Technologies|
|Business groups:||Agri-Science, Crop and Food Science|
|Additional Information:||© John Wiley and Sons Ltd.|
|Keywords:||Probabilistic forecast; Southern Oscillation Index (SOI) phases; agro-climatic models; forecast quality; forecast skill.|
|Subjects:||Agriculture > Agriculture (General) > Agricultural meteorology. Crops and climate|
|Deposited On:||03 Feb 2004|
|Last Modified:||26 Oct 2011 00:01|
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