Resolution of probabilistic weather forecasts with application in disease management

G Hughes, N McRoberts, FJ Burnett

Research output: Contribution to journalArticle

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.
Original languageEnglish
Pages (from-to)158 - 162
Number of pages5
JournalPhytopathology
Volume107
Issue number2
Early online date22 Dec 2016
DOIs
Publication statusFirst published - 22 Dec 2016

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weather
entropy
forecast
risk factor
divergence
methodology
evaluation
measuring

Bibliographical note

1030769

Keywords

  • Forecast evaluation
  • Forecast skill
  • Information theory

Cite this

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Resolution of probabilistic weather forecasts with application in disease management. / Hughes, G; McRoberts, N; Burnett, FJ.

In: Phytopathology, Vol. 107, No. 2, 22.12.2016, p. 158 - 162.

Research output: Contribution to journalArticle

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KW - Forecast evaluation

KW - Forecast skill

KW - Information theory

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