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Decide Now or Wait for the Next Forecast? Testing a Decision Framework Using Real Forecasts and Observations

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  • 1 a London, United Kingdom
  • | 2 b Know-Center GmbH, Graz, Austria
  • | 3 c Department of Meteorology, Stockholm University, Stockholm, Sweden
  • | 4 d Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
  • | 5 e Department of Earth Sciences, Uppsala University, Uppsala, Sweden
  • | 6 f Centre of Natural Hazards and Disaster Science, Uppsala University, Uppsala, Sweden
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Abstract

Users of meteorological forecasts are often faced with the question of whether to make a decision now, on the basis of the current forecast, or to wait for the next and, it is hoped, more accurate forecast before making the decision. Following previous authors, we analyze this question as an extension of the well-known cost–loss model. Within this extended cost–loss model, the question of whether to decide now or to wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions, we derive a simple simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions in most cases relative to three simpler alternative decision-making schemes, in both a simulated context and when we use reforecasts, surface observations, and rigorous out-of-sample validation of the decisions. To the best of our knowledge, this is the first time that a dynamic multistage decision algorithm has been demonstrated to work using real weather observations. Our results have implications for the additional kinds of information that forecasters of weather and climate could produce to facilitate good decision-making on the basis of their forecasts.

Significance Statement

Users of weather and climate forecasts often find themselves in a situation in which they could make a decision now or they could wait for the next forecast before they make the decision. Building on previous research, we derive a simple algorithm for approaching this situation in a logical way. Our algorithm can be used in many common forecasting situations, and we demonstrate that the algorithm works by testing it using forecasts and real temperature measurements.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Stephen Jewson, stephen.jewson@gmail.com

Abstract

Users of meteorological forecasts are often faced with the question of whether to make a decision now, on the basis of the current forecast, or to wait for the next and, it is hoped, more accurate forecast before making the decision. Following previous authors, we analyze this question as an extension of the well-known cost–loss model. Within this extended cost–loss model, the question of whether to decide now or to wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions, we derive a simple simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions in most cases relative to three simpler alternative decision-making schemes, in both a simulated context and when we use reforecasts, surface observations, and rigorous out-of-sample validation of the decisions. To the best of our knowledge, this is the first time that a dynamic multistage decision algorithm has been demonstrated to work using real weather observations. Our results have implications for the additional kinds of information that forecasters of weather and climate could produce to facilitate good decision-making on the basis of their forecasts.

Significance Statement

Users of weather and climate forecasts often find themselves in a situation in which they could make a decision now or they could wait for the next forecast before they make the decision. Building on previous research, we derive a simple algorithm for approaching this situation in a logical way. Our algorithm can be used in many common forecasting situations, and we demonstrate that the algorithm works by testing it using forecasts and real temperature measurements.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Stephen Jewson, stephen.jewson@gmail.com
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