Version 1
: Received: 13 February 2020 / Approved: 16 February 2020 / Online: 16 February 2020 (13:28:53 CET)
Version 2
: Received: 7 May 2020 / Approved: 8 May 2020 / Online: 8 May 2020 (04:28:30 CEST)
Jewson, S.; Scher, S.; Messori, G. Decide Now or Wait for the Next Forecast? Testing A Decision Framework Using Real Forecasts and Observations. Monthly Weather Review, 2021. https://doi.org/10.1175/mwr-d-20-0392.1.
Jewson, S.; Scher, S.; Messori, G. Decide Now or Wait for the Next Forecast? Testing A Decision Framework Using Real Forecasts and Observations. Monthly Weather Review, 2021. https://doi.org/10.1175/mwr-d-20-0392.1.
Jewson, S.; Scher, S.; Messori, G. Decide Now or Wait for the Next Forecast? Testing A Decision Framework Using Real Forecasts and Observations. Monthly Weather Review, 2021. https://doi.org/10.1175/mwr-d-20-0392.1.
Jewson, S.; Scher, S.; Messori, G. Decide Now or Wait for the Next Forecast? Testing A Decision Framework Using Real Forecasts and Observations. Monthly Weather Review, 2021. https://doi.org/10.1175/mwr-d-20-0392.1.
Abstract
Users of meteorological forecasts are often faced with the question of whether to make a decision now based on the current forecast or whether to wait for the next and hopefully more accurate forecast before making the decision. One would imagine that the answer to this question should depend on the extent to which there is a benefit in making the decision now rather than later, combined with an understanding of how the skill of the forecast improves, and information about the possible size and nature of forecast changes. We extend the well-known cost-loss model for forecast-based decision making to capture an idealized version of this situation. We find that within this extended cost-loss model, the question of whether to decide now or 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 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 relative to three simpler alternative decision-making schemes. Similar problems have been studied in many other fields, and we explore some of the connections.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
8 May 2020
Commenter:
Stephen Jewson
Commenter's Conflict of Interests:
Author
Comment:
1) We've described now connections to similar methods in other fields in more detail at various points in the article. 2) We've described an alternative solution method based on numerical integration rather than simulation 3) We've used a different data set for the example. The results are now much better and very clearly show that the method we describe leads to better decisions.
Commenter: Stephen Jewson
Commenter's Conflict of Interests: Author
2) We've described an alternative solution method based on numerical integration rather than simulation
3) We've used a different data set for the example. The results are now much better and very clearly show that the method we describe leads to better decisions.