| 1. |
- Jewson, Stephen, et al.
(författare)
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Communicating Properties of Changes in Lagged Weather Forecasts
- 2022
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Ingår i: Weather and forecasting. - : American Meteorological Society. - 0882-8156 .- 1520-0434. ; 37:1, s. 125-142
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Tidskriftsartikel (refereegranskat)abstract
- Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.
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| 2. |
- Jewson, Stephen, et al.
(författare)
-
Communicating Properties of Changes in Lagged Weather Forecasts
- 2022
-
Ingår i: Weather and forecasting. - 0882-8156 .- 1520-0434. ; 37:1, s. 125-142
-
Tidskriftsartikel (refereegranskat)abstract
- Weather forecasts, seasonal forecasts, and climate projections can help their users make good decisions. It has recently been shown that when the decisions include the question of whether to act now or wait for the next forecast, even better decisions can be made if information describing potential forecast changes is also available. In this article, we discuss another set of situations in which forecast change information can be useful, which arise when forecast users need to decide which of a series of lagged forecasts to use. Motivated by these potential applications of forecast change information, we then discuss a number of ways in which forecast change information can be presented, using ECMWF reforecasts and corresponding observations as illustration. We first show metrics that illustrate changes in forecast values, such as average sizes of changes, probabilities of changes of different sizes, and percentiles of the distribution of changes, and then show metrics that illustrate changes in forecast skill, such as increase in average skill and probabilities that later forecasts will be more accurate. We give four illustrative numerical examples in which these metrics determine which of a series of lagged forecasts to use. In conclusion, we suggest that providers of weather forecasts, seasonal forecasts, and climate projections might consider presenting forecast change information, in order to help forecast users make better decisions.
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| 3. |
- Jewson, Stephen, et al.
(författare)
-
Decide Now or Wait for the Next Forecast? Testing A Decision Framework Using Real Forecasts and Observations
- 2021
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Ingår i: Monthly Weather Review. - : American Meteorological Society. - 0027-0644 .- 1520-0493. ; 149:6, s. 1637-1650
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Tidskriftsartikel (refereegranskat)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.
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| 4. |
- Jewson, Stephen, et al.
(författare)
-
Decide Now or Wait for the Next Forecast? Testing a Decision Framework Using Real Forecasts and Observations
- 2021
-
Ingår i: Monthly Weather Review. - 0027-0644 .- 1520-0493. ; 149:6, s. 1637-1650
-
Tidskriftsartikel (refereegranskat)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.
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| 5. |
- Jewson, Stephen, et al.
(författare)
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Developing Representative Impact Scenarios From Climate Projection Ensembles, With Application to UKCP18 and EURO-CORDEX Precipitation
- 2023
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Ingår i: Journal of Advances in Modeling Earth Systems. - 1942-2466. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Calculating impacts from climate projection ensembles can be challenging. A simple approach might consider just the ensemble mean, but this ignores much of the information in the ensemble and does not explore the range of possible impacts. A more thorough approach would consider every ensemble member, but may be computationally unfeasible for many impact models. We investigate the compromise in which we represent the ensemble by the mean and a single deviation from the mean. The deviation from the mean would ideally be representative both of variability in the ensemble, and have a significant impact, according to some impact metric. We compare methods for calculating the deviation from the mean, based on traditional compositing and a statistical method known as Directional Component Analysis (DCA). DCA is based on linearizing the impact metric around the ensemble mean. We illustrate the methods with synthetic examples, and derive new mathematical results that clarify the interpretation of DCA. We then use the methods to derive scenarios from the UKCP18 and EURO-CORDEX projections of future precipitation in Europe. We find that the worst ensemble member is not robust, but that deviations from the ensemble mean calculated using compositing and DCA are robust. They thus give robust insight into the patterns of change in the ensemble. We conclude that mean and representative deviation methods may be suitable for climate projection users who wish to explore the implications of the uncertainty around the ensemble mean without having to calculate the impacts of every ensemble member.
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| 6. |
- Jewson, Stephen, et al.
(författare)
-
Developing Representative Impact Scenarios From Climate Projection Ensembles, With Application to UKCP18 and EURO‐CORDEX Precipitation
- 2022
-
Ingår i: Journal of Advances in Modeling Earth Systems. - : John Wiley & Sons. - 1942-2466. ; 15:1
-
Tidskriftsartikel (refereegranskat)abstract
- Calculating impacts from climate projection ensembles can be challenging. A simple approach might consider just the ensemble mean, but this ignores much of the information in the ensemble and does not explore the range of possible impacts. A more thorough approach would consider every ensemble member, but may be computationally unfeasible for many impact models. We investigate the compromise in which we represent the ensemble by the mean and a single deviation from the mean. The deviation from the mean would ideally be representative both of variability in the ensemble, and have a significant impact, according to some impact metric. We compare methods for calculating the deviation from the mean, based on traditional compositing and a statistical method known as Directional Component Analysis (DCA). DCA is based on linearizing the impact metric around the ensemble mean. We illustrate the methods with synthetic examples, and derive new mathematical results that clarify the interpretation of DCA. We then use the methods to derive scenarios from the UKCP18 and EURO-CORDEX projections of future precipitation in Europe. We find that the worst ensemble member is not robust, but that deviations from the ensemble mean calculated using compositing and DCA are robust. They thus give robust insight into the patterns of change in the ensemble. We conclude that mean and representative deviation methods may be suitable for climate projection users who wish to explore the implications of the uncertainty around the ensemble mean without having to calculate the impacts of every ensemble member.
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| 7. |
- Scher, Sebastian, et al.
(författare)
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Robust Worst-Case Scenarios from Ensemble Forecasts
- 2021
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Ingår i: Weather and forecasting. - : American Meteorological Society. - 0882-8156 .- 1520-0434. ; 36:4, s. 1357-1373
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Tidskriftsartikel (refereegranskat)abstract
- To extract the most information from an ensemble forecast, users would need to consider the possible impacts of every member in the ensemble. However, not all users have the resources to do this. Many may opt to consider only the ensemble mean and possibly some measure of spread around the mean. This provides little information about potential worst-case scenarios. We explore different methods to extract worst-case scenarios from an ensemble forecast, for a given definition of severity of impact: taking the worst member of the ensemble, calculating the mean of the N worst members, and two methods that use a statistical tool known as directional component analysis (DCA). We assess the advantages and disadvantages of the four methods in terms of whether they produce spatial worst-case scenarios that are not overly sensitive to the finite size and randomness of the ensemble or small changes in the chosen geographical domain. The methods are tested on synthetic data and on temperature forecasts from ECMWF. The mean of the N worst members is more robust than the worst member, while the DCA-based patterns are more robust than either. Furthermore, if the ensemble variability is well described by the covariance matrix, the DCA patterns have the statistical property that they are just as severe as those from the other two methods, but more likely. We conclude that the DCA approach is a tool that could be routinely applied to extract worst-case scenarios from ensemble forecasts.
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