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• W89988950 abstract "Limits to predictability mean that in practice our projection of the future state of the atmosphere can only be probabilistic. Users of seasonal forecasts demand probabilistic forecasts for risk management and decision-making. They also require an indication of model skill which provides information about when they should use a forecast and when they should ignore it. POAMA (Predictive Ocean-Atmosphere Model for Australia) is a coupled ocean-atmosphere model used to generate seasonal forecasts based on observed initial conditions. POAMA has been run operationally by the Bureau of Meteorology since 2002, with an initial focus on the prediction of tropical sea surface temperatures, for which the model has demonstrable skill. With recent upgrades to POAMA, there has been an increasing focus on developing regional atmospheric forecasts on seasonal time scales, organised in programs such as the South East Australian Climate Initiative (SEACI). We demonstrate ways in which standard measures for the assessment of probabilistic forecast skill in meteo- rology can be incorporated into our presentation of POAMA forecasts, rather than presenting this information separately. Our current techniques for visualising the spatial and temporal distribution of skill often tend to be based on model diagnostics and are not always suitable or valuable for end users. The standard measures for the assessment of probabilistic forecast skill in meteorology such as the Brier and ROC scores are based on factorisations of the joint probability of forecasts and observations. We want to use these verification measures to improve the usefulness of probabilistic seasonal forecasts. Chaos enforces a limit on our ability to use dynamical models to predict the future. Unavoidable imperfections in our assimilation of initial conditions, and the sensitiviy of atmospheric states to fluctuations that cannot prac- tically be observed mean that we can only estimate the most probable state of the atmosphere. This uncertainty in initial state propagates through our model with time. Imperfections in the specification of model physics and the sensitivity of the model to errors smaller than numerical roundoff impose further limits on predictability. Ensemble forecasting, widely used in weather and climate forecasting, attempts to quantify unpredictability by generating multiple realisations of a forecast based upon perturbed initial conditions. In essence the starting ensemble is a coarse-grained initial probability density, and the development of the ensemble provides a coarse- grained solution for the evolution of probability density in time. The ensemble spread as projected into the model future gives us a representation of many potential future states of the system of interest. To enable forecast users to incorporate information about skill into their decision-making, we need to com- municate the degree to which the verification of the dynamical forecast ought to change the forecast user's prior belief of the event's probability. In this sense the verification provides additional information about the conditional probabilities of the events we seek to forecast. The ability of dynamical models to provide skillful forecasts varies strongly by season and area. Where it is possible to resolve this variation we should provide this information. We demonstrate a novel way of presenting forecasts and verification results for seasonal rainfall forecasts for various Australian regions using the POAMA dynamical model. A guiding assumption in our forecast product development is that forecast users are the experts in their business, and that what they need are probabilistic forecasts in which information about the level of certainty, resolution and reliability is inbuilt. Users are capable of more sophisticated decision making strategies than simply trusting or discarding a forecast. By presenting probability forecasts appropriately we can support these more advanced strategies." @default.
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• W89988950 date "2009-01-01" @default.
• W89988950 modified "2023-09-27" @default.
• W89988950 title "Combining Verification with Probabilistic Forecasts for Decision Making" @default.
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