about forecasts but no responsibility for uncertainty due to insufficient evidence.

Finding: Different types and sources of uncertainty in hydrometeorological forecasts are processed by the transmitters and recipients of uncertainty information in different ways.

Recommendation 2.2: The Enterprise should signal to users the different sources of uncertainty in their probabilistic forecasts and risk communication products.


This section explores objective, statistical approaches to decision making under uncertainty as opposed to the psychological factors covered in the preceding section. In statistical decision theory all sources of uncertainty are assessed and their impact on a process of interest is quantified so that a “best” decision can be made. For decisions that use weather or seasonal climate forecasts, the sources of uncertainty include not just atmospheric processes but also any other processes that influence the consequence of the event. For instance, agricultural outcomes may be influenced by uncertainty in the market price of the product, as well as by the local weather forecast. These objective approaches provide a user with a decision, but in a practical sense individual users are not bound by these objectively produced decisions, and the psychological factors discussed in Section 2.2 will still be in play. A key advantage of analytical approaches such as statistical decision theory is that, if properly developed, they provide a formal structure for eliciting and integrating all information relevant to a particular decision process. Thus, the context for the use of hydrometeorological forecasts, as well as the sensitivity of the decisions to these forecasts, can be made clear.

The following section begins with a brief historical context and then discusses the basic concepts associated with statistical decision theory, linking to a series of examples that seek to convey some of the issues that emerge in considering decision making under uncertainty and risk in the hydrometeorological context. The section closes by outlining findings in the application of statistical decision theory, with an eye toward implications for NWS.

Historical Context

There is a long history of the use of concepts from statistical decision theory12 for the management of risk in the agriculture, water, energy, insurance, emergency planning, and business communities. The hydrometeorological community, as a provider of probabilistic information, participated in the evolution of this literature as well (e.g., Thompson and Brier, 1955; Epstein, 1962; Glahn, 1964; Murphy, 1976; Katz et al., 1982; Brown et al., 1986; Murphy and Ye, 1990; Wilks and Hamill, 1995).

The statistical decision theory framework has addressed both the derivation of “optimal” decisions in the presence of uncertainty and the associated value of information (e.g., improved forecasts or more data). The literature on statistical decision theory is quite mature with respect to both theory and to the development of case studies and examples. However, the frequency of applications for real-world decisions varies widely depending on the sector, the setting, and the dimension of the problem. Typically, decision-support systems that use statistical decision theory are developed on a case-by-case basis for a particular application, and generalized applications that facilitate their broader use are not readily available. Even if generalized applications were available, the data requirements and peculiarities of each problem might necessitate significant modifications. Where decision-support systems are used most routinely, they are embedded in either legal guidelines (e.g., federal water project design guidelines), are part of a specific corporate culture, or are developed as part of a customized software package for a production scheduling, inventory management, or protective response.

NOAA/NWS has historically supported decision-support systems in water resources management (Fread et al., 1995; Changnon, 2002; Georgakakos and Carpenter, 2005; Power et al., 2005). For example, streamflow observations and forecasts are considered in the operation of some large reservoir facilities that have competing objectives such as flood control, hydroelectric power production, ecosystem health, recreation, river transportation, and others. Disaster management agencies also routinely use flood forecasts. The decision-support systems in these cases may use simulation models for scenario analysis, or linked simulation and optimization tools.

Illustration of Seasonal Climate-related Use Scenarios

Analytic processing, of which statistical decision theory is a common example, can serve to summarize and focus the available information. A starting premise of statistical decision theory is that the key elements that characterize the decision problem can be and have been identified. This entails the identification of

  • the decision maker’s objectives, formalized by a numerical utility function that measures preferences with respect to different consequences;

  • all actions available to the decision maker;

  • the possible consequences of these actions; and

  • the conditional probability distribution of each consequence given each action.


 Also known as Bayesian decision analysis.

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