in NWS forecasts dealing with the potential rise of the Red River in 1997 contributed to insufficient preparation by the population and public agencies. This catastrophic flooding event, which caused over a billion dollars of damage, was well within the typical error of the forecast river level. But such uncertainty information was not provided to the public or even other federal agencies.

With the availability of uncertainty information, users— each with their own sensitivity to costs and losses and with varying thresholds for taking protective action—could better decide for themselves whether to take action and the appropriate level of response to hydrometeorological situations. For example, current NWS wind predictions provide limited uncertainty information; consequently, for users who experience damage at 30 mph or more, a single-valued prediction of 25 mph or even a range of say 23 to 27 mph might not prompt any protective action. A probabilistic wind prediction, however, would likely indicate a modest probability that wind speeds could exceed 30 mph. Thus, the user with a low cost/loss ratio (low cost to protect or very expensive loss) might choose to use resources for protection in such a circumstance, whereas users with a high cost/loss ratio would likely do the opposite. In this way, probabilistic information can improve use of resources and enhance protection of life and property. More complex situations arise in the case of water resources facilities that serve many users and multiple objectives (e.g., water supply, ecosystem health, hydroelectric power production, flood control). In such cases, more elaborate decision-support systems may be necessary to guide decisions that result in substantially increased benefits for all stakeholders (Loucks, 1989).

In addition to socioeconomic value and scientific validity, a third reason for provision of uncertainty information is to retain user confidence. Forecasting a single atmospheric or hydrologic evolution when considerable uncertainty exists, without effectively communicating that uncertainty, inevitably undermines user confidence since there will always be significant and unavoidable forecast errors. If users knew that a range of occurrences was possible, the credibility of the forecasting community could be maintained since a probabilistic prediction system may have indicated a significant probability for the actual occurrence.

Finally, there is also an ethical dimension to the lack of uncertainty information in most hydrometeorological predictions. The Enterprise is providing many users with deterministic forecasts for a week and beyond, implying a level of forecast accuracy and skill that does not exist. Providing such single-value forecasts at any time range is deceptive and incompatible with the well-known state of the science, which acknowledges the inherent uncertainty in prediction. By comparison, in the medical arena probabilistic prognoses are commonplace as are the probabilities of cure with various therapies. As noted in recommendation 8 of the Fair Weather report (NRC, 2003a), NWS, as “the organization responsible for setting the scientific standard for operational meteorology” should “adopt and improve probabilistic methods for communicating uncertainties in the data and forecasts where such methods are accepted as scientifically valid.”


The shift of hydrometeorological prediction to a scientifically valid approach that fully considers, communicates, validates, and appropriately applies forecast uncertainty will demand the cooperation of the entire Enterprise. For NWS, this cooperation occurs in the context of NOAA’s partnership policy, which guides the agency in interactions with others in the Enterprise (Box 1.3).

Treating uncertainty as a fundamental characteristic of hydrometeorological predictions will require new links and feedbacks among the various sectors of the Enterprise. The academic, public, and private sectors will need to cooperate in estimating and studying uncertainty, understanding user needs and capabilities, generating new products that effectively communicate uncertainty, and developing new types of public outreach and educational and training programs to promote appropriate interpretation and use of these new forecasts. In addition, uncertainty-explicit forecasts will likely foster the emergence of new intermediaries from academia and the private sector to develop decision-support systems

BOX 1.3

NOAA Partnership Policy

NOAA’s Policy on Partnerships in the Provision of Environmental Information (updated January 2006) states that “[t]he nation benefits from government information disseminated both by Federal agencies and by diverse nonfederal parties, including commercial and not-for-profit entities. NOAA recognizes cooperation, not competition, with private sector and academic and research entities best serves the public interest and best meets the varied needs of specific individuals, organizations, and economic entities. NOAA will take advantage of existing capabilities and services of commercial and academic sectors to support efficient performance of NOAA’s mission and avoid duplication and competition in areas not related to the NOAA mission. NOAA will give due consideration to these abilities and consider the effects of its decisions on the activities of these entities, in accordance with its responsibilities as an agency of the U.S. Government, to serve the public interest and advance the nation’s environmental information enterprise as a whole.”


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