Finding 2:³ Understanding user needs and effectively communicating the value of uncertainty information for addressing those needs are perhaps the largest and most important tasks for the Enterprise. Yet, forecast information is often provided without full understanding of user needs or how to develop products that best support user decisions.

Parts of the Enterprise (e.g., within the private sector and academia) have developed a sophisticated understanding of user needs. In addition, there is a wealth of relevant knowledge in the social and behavioral sciences that could be more effectively incorporated into product research and development. Currently, this variety of resources is not being fully tapped by NOAA,⁴ and user perspectives are not incorporated from the outset of the product development process.

Recommendation 2: NOAA should improve its product development process by collaborating with users and partners in the Enterprise from the outset and engaging and using social and behavioral science expertise.

Finding 3:⁵ Enhanced Enterprise-wide educational initiatives will underpin efforts to improve communication and use of uncertainty information. There are three critical areas of focus: (1) undergraduate and graduate education; (2) recurrent forecaster training, and (3) user outreach and education.

Recommendation 3: All sectors and professional organizations of the Enterprise should cooperate in educational initiatives that will improve communication and use of uncertainty information. In particular, (1) hydrometeorological curricula should include understanding and communication of risk and uncertainty, (2) ongoing training of forecasters should expose them to the latest tools in these areas, and (3) forecast providers should help users, especially members of the public, understand the value of uncertainty information and work with users to help them effectively incorporate this information into their decisions.

Finding 4:⁶ The ability of NOAA to distribute and communicate uncertainty information is predicated on the capacity to produce post-processed probabilistic model guidance on a variety of spatial scales. Currently, NOAA maintains long-range (global) and short-range ensemble prediction systems. However, the short-range system undergoes no post-processing and uses an ensemble generation method (breeding) that may not be appropriate for short-range prediction. In addition, the short-range model has insufficient resolution to generate useful uncertainty information at the regional level. For forecasts at all scales, comprehensive post-processing is needed to produce reliable (or calibrated) uncertainty information.

Recommendation 4: NOAA should develop and maintain the ability to produce objective uncertainty information from the global to the regional scale.

Finding 5:⁷ NWS, through the National Centers for Environmental Prediction (NCEP), produces a large amount of model output from its deterministic and ensemble numerical weather prediction models. The ensemble forecasts and output from statistical post-processing (i.e., Model Output Statistics) already produce a wide variety of uncertainty information. However, both the model output and statistical information regarding its skill are difficult to access from outside NCEP. Thus, NWS is missing an opportunity to provide the underlying datasets that can drive improved uncertainty estimation and communication across the Enterprise.

Recommendation 5: To ensure widespread use of uncertainty information, NWS should make all raw and post-processed probabilistic products easily accessible to the Enterprise at full spatial and temporal resolution. Sufficient computer and communications resources should be acquired to ensure effective access by external users and NWS personnel.

Finding 6:⁸ To make effective use of uncertainty products, users need complete forecast verification information that measures all aspects of forecast performance. In addition, comprehensive verification information is needed to improve forecasting systems. Such information includes previous numerical forecasts, observations, post-processed uncer-