BOX 3.1

Wide Breadth and Depth of User Needs

User needs for hydrometeorological information span a wide variety of parameters and time and spatial scales. For instance, minimum daily temperature is important for citrus farming but has little relevance for water conservation, where seasonal total streamflow is of primary importance. In addition, requirements for uncertainty information are not always well defined among users. Even in the cases where they are well defined, they can vary greatly among users and even for a single user who has multiple objectives. The citrus farmer may require local bias information for a 10-day temperature projection, and at the same time require probability information for a winter-season temperature and precipitation projection. The manager of a large multiobjective reservoir facility may require ensemble inflow forecast information with a seasonal (or longer) time horizon for flood and drought mitigation but with hourly resolution for hydroelectric power production.

TABLE 3.1 Forecasting System Components




Observations are the basis of verification and are critical to the data assimilation process. Observations in conjunction with historical forecasts provide a basis for forecast post-processing (e.g., Model Output Statistics, or MOS) and associated uncertainty estimates.

Data assimilation

Data assimilation blends observation and model information to provide the initial conditions from which forecast models are launched. Data assimilation can also provide the uncertainty distribution associated with the initial conditions.

Historical forecast guidance

An archive of historical model forecasts combined with an associated archive of verifying observations enables useful post-processing of current forecasts.

Current forecast guidance

Model forecasts are used by human forecasters as guidance for official NWS forecasts.


Models range from first-principle to empirical. Knowledge of the model being used and its limitations helps drive model development and model assessment.

Model development

Models are constantly updated and improved, driven by computational and scientific capabilities and, ultimately, the choice of verification measures.

Ensemble forecasting system

A collection of initial conditions, and sometimes variations in models and/or model physics, that are propagated forward by a model. The resulting collection of forecasts provides information about forecast uncertainty. Ensemble forecasting systems are developing into the primary means of forecast uncertainty production.

Forecast post-processing

Forecast post-processing projects forecasts from model space into observation space. Given a long record of historical forecasts and associated verifying observations it is possible to make the forecasts more valuable for a disparate set of forecast users. Post-processing can be cast in a probabilistic form, naturally providing quantitative uncertainty information. Examples of post-processing include bias correction, MOS, and Gaussian mixture model approaches like Bayesian model averaging (BMA).

Forecast verification

Forecast verification is the means by which the quality of forecasts is assessed. Verification provides information to users regarding the quality of the forecasts to aid in their understanding and application of the forecasts in decision making. In addition, verification provides base-level uncertainty information. Verification also drives the development of the entire forecasting system. Choices made in model development, observing system design, data assimilation, etc., are all predicated on a specified set of norms expressed through model verification choices.

and model verification to assess performance. This section focuses on the global weather modeling component of the Global Climate and Weather Modeling Branch (GMB4) and on the mesoscale weather modeling component of the Mesoscale Modeling Branch (MMB5) of EMC.

Ensemble Forecasting Systems

Ensemble forecasting systems form the heart of EMC’s efforts to provide probabilistic forecast information.6 The aim of ensemble forecasting is to generate a collection of forecasts based on varying initial conditions and model

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