This project examines the impact of uncertainty and error in model formulation on errors in weather forecasts produced by ensemble prediction systems (EPSs). In an EPS, forecasts are produced by running an ensemble of forecast models in which each model starts out with a slightly different initial condition (models can also differ in their formulation), and the resulting ensemble of forecasts is analyzed statistically to produce an optimal forecast, and estimate of the error in the forecast, and (in combination with real-world observations), a set of initial conditions for the next forecast cycle. The work is based on the hypothesis that errors in model formulation (principally errors in parameterization and truncation) introduce errors into the model integrations at the scale of the parameterized processes, presumably at or near the truncation limit of the model, and these errors are propagated upscale by resolved model dynamics until they produce forecast uncertainty at synoptic scales. Because upscale propagation determines the forecast impacts at substantial lead times (day three, for instance), forecast errors due to model errors do not have any particular characteristics that would distinguish them from forecast errors due to initialization errors (which would likely undergo the same upscale propagation before affecting the forecast). Based on the above results, the PIs conjecture that the effect of model errors could be accounted for, at least approximately, by modulating the magnitude of the different error patterns in the low-dimensional vector space which contains most of the forecast uncertainty from all sources. The research has a three part agenda, in which the first part will test the hypothesis in forecasts archived in the THORPEX Interactive Ground Global Ensemble (TIGGE) data set. The TIGGE archive contains forecasts produced by a variety of ensemble prediction systems using a variety of techniques to account for errors in model formulation and intial conditions, thus allowing numerous tests of the hypothesis. The second part consists of a suite of "perfect model" experiments, in which the "true" state of the atmosphere will be taken from the same model used in the ensemble forecast system. The perfect model configuration enables experiments in which there is no model error, as the "true" system can have exactly the same physics and truncation as the forecast model. Such experiments are useful for considering other sources of forecast errors. The third part consists of forecast experiments using a state-of-the-art data assimilation system to assimilate real-world observations, and the PIs will attempt to specific challenging forecast cases, such as prediction of cyclogenesis produced from a warm-core tropical cyclone.

In addition to its scientiifc merit, the work will have societal benefit by developing a strategy to improve the quality of weather forecasts issued to the general public. The work also seeks to improve understanding of the uncertainty inherent in weather forecasts, so that information regarding the likely accuracy of forecasts can be included in forecast guidance. The work may also have applicability to climate and earth system models used to produce climate projections and long-range forecasts, and to understanding and predicting the behavior of other complex systems. In addition, the project provides support and training to a graduate student, thereby developing the workforce in this research area.

National Science Foundation (NSF)
Division of Atmospheric and Geospace Sciences (AGS)
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Eric DeWeaver
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Texas A&M University
College Station
United States
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