Numerical weather prediction error over the North Pacific and western North America : an investigation with short-range ensemble techniques

• Main Author: Hacker, Joshua P.
• Language: English
• Published: 2009
• Online Access: http://hdl.handle.net/2429/13074

Numerical weather prediction models, which are discretized approximations to the physical equations of the atmosphere, are a critical part of weather forecasting. They can project an observed state of the atmosphere into the future, but forecasts have many error sources. To counter this, several forecasts made from slightly different initial states or models can be made over the same domain and time period. This approach, called ensemble forecasting, can forecast uncertainty, and produce a better average forecast. In this study, ensemble forecasts are generated and analyzed to understand the nature of initial condition (IC) and model error over the North Pacific. A poorlypredicted storm event (a bust) in Feb. 1999 is a useful case study period. To approximate different aspects of IC uncertainty, three IC-perturbation methods are used: (1) ranked perturbations that target coherent structures in the analyses; (2) perturbations that simulate differences between operational analyses from major forecast centers; and (3) random perturbations. An ensemble of different model configurations approximates model uncertainty. Ensembles are verified several ways to separate model and IC uncertainty, and evaluate ensemble performance. It is found that during the period surrounding the bust, IC error is greater than model error. But for one critical forecast, model error is unusually high while error from ICs is unusually small. Comparison with rawinsonde observations shows that differences between operational analyses cannot account for analysis error. Ensembles generated with this information show that analysis differences contain some spatial information about analysis uncertainty, but the magnitude is too small. To account for total forecast error, model uncertainty must be included with IC uncertainty. Comparing different ensembles reveals that a scaled ranked-perturbation ensembles has the best characteristics, including perturbation magnitude, uncertainty growth vs. error growth, spread-error correlation, and shape of the variance spectrum. Its properties are verified by running an independent case study, and only minor differences are found. Contributions to the field of weather prediction include a new ranked-perturbation method that results in improved short-range ensemble-mean forecasts, an understanding of the relationship between analysis error and analysis differences, and the confirmation that model uncertainty is necessary to account for short-range forecast error.