P2.26 Exploring the Predictability of Mesoscale cyclogenesis in complex terrain using ensemble data assimilation

Wednesday, 1 September 2010
Alpine Ballroom B (Resort at Squaw Creek)
P. Alexander Reinecke, NRL, Monterey, CA; and D. R. Durran and J. D. Doyle

The proliferation of high-resolution numerical weather prediction over the last decade has been widely seen as a great achievement for forecasting mesoscale phenomena. However, many aspects regarding the predictability of the mesoscale are still unknown. One such example is the prediction of snow in the Pacific Northwest region. Such events involve the intricate interaction between mesoscale cyclogenesis, boundary layer processes, and the complex topography of the region. While these events present significant challenges for operational forecasters fundamental bounds on the predictive time scales based on initial condition uncertainties have not been established.

In this presentation, initial-condition sensitivities in short-term mesoscale forecasts of cyclogenesis and related snowstorms over the Puget Sound region of the Pacific Northwest are explored for two examples from December 2008. The Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS®) and an ensemble Kalman filter (EnKF) data assimilation system is used to construct a 100-member ensemble of initial condition perturbations. The perturbation error growth over a 36-hr forecast period is used to provide information on the predictive time scale of the event. In both cases the ensemble spread of the 850-hPa temperature grows larger than 8 C during the 36-hr forecast and the spread in the location of the mesocyclone is as large as 400 km. Such large perturbation error growth suggests that the predictive time scale for deterministic forecasts of these mesoscale cyclogenesis events can be less than 36 hours. Nevertheless, in both cases the ensemble mean forecast is remarkably consistent at increasing lead times highlighting the benefit of probabilistic mesoscale data assimilation and forecasting.

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