The predictability of the atmosphere is better understood by identifying the sources and evolution of errors in numerical weather prediction model forecasts. Forecast errors can originate from two primary sources: initial conditions (i.e. analysis) or the model formulation (i.e. model error). An analysis can be improved by taking additional observations in regions where small changes to the analysis will lead to large differences in the subsequent forecast (i.e. ``targeting''). These regions of high sensitivity can be identified using ensemble analysis and forecast data from an ensemble Kalman filter (EnKF) (see data assimilation research). In addition, this procedure can also be used to estimate how individual observation impact the forecast.

Previous research has shown that forecasts of extratropical transition (ET) events are very sensitive to how the tropical cyclone phases with the mid-latitude flow in the forecast model. Graduate student Ryan Torn has applied an EnKF for several ET events to determine the most sensitive regions for ET forecasts in the western Pacific Ocean. These experiments indicate that the largest sensitivities are associated with upper-level troughs upstream of the tropical cyclone. Observation impact calculations indicate that assimilating ~40 key observations can have nearly the same impact on the ET forecast as assimilating all 12,000 available observations.

Figure: Sensitivity of the 48 hour forecast of tropical cyclone minimum central pressure to the analysis of 500 hPa geopotential height (colors) for the forecast initialized 12 UTC 19 October 2004. Regions of warm (cold) colors indicate that increasing the analysis of 500 hPa height at that point will increase (decrease) the 48 hour forecast of minimum central pressure. The contours are the ensemble mean analysis of 500 hPa height.

Recent Papers:

Ancell, B., and G. J. Hakim, 2006: Comparing adjoint and ensemble sensitivity analysis. Mon. Wea. Rev. 133,  submitted. (pdf)

Dirren, S., R. D. Torn, and G. J. Hakim, 2006: A data assimilation case-study using a limited-area ensemble Kalman filter. Mon. Wea. Rev. 133, accepted. (pdf)

Hakim, G. J., and R. D. Torn, 2006: Ensemble Synoptic Analysis. Fred Sanders Monograph,  American Meteorological Society,  accepted. (pdf)

Torn, R., D., G. J. Hakim, and C. Snyder, 2006: Boundary conditions for limited-area ensemble Kalman filters. Mon. Wea. Rev. 134,  2490--2502. (pdf)

Snyder, C., and G. J. Hakim, 2005: Cyclogenetic perturbations and analysis errors decomposed into singular vectors J. Atmos. Sci. 62, 2234--2247. (pdf)

Stevens, M. R., and G. J. Hakim, 2005: Perturbation growth in baroclinic waves. J. Atmos. Sci. 62,  2847--2863. (pdf)

Hakim, G. J., 2005: Vertical structure of midlatitude analysis and forecast errors. Mon. Wea. Rev. 133,  567--578. (pdf)

Dirren, S., and G. J. Hakim, 2005: Toward the assimilation of time-averaged observations. Geophys. Res. Lett. 32, L04804, doi:10.1029/2004GL021444. (pdf)