Modern data assimilation schemes produce a "best" analysis of the current state by combining a model forecast and observations. Information from the observation is spread to the model state variables using the forecast error statistics. Whereas most operational data assimilation schemes assume these error statistics, the ensemble Kalman filter (EnKF) scheme provides flow-dependent error estimates, and in theory, more optimal usage of observations. In addition, the EnKF generates a sample of equally likely analyses that can be used to estimate the analysis error and can be used for ensemble forecasting. Since December 2004, Ryan Torn has maintained a real-time psuedo-operational Weather Research and Forecasting (WRF) model EnKF for a domain over the eastern Pacific and western North America. This system was build as a proof-of-concept test of the performance of an EnKF in a region of sparse in-situ data and to do basic research on predictability and observation network design (real time EnKF). Although the WRF EnKF system assimilates approximately only ~2% of the observations used by other operational systems, the forecasts are comparable in skill.

In addition to providing probabilistic analyses and forecasts, data from this system are being used to understand forecast sensitivity and the dynamics of weather systems.


Figure: Analysis ensemble mean SLP (contours) and spread (shading; an estimate of analysis error) from the UW WRF EnKF system valid 00 UTC 28 October 2005.



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)

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