Comparison of Ensemble Kalman Filter–Based Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow

Abstract

The ensemble Kalman filter (EnKF) technique is compared to other modeling approaches for a case study of banded snow. The forecasts include a 12-and 3-km grid-spaced deterministic forecast (D12 and D3), a 12-km 30-member ensemble (E12), and a 12-km 30-member ensemble with EnKF-based four-dimensional data assimilation (EKF12). In D12 and D3, flow patterns are not ideal for banded snow, but they have similar precipitation accumulations in the correct location. The increased resolution did not improve the quantitative precipitation forecast. The E12 ensemble mean has a flow pattern favorable for banding and precipitation in the approximate correct location, although the magnitudes and probabilities of relevant features are quite low. Six members produced good forecasts of the flow patterns and the precipitation structure. The EKF12 ensemble mean has an ideal flow pattern for banded snow and the mean produces banded precipitation, but relevant features are about 100 km too far north. The EKF12 has a much lower spread than does E12, a consequence of their different initial conditions. Comparison of the initial ensemble means shows that EKF12 has a closed surface low and a region of high low-to midlevel humidity that are not present in E12. These features act in concert to produce a stronger ensemble-mean cyclonic system with heavier precipitation at the time of banding.

An efficient implementation of the ensemble Kalman filter based on an iterative Sherman–Morrison formula

“…Instead, the effect of model errors can be accounted for by adding random vectors ξ i ∼ N (0, Q) to model states: x B i ← x B i + ξ i . Prior to any measurement, the forecast step provides the best estimation to the true vector state x true [28].…”

The Utility of Convection-Permitting Ensembles for the Prediction of Stationary Convective Bands

“…For example, in case studies of two convection events over central Europe, Hanley et al (2011, 2013 found that some ensemble members provided far more accurate predictions of the event than did the ''control'' members, which would have served as the sole realizations of deterministic forecasts. Similarly, simulations of nonorographically forced snowbands have highlighted that changing the initial conditions, and hence the large-scale environment, can alter the organization of the simulated precipitation (Suarez et al 2012).…”