Abstract
An ensemble Kalman filter (EnKF) coupled to the Advanced Research version of the Weather Research and Forecasting (WRF) model is used to generate ensemble analyses and forecasts of a strong African easterly wave (AEW) during the African Monsoon Multidisciplinary Analysis field campaign. Ensemble sensitivity analysis is then used to evaluate the impacts of initial condition errors on AEW amplitude and position forecasts at two different initialization times.
WRF forecasts initialized at 0000 UTC 8 September 2006, prior to the amplification of the AEW, are characterized by large variability in evolution as compared to forecasts initialized 48 h later when the AEW is within a denser observation network. Short-lead-time amplitude forecasts are most sensitive to the midtropospheric meridional winds, while at longer lead times, midtropospheric θe errors have equal or larger impacts. For AEW longitude forecasts, the largest sensitivities are associated with the θe downstream of the AEW and, to a lesser extent, the meridional winds. Ensemble predictions of how initial condition errors impact the AEW amplitude and position compare qualitatively well with perturbed integrations of the WRF model.
Much of the precipitation associated with the AEW is generated by the Kain–Fritsch cumulus parameterization, thus the initial-condition sensitivities are also computed for ensemble forecasts that employ the Betts–Miller–Janjić and Grell cumulus parameterization schemes, and for a high-resolution nested domain with explicit convection, but with the same initial conditions. While the 12-h AEW amplitude forecast is characterized by consistent initial-condition sensitivity among the different schemes, there is greater variability among methods beyond 24 h. In contrast, the AEW longitude forecast is sensitive to the downstream thermodynamic profile with all cumulus schemes.
Bayesian Network Model for Flood Forecasting Based on Atmospheric Ensemble Forecasts
