A study of the Impact of Multiscale Initial Condition Perturbations Based on a Nested Ensemble Kalman Filter on Convection-Allowing Ensemble Forecasts
A separately-submitted paper showing the importance of near grid-scale IC perturbations relative to larger scale IC and physics perturbations suggests the hypothesis that a method of generating IC perturbations that are flow-dependent on all scales will improve precipitation forecast skill, compared to a common practice of using relatively coarse resolution IC perturbations.
In this study a data assimilation configuration is designed to assimilate both meso/regional-scale conventional observations as well as convective-scale radar observations using the Ensemble Square Root Filter (EnSRF). A real data case of upscale growing convection is used to demonstrate the ability of the multiscale EnSRF configuration to retrieve the regional-, meso-, and convective-scale features needed for a skillful precipitation forecast. The inner nest of convective-scale data assimilation improves the forecast skill throughout the life of the developing MCS, compared to mesoscale data assimilation only. The EnSRF configuration results in improved forecast skill at most lead times, compared to a similar 3DVar configuration, when radar radial velocity observations are assimilated. Further skill improvements are attained at many lead times by also assimilating radar reflectivity. The EnSRF provides an analysis ensemble with multiscale, flow-dependent perturbations consistent with the analysis uncertainty. The hypothesis that such perturbations can improve the sampling of IC errors in a storm scale ensemble is then tested with an OSSE to isolate the impact of IC/LBC errors from the other sources of forecast error.