This study investigates the impact of applying different types of initial and boundary perturbations for convective-scale ensemble data assimilation systems. Several observing system simulation experiments (OSSEs) were performed with a 2-km horizontal resolution, considering a realistic environment, taking model error into account, and combining different perturbations’ types with warm/cold start initialization. Initial perturbations produce a longlasting impact on the analysis’s quality, particularly for variables not directly linked to radar observations. Warm-started experiments provide the most accurate analysis and forecasts and a more consistent ensemble spread across the different spatial scales. Random small-scale perturbations exhibit similar results, although a longer convergence time is required to up-and-downscale the initial perturbations to obtain a similar error reduction. Adding random large-scale perturbations reduce the error in the first assimilation cycles but produce a slightly detrimental effect afterward.
CITATION STYLE
Maldonado, P., Ruiz, J., & Saulo, C. (2021). Sensitivity to Initial and Boundary Perturbations in Convective-Scale Ensemble-Based Data Assimilation: Imperfect-Model OSSEs. Scientific Online Letters on the Atmosphere, 17, 96–102. https://doi.org/10.2151/SOLA.2021-015
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