Flow-Dependent Large-Scale Blending for Limited-Area Ensemble Data Assimilation

Abstract

We propose a method of flow-dependent large-scale blending (LSB) method for limited-area model data assimilation (LAM DA). By incorporating the information from the global model (GM), LSB methods alleviate the large-scale degradation caused by limitations in the domain size and observations. Our proposed LSB method (nested EnVar) extends the static variational DA augmented by GM information (nested 3DVar) of previous studies, thus dynamically determining the relative weights of GM information based on the uncertainties in GM. The simultaneous assimilation of GM information by the nested EnVar avoids disturbing the optimal state of DA caused by independent blending. The nested EnVar is compared against the nested 3DVar and background LSB methods in the cycled assimilation experiments using a nested system of chaotic models with a single spatial dimension. We also investigate the impact of flow-dependency on the blended analysis and forecast. All LSB methods reduce the large-scale errors in LAM DA and provide better analyses and forecasts than GM downscaling. When dense and uneven observations are assimilated into the LAM domain, the nested EnVar outperforms the conventional DA and other LSB methods. By dynamically incorporating the GM uncertainty, the nested EnVar improves the analyses and their stability across scales. These results suggest that the nested EnVar is a promising alternative to traditional LSB methods in high-resolution simulations of hierarchical phenomena with high variability.