Large‐scale environment fields play an important role in the accurate prediction of typhoons. However, regional predictions for typhoons often suffer from inadequate representation of large‐scale flow pattern such as those from global models due to limited domain size and observations employed in regional models, especially when multiple typhoons that interact concurrently occur in a regional domain. This study merges the large‐scale information from global model forecasts with the mesoscale information from regional model forecasts in the hybrid ensemble‐variational (EnVar) data assimilation by adding an analysis constraint in the EnVar cost function, which is defined by the departure of the regional model EnVar analysis from the global model fields and takes advantage of flow‐dependent ensemble background error covariance for the introduction of large scales using data assimilation. The EnVar assimilation impacts of the large‐scale fields on predictions of triple typhoons are assessed by conducting cycling assimilation and forecast experiments for a 13‐day‐long period in July 2015 when three typhoons concurrently occurred. Results show that the large‐scale constraint for EnVar can clearly improve the triple‐typhoons' track and intensity forecasts of the regional model. The large‐scale information introduced by the proposed method is also shown to reduce forecast errors of wind, temperature and humidity, respectively. Predictions of the rainfall caused by typhoons are also ameliorated. Besides, the analysis‐constrained regional predictions provide better model dynamic fields in terms of sea surface pressure, geopotential height, and water vapor transport, as well as developed typhoon structures. In addition, the adaptive bias correction for radiance assimilation presents a stable performance under the influence of introducing extra background large‐scale fields. The results indicate that the large‐scale analysis constraint introduced in the hybrid EnVar takes advantages of the multiscale information from the global model and the regional model respectively, thus improving the final results of the predictions of multiple typhoons.

中文翻译：

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在区域混合EnVar数据同化中引入大规模分析约束以预测三重台风

大尺度环境场对于台风的准确预报具有重要作用。然而，由于区域规模和区域模型中采用的观测有限，台风的区域预测常常无法充分表示大规模流型，例如来自全球模型的流型，特别是当多个台风同时相互作用发生在区域域中时。本研究通过在 EnVar 成本函数中添加分析约束，将来自全球模式预测的大尺度信息与来自区域模式预测的中尺度信息融合在混合集合变分（EnVar）数据同化中，该函数由来自全局模型场的区域模型 EnVar 分析，并利用依赖于流的集合背景误差协方差来使用数据同化引入大尺度。2015年7月3个台风同时发生时，通过进行为期13天的循环同化预报实验，评估了大尺度场EnVar同化对三重台风预报的影响。结果表明，EnVar的大尺度约束可以明显改善区域模式的三重台风路径和强度预报。该方法引入的大规模信息也被证明可以分别减少风、温度和湿度的预报误差。对台风造成的降雨量的预测也有所改善。此外，分析约束的区域预测在海面压力、位势高度、水汽输送以及发达的台风结构方面提供了更好的模型动力场。此外，辐射同化的自适应偏差校正在引入额外背景大尺度场的影响下表现出稳定的性能。结果表明，混合EnVar中引入的大尺度分析约束分别利用了全球模式和区域模式的多尺度信息，从而改善了多台风预报的最终结果。