Scholarlink

<p>Coupled data assimilation (CDA) is a powerful strategy for integrating observations with coupled numerical models. This strategy holds great potential for enhancing weather and climate reanalysis and prediction. How to address cross-scale interactions in CDA is an important issue. In particular, the cross-scale interactions in the strongly coupled data assimilation (SCDA) framework pose substantial challenges. In this study, increasing the state estimation accuracy using an ensemble adjustment Kalman filter based on the two-scale Lorenz’96 (tsL96) model is investigated. Using the SCDA framework, we adopt cross-component localization factors and several covariance inflation schemes to address the filter divergence problem. The results show that ensembles of an appropriate size can achieve good assimilation results, the optimal localization parameters are scale-dependent for the model variables, and the adaptive inflation scheme outperforms the static fixed and relaxation-to-prior spread schemes. Although these experiments were carried out using an ideal framework, this study provides a valuable reference for improving estimation accuracy with the SCDA framework in operational simulation and prediction models.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

State estimation improvement in strongly coupled data assimilation with a two-scale Lorenz model

  • 期刊论文

  • 发表日期：

    2026年1月1日

查看全文
  • Yanqiu Gao,
  • Shaoting Ge,
  • Jicai Zhang,
  • Yiting Wang,
  • Qun He

摘要

Coupled data assimilation (CDA) is a powerful strategy for integrating observations with coupled numerical models. This strategy holds great potential for enhancing weather and climate reanalysis and prediction. How to address cross-scale interactions in CDA is an important issue. In particular, the cross-scale interactions in the strongly coupled data assimilation (SCDA) framework pose substantial challenges. In this study, increasing the state estimation accuracy using an ensemble adjustment Kalman filter based on the two-scale Lorenz’96 (tsL96) model is investigated. Using the SCDA framework, we adopt cross-component localization factors and several covariance inflation schemes to address the filter divergence problem. The results show that ensembles of an appropriate size can achieve good assimilation results, the optimal localization parameters are scale-dependent for the model variables, and the adaptive inflation scheme outperforms the static fixed and relaxation-to-prior spread schemes. Although these experiments were carried out using an ideal framework, this study provides a valuable reference for improving estimation accuracy with the SCDA framework in operational simulation and prediction models.