JMA Operational Hourly Hybrid 3DVar with Singular Vector-Based Mesoscale Ensemble Prediction System

DOI Web Site Web Site 参考文献34件
  • YOKOTA Sho
    Numerical Prediction Development Center, Japan Meteorological Agency, Ibaraki, Japan Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan NOAA/Environmental Modeling Center, Maryland, USA
  • BANNO Takahiro
    Numerical Prediction Development Center, Japan Meteorological Agency, Ibaraki, Japan
  • OIGAWA Masanori
    Numerical Prediction Development Center, Japan Meteorological Agency, Ibaraki, Japan
  • AKIMOTO Ginga
    Numerical Prediction Development Center, Japan Meteorological Agency, Ibaraki, Japan
  • KAWANO Kohei
    Numerical Prediction Development Center, Japan Meteorological Agency, Ibaraki, Japan
  • IKUTA Yasutaka
    Numerical Prediction Development Center, Japan Meteorological Agency, Ibaraki, Japan Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan

この論文をさがす

説明

<p>This study hybridizes the background error covariance (BEC) of the hourly atmospheric three-dimensional variational data assimilation (3DVar) in Local Analysis (LA) operated at Japan Meteorological Agency using the flow-dependent BEC derived from the singular vector-based Mesoscale Ensemble Prediction System (MEPS) and the static BEC. The impact of introducing the hybrid BEC into the 3DVar is examined, along with its sensitivities to various factors like the ensemble size that is augmented by using lagged ensemble forecasts, the weight given to the ensemble-based component of BEC, the localization scales, and the use (or not) of the cross-variable correlation. This hybrid 3DVar system can be operated with small additional computational cost because it has no coupling with another ensemble data assimilation system. In sensitivity experiments, this hybrid 3DVar is shown to yield smaller forecast root mean square errors than the pure 3DVar, especially for surface variables. Moreover, the hybrid 3DVar shows a better equitable threat score for strong precipitation. These improvements were greater in the experiments with larger ensemble sizes that were increased by using lagged ensemble forecasts because of the reduced sampling errors in the ensemble-based BEC. These results were sensitive to the weight given to the ensemble-based BEC and the horizontal localization scale, whose optimal values were found to be approximately 0.5 and 100 km, respectively. The longer vertical correlation scale and the cross-variable correlation were also found important to create dynamically-balanced analysis, which is especially true for heavy rain cases.</p>

収録刊行物

  • 気象集誌. 第2輯

    気象集誌. 第2輯 102 (2), 129-150, 2024

    公益社団法人 日本気象学会

参考文献 (34)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ