高级检索
周菲凡, 叶一苇, 段晚锁, 等. 2022. 伴随敏感性方法、第一奇异向量方法以及条件非线性最优扰动方法在台风目标观测敏感区识别中的比较研究[J]. 大气科学, 46(3): 677−690. doi: 10.3878/j.issn.1006-9895.2202.22008
引用本文: 周菲凡, 叶一苇, 段晚锁, 等. 2022. 伴随敏感性方法、第一奇异向量方法以及条件非线性最优扰动方法在台风目标观测敏感区识别中的比较研究[J]. 大气科学, 46(3): 677−690. doi: 10.3878/j.issn.1006-9895.2202.22008
ZHOU Feifan, YE Yiwei, DUAN Wansuo, et al. 2022. Comparisons of Adjoint Sensitivity, Leading Singular Vector, and Conditional Nonlinear Optimal Perturbations in the Identification of Sensitive Areas for Tropical-Cyclone-Targeted Observations [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 677−690. doi: 10.3878/j.issn.1006-9895.2202.22008
Citation: ZHOU Feifan, YE Yiwei, DUAN Wansuo, et al. 2022. Comparisons of Adjoint Sensitivity, Leading Singular Vector, and Conditional Nonlinear Optimal Perturbations in the Identification of Sensitive Areas for Tropical-Cyclone-Targeted Observations [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(3): 677−690. doi: 10.3878/j.issn.1006-9895.2202.22008

伴随敏感性方法、第一奇异向量方法以及条件非线性最优扰动方法在台风目标观测敏感区识别中的比较研究

Comparisons of Adjoint Sensitivity, Leading Singular Vector, and Conditional Nonlinear Optimal Perturbations in the Identification of Sensitive Areas for Tropical-Cyclone-Targeted Observations

  • 摘要: 本文通过深入分析伴随敏感性(ADS)方法、第一奇异向量(LSV)方法、以及条件非线性最优扰动(CNOP)方法在目标观测敏感区识别方面的原理,提出了非线性程度的概念和计算方法,考察了转向型和直线型台风的非线性程度,分析了上述三种方法在不同非线性程度下识别的敏感区的异同,同时对比了转向型和直线型台风的敏感区的差异,并通过敏感性试验探讨了在不同非线性程度下以及在转向型与直线型台风中,预报对敏感区内初值的敏感性程度,进而探讨台风目标观测在不同情况下的有效性。结果表明,转向型台风的非线性程度差别比较大,或者特别强,或者特别弱;而直线型台风非线性程度居中,不同台风个例之间的非线性程度差别较小。对于非线性较弱的台风,三种方法识别的敏感区较为相似,而对于非线性较强的台风,LSV方法与ADS方法识别的敏感区较为相似,但是与CNOP方法识别的敏感区具有较大的差别。对于转向型台风,敏感区主要位于行进路径的右前方,而对于直线型台风,敏感区主要位于初始台风位置的后方。敏感性试验表明,不论台风非线性强弱,转向还是直行,CNOP敏感区内的随机扰动发展最大,而LSV敏感区内叠加的随机扰动发展次之,ADS敏感区内叠加的扰动发展最小;此外,非线性弱的台风,扰动的发展大于非线性强的台风的扰动的发展,表明非线性弱的台风预报受初值影响更大,目标观测的效果可能会更明显。

     

    Abstract: By analyzing how adjoint sensitivity (ADS), leading singular vector (LSV), and conditional nonlinear optimal perturbation (CNOP) methods are used to identify sensitive areas for target observation, the authors developed the concept of nonlinear degree. Moreover, the authors investigated the nonlinear degrees of straight and recurved types of typhoons. Subsequently, the sensitive areas identified using the three methods indicated earlier under different nonlinear degrees, together with the sensitive areas for straight and recurved types of typhoons, were analyzed. Finally, the sensitivity of the forecast to the initial values in the sensitive areas under different nonlinear degrees and for straight and recurved types of typhoons was explored. The results showed that the nonlinear degrees of recurved typhoons were quite different, either particularly strong or particularly weak, whereas negligible differences were observed among the nonlinear degrees of straight typhoons. For typhoons with weak nonlinearity, the sensitive areas identified using the three methods were similar, whereas for typhoons with strong nonlinearity, the sensitive areas identified by the LSV and ADS methods were similar, but they were quite different from those identified by the CNOP method. For recurved typhoons, the sensitive area was mainly located at the right front of its travel path, whereas for straight typhoons, the sensitive area was mainly located behind its travel path. The sensitivity test showed that the development of random perturbation in CNOP sensitive areas was the largest, regardless of whether the typhoon was strong nonlinear or not. In addition, the development of perturbation for weak nonlinear typhoons was greater than that for strong nonlinear typhoons. These results imply that the prediction for weak nonlinear typhoons is more sensitive to the initial uncertainty; thus, targeted observations for this kind of typhoon may be more effective.

     

/

返回文章
返回