对导致登陆热带气旋快速强度变化的环境和涡旋尺度变量的低波数分析

B. Saiprasanth, Z. Haddad, S. Hristova-Veleva, F. Marks
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引用次数: 3

摘要

由于影响热带气旋强度变化的因素涉及多个尺度,因此预测热带气旋强度的快速变化非常困难。外部和内部的动力学和热力学变量以非线性的方式同时起作用,要么补充、放大、抑制,要么根本不影响TC强度。我们试图解决以下问题:影响TC内快速强度变化的外部变量和涡尺度变量的相对重要性是什么?此外,从观测的角度来看,这些变量中哪些必须优先考虑?为了回答这些问题,我们对大量具有代表性的tc进行了系统分析,利用波数(WN)滤波场的判别分析得出了具有统计意义的结论,并通过主成分分析来检测过拟合,并识别出与强度快速变化一致的变量子集(来自环境和涡旋)。我们的分析表明,少数变量对TC的快速强度变化影响最大。涡旋内部最重要的变量是最大风半径内的降水wn0、降水wn1的幅值和雨带区中层水平水汽通量辐合。同样,最重要的环境变量是最干燥空气与切变矢量的夹角和环境风切变的大小。在未来的观测和随后的数据同化工作中,必须优先考虑这些变量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A low-wavenumber analysis of the environmental and vortex-scale variables responsible for rapid intensity changes in landfalling tropical cyclones
Forecasting rapid intensity changes in tropical cyclones (TCs) is hard as the factors responsible span many scales. External and internal dynamical and thermodynamical variables act simultaneously in a nonlinear fashion, either complementing, amplifying, inhibiting or not impacting the TC intensity at all. We try to address the following question: What is the relative importance of the external and vortex-scale variables that influence rapid intensity changes within a TC? Further, which of these variables must be prioritized from an observational standpoint? To answer these questions, a systematic analysis was conducted on a large number of representative TCs to make statistically significant conclusions using discriminant analyses of wavenumber (WN) - filtered fields, with a principal component analysis to detect over-fitting and identify the subset of variables (from the environment and the vortex) consistently correlated with rapid intensity change. Our analyses indicate that a small number of variables wield the most influence on TC rapid intensity changes. The most important variables within the vortex are the WN 0 of precipitation within the radius of maximum winds, the amplitudes of WN 1 of precipitation and the mid-level horizontal moisture flux convergence in the rain band region. Likewise, the most important environmental variables are the angle of the driest air from the shear vector and the magnitude of environmental wind shear. These variables must be prioritized in future observational and consequent data assimilation efforts.
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