持续对流风暴中龙卷风强度的环境和雷达预测

IF 0.8 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES Journal of Operational Meteorology Pub Date : 2023-05-22 DOI:10.15191/nwajom.2023.1105
Michael F. Sessa, R. Trapp
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引用次数: 0

摘要

通过对300个龙卷风案例的多普勒雷达数据和环境参数的分析,提出了一个以龙卷风发生为条件的持续风暴前阶段龙卷风强度预测的替代框架。该框架建立在风暴前中气旋宽度与随后龙卷风的EF等级之间的稳健关系(R²=0.69)之上。相反,风暴前中气旋强度与EF尺度之间的线性关系要弱得多(R²=0.29)。此外,还利用每种情况的环境信息来探索环境参数与龙卷风强度之间的关系。这种关系在一定程度上取决于龙卷风强度类别的分布[即,非显著(EF0–1)与显著(EF2–5),或弱(EF0-1)与强(EF2-3)与猛烈(EF4–5)]。低水平剪切参数区分显著龙卷风和非显著龙卷风的环境,但不区分剧烈龙卷风和强龙卷风的环境。热力学参数的情况正好相反。为应对基于影响的警报,该框架的操作实施将需要对中气旋宽度以及强度和其他属性进行实时、自动的量化。这项研究表明,从龙卷风前分析中获得的信息将使运营预报员能够在龙卷风发展之前,在警告文本中了解并向公众传达有关潜在龙卷风强度的信息,以更好地保护生命和财产。目前,这些关系正被用于机器学习模型中,用于非显著龙卷风强度与显著龙卷风强度的二元预测,并在其中展示了技能。
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Environmental and Radar-Derived Predictors of Tornado Intensity within Ongoing Convective Storms
Analyses of Doppler radar data and environmental parameters for 300 tornado cases are used to propose an alternative framework for tornado intensity prediction during pretornadic stages of ongoing storms, conditional on tornadogenesis. This framework is founded on the robust relationship (R² = 0.69) between pretornadic mesocyclone width and the EF rating of the subsequent tornado. In contrast, the linear relationship between pretornadic mesocyclone intensity and EF scale is much weaker (R² = 0.29). Environmental information for each case was additionally used to explore relationships between environmental parameters and tornado intensity. Such relationships depend in part on how the tornado-intensity categories are distributed [i.e., nonsignificant (EF0–1) versus significant (EF2–5), or weak (EF0–1) versus strong (EF2–3) versus violent (EF4–5)]. Low-level shear parameters discriminate the environments of significant tornadoes from nonsignificant tornadoes, but not the environments of violent tornadoes from strong tornadoes. The converse is true for thermodynamic parameters. Operational implementation of this framework for thepurposes of impact-based warnings will require real-time, automated quantification of mesocyclone width in addition to intensity and other attributes. The information gained from the pretornadic analysis demonstrated in this study would allow an operational forecaster to be aware of—and communicate—information about potential tornado intensity in warning text to the public before a tornado develops to better protect life and property. Currently, these relationships are being utilized in machine learning models for binary prediction of non-significant versus significant tornado intensity where skill is being demonstrated.
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来源期刊
Journal of Operational Meteorology
Journal of Operational Meteorology METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
2.40
自引率
0.00%
发文量
4
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