Abstract

In this article, the synoptic-scale spatial structures for raising tornadic and hail storms are compared by analyzing the total and anomalous variable fields from the troposphere to the stratosphere. 15 cases of tornado outbreaks and 20 cases of hail storms that occurred in the central United States during 1980–2011 were studied. The anomalous temperature–height field shows that a tornadic or hail storm usually occurs at the boundary of anomalous warm and cold air masses horizontally in the troposphere. In one side, an anomalous warm air mass in the mid-low troposphere and an anomalous cold air mass in the stratosphere are vertically separated by a positive center of height anomalies at the upper troposphere. In another side, an opposite vertical pattern shows that an anomalous cold air mass in the mid-low troposphere and an anomalous warm air mass in the stratosphere are separated by a negative center of height anomalies at the upper troposphere. Therefore, two pairs of adjacent anomalous warm/cold centers and one pair of anomalous high/low centers combining together form a major tornadic or hail storm paradigm, which can be physically considered as the storage of anomalous potential energy (APE) to generate severe weather. To quantitatively measure the APE, we define an index of anomalous convective instability (ACI) which is a difference of integrating temperature anomalies based on two vertically opposite anomalous air masses. The APE transformation to anomalous kinetic energy, which reduces horizontal and vertical gradients of temperature anomalies, produces anomalous rising and sinking flows in the lower-layer anomalous warm and cold air mass sides, respectively. The intensity of ACI index for tornadic storm cases is 1.5 times larger than that of hail storm cases in average. Thus, this expression of anomalous variables is better than total variables used in the traditional synoptic chart and the ACI index is better than other indices to detect potential tornadic and hail storms in order to understand the environmental conditions affecting severe weather in analytical and model output datasets.

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