Background Distinguishing dynamic variations of the climate from the physical urban indicators is a challenge to assess the factors affecting weather severity. Hence, the time-series of the severe weather threat index (SWEAT) were considered in the four urban areas of Turkey and Iran to identify its affecting factors among the climatic variables and urban indicators in 2018. The SWEAT data were obtained from the upper-air sounding database of the University of Wyoming. The climatic variables were extracted from the Asia Pacific data research center (APDRC). The spatial statistics for urban expansion were collected from global human built-up and settlement extent (HBASE) data sets. A quantitative measuring of the Pearson correlation test was used to expose the relationships between dependent index (SWEAT) and independent variables (climatic and anthropogenic). Results Results revealed that the high and extreme severity classes of the weather condition in the Ankara, Istanbul, Mashhad, and Tehran are estimated as 7.7% (28 days), 15.3% (56 days), 1.1% (4 days), and 4.4% (16 days), respectively. The strongest values of the annual SWEAT index, exposing the unstable and severe weather conditions, were observed for Istanbul and Ankara urban regions. This result may be corresponding to the highest values of mean annual precipitation and relative humidity in addition to the largest values of urban expansion and sprawl index. The statistical correlation tests in annual scale confirmed the effective role of climatic elements of precipitation, relative humidity, and cloudiness (R from 0.94 to 0.99) and the urban expansion indicators (R from 0.86 to 0.91) in increasing annual severe weather index of SWEAT at above 85–95% of confidence level. Conclusions The correlations between the urban expansion indicators and outcome SWEAT index can be strengthened by some climatic elements (e.g., precipitation, humidity, and cloudiness), revealing the mediator and magnifier task. However, the mentioned correlations can be weakened by another climatic variable (i.e., air temperature), revealing a moderator and modifier task. Ultimately, investigation of the weather severity indices (e.g., SWEAT index) could be applied to identify the local and regional evidence of climate change in the urban areas.

中文翻译：

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影响土耳其和伊朗城市地区恶劣天气威胁指数的因素

背景 从物理城市指标中区分气候的动态变化是评估影响天气严重性的因素的一项挑战。因此，考虑了土耳其和伊朗四个城市地区的恶劣天气威胁指数（SWEAT）的时间序列，以确定其在 2018 年气候变量和城市指标中的影响因素。怀俄明大学的空气探测数据库。气候变量来自亚太数据研究中心（APDRC）。城市扩张的空间统计数据是从全球人类建成和定居范围 (HBASE) 数据集收集的。Pearson 相关性检验的定量测量用于揭示相关指数 (SWEAT) 与自变量（气候和人为因素）之间的关系。结果 结果显示，安卡拉、伊斯坦布尔、马什哈德和德黑兰的天气状况的高和极端严重等级估计为 7.7%（28 天）、15.3%（56 天）、1.1%（4 天）和 4.4 %（16 天），分别。在伊斯坦布尔和安卡拉城市地区观察到年度 SWEAT 指数的最强值，暴露出不稳定和恶劣的天气条件。除了城市扩张和蔓延指数的最大值外，该结果可能对应于年平均降水量和相对湿度的最高值。年尺度的统计相关性检验证实了降水、相对湿度和多云等气候要素（R从0.94到0.99）和城市扩张指标（R从0.86到0.91）在增加SWEAT年恶劣天气指数中的有效作用。置信水平的 85-95% 以上。结论城市扩张指标与结果SWEAT指数之间的相关性可以通过一些气候要素（如降水、湿度和云量）来加强，揭示了中介和放大任务。然而，提到的相关性可能会被另一个气候变量（即气温）削弱，从而揭示了调节器和调节器的任务。最终，调查天气严重性指数（例如，