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利用气象站数据评估澳大利亚气象局湿球 globe 温度模型。

Assessment of the Australian Bureau of Meteorology wet bulb globe temperature model using weather station data.

机构信息

Department of Geography, University of Georgia, Athens, GA, 30602, USA.

Department of Kinesiology, University of Georgia, Athens, GA, 30602, USA.

出版信息

Int J Biometeorol. 2018 Dec;62(12):2205-2213. doi: 10.1007/s00484-018-1624-1. Epub 2018 Oct 3.

Abstract

Exertional heat illnesses affect thousands of athletes each year and are a leading cause of death in sports. The wet bulb globe temperature (WBGT) is widely used as a heat stress metric in athletics for adjusting activities. The WBGT can be measured on-site with portable sensors, but instrument cost may provide a barrier for usage. Modeling WBGT from weather station data, then, presents an affordable option. Our study compares two WBGT models of varying levels of sophistication: the Australian Bureau of Meteorology (ABM) model which uses only temperature and humidity as inputs and a physically based model by Liljegren that incorporates temperature, humidity, wind speed, and solar radiation in determining WBGT outputs. The setting for the study is 19 University of Georgia Weather Network stations selected from across the state of Georgia, USA, over a 6-year period (2008-2014) during late summer and early fall months. Results show that the ABM model's performance relative to the Liljegren model varies based on time of day and weather conditions. WBGTs from the ABM model are most similar to those from the Liljegren model during midday when the assumption of moderately high sun most frequently occurs. We observed increasingly large positive biases with the ABM model both earlier and later in the day during periods with lower solar radiation. Even during midday, large (≥ 3 °C) underestimates may occur during low wind conditions and overestimates during periods with high cloud cover. Such differences can lead to inaccurate activity modification and pose dangers for athletes either by underestimating heat-related hazards or by imposing an opportunity cost if practice activities are limited by overestimating the heat hazard.

摘要

运动性热疾病每年影响数千名运动员,是运动中导致死亡的主要原因。湿球黑球温度 (WBGT) 广泛用作田径运动中的热应激指标,用于调整活动。WBGT 可以使用便携式传感器在现场测量,但仪器成本可能会成为使用的障碍。因此,从气象站数据建模 WBGT 提供了一种经济实惠的选择。我们的研究比较了两种具有不同复杂程度的 WBGT 模型:仅使用温度和湿度作为输入的澳大利亚气象局 (ABM) 模型和由 Liljegren 提出的物理基础模型,该模型在确定 WBGT 输出时包含温度、湿度、风速和太阳辐射。研究的地点是美国佐治亚州的 19 个佐治亚大学气象网络站,这些站点在 6 年的时间(2008-2014 年)内选择,位于夏季末和初秋期间。结果表明,ABM 模型相对于 Liljegren 模型的性能因一天中的时间和天气条件而异。当假设中度高阳光最常出现时,ABM 模型的 WBGT 与 Liljegren 模型的 WBGT 最为相似。我们观察到,随着太阳辐射的降低,ABM 模型在一天中的早些时候和晚些时候出现越来越大的正偏差。即使在中午,当风速较低时,也可能会出现较大的(≥3°C)低估,而在高云覆盖率期间则会出现高估。这些差异可能导致活动调整不准确,并因低估与热相关的危害或因高估热危害而限制实践活动而对运动员构成危险。

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