Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Metaxa & Vassileos Pavlou Str, GR 152 36, Palea Penteli, Athens, Greece.
Environ Monit Assess. 2013 Oct;185(10):8239-58. doi: 10.1007/s10661-013-3170-y. Epub 2013 Apr 27.
The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.
由于气候变化，预计夏季平均气温以及炎热天气和热浪的频率和强度都会增加。鉴于这一后果，我们提出了一种评估大城市内部月度热浪危害和风险及其空间分布的方法。利用同化了卫星衍生的地表温度图像的简单城市气候模型，生成了城市气温场的历史数据库。然后，通过分析分布在希腊雅典的 1 公里网格上的这些每小时的空气温度，确定了在热浪事件中更有可能遭受更高温度的区域，从而估算出热浪危害。创新之处在于人工智能模糊逻辑模型，该模型通过考虑热浪的持续时间、强度和发生时间，将其从轻度到极端进行分类。随后，逐月危害被估计为在一个月内每个网格单元发生的个别热浪的累积效应。最后，通过整合计算得出的人口对热浪脆弱性的地理空间信息，生成了月度热浪风险图，这些信息来自社会经济变量。
