The spatial variability of land cover in cities results in a heterogeneous urban microclimate, which is often not represented with regulatory meteorological sensor networks. Crowdsourced sensor networks have the potential to address this shortcoming with real-time and fine-grained temperature measurements across cities. We use crowdsourced data from over 500 citizen weather stations during summer in Sydney, Australia, combined with 100-m land use and Local Climate Zone (LCZ) maps to explore intra-urban variabilities in air temperature. Sydney presents unique drivers for spatio-temporal variability, with its climate influenced by the ocean, mountainous topography, and diverse urban land use. Here, we explore the interplay of geography with urban form and fabric on spatial variability in urban temperatures. The crowdsourced data consists of 2.3 million data points that were quality controlled and compared with reference data from five synoptic weather stations. Crowdsourced stations measured higher night-time temperatures, higher maximum temperatures on warm days, and cooler maximum temperatures on cool days compared to the reference stations. These differences are likely due to siting, with crowdsourced weather stations closer to anthropogenic heat emissions, urban materials with high thermal inertia, and in areas of reduced sky view factor. Distance from the coast was found to be the dominant factor impacting the spatial variability in urban temperatures, with diurnal temperature range greater for sensors located inland. Further differences in urban temperature could be explained by spatial variability in urban land-use and land-cover. Temperature varied both within and between LCZs across the city. Crowdsourced nocturnal temperatures were particularly sensitive to surrounding land cover, with lower temperatures in regions with higher vegetation cover, and higher temperatures in regions with more impervious surfaces. Crowdsourced weather stations provide highly relevant data for health monitoring and urban planning, however, there are several challenges to overcome to interpret this data including a lack of metadata and an uneven distribution of stations with a possible socio-economic bias. The sheer number of crowdsourced weather stations available can provide a high-resolution understanding of the variability of urban heat that is not possible to obtain via traditional networks.

城市土地覆盖的空间可变性导致了城市小气候的异质性，这通常没有用监管气象传感器网络来表示。众包传感器网络有可能通过跨城市的实时和细粒度温度测量来解决这一缺陷。我们使用来自澳大利亚悉尼夏季 500 多个市民气象站的众包数据，结合 100 米土地利用和当地气候区 (LCZ) 地图来探索城市内气温的变化。悉尼为时空变化提供了独特的驱动力，其气候受海洋、山区地形和多样化的城市土地利用的影响。在这里，我们探讨了地理与城市形态和结构对城市温度空间变异性的相互作用。众包数据由2个组成。300 万个数据点经过质量控制，并与来自五个天气气象站的参考数据进行了比较。与参考站相比，众包站测量的夜间温度更高，温暖的日子里最高温度更高，凉爽的日子里最高温度更低。这些差异可能是由于选址、众包气象站更靠近人为热排放、具有高热惯性的城市材料以及天空视野系数降低的区域造成的。发现与海岸的距离是影响城市温度空间变化的主要因素，内陆传感器的昼夜温度范围更大。城市温度的​​进一步差异可以用城市土地利用和土地覆盖的空间变异性来解释。整个城市的 LCZ 内部和之间的温度都不同。众包的夜间温度对周围的土地覆盖特别敏感，植被覆盖率高的地区温度较低，而地表不透水率较高的地区温度较高。众包气象站为健康监测和城市规划提供高度相关的数据，但是，在解释这些数据时需要克服几个挑战，包括缺乏元数据和可能存在社会经济偏见的站分布不均。可用的众包气象站的绝对数量可以提供对城市热量变化的高分辨率理解，这是通过传统网络无法获得的。众包的夜间温度对周围的土地覆盖特别敏感，植被覆盖率高的地区温度较低，而地表不透水率较高的地区温度较高。众包气象站为健康监测和城市规划提供高度相关的数据，但是，在解释这些数据时需要克服几个挑战，包括缺乏元数据和可能存在社会经济偏见的站分布不均。可用的众包气象站的绝对数量可以提供对城市热量变化的高分辨率理解，这是通过传统网络无法获得的。众包的夜间温度对周围的土地覆盖特别敏感，植被覆盖率高的地区温度较低，而地表不透水率较高的地区温度较高。众包气象站为健康监测和城市规划提供高度相关的数据，但是，在解释这些数据时需要克服几个挑战，包括缺乏元数据和可能存在社会经济偏见的站分布不均。可用的众包气象站的绝对数量可以提供对城市热量变化的高分辨率理解，这是通过传统网络无法获得的。众包气象站为健康监测和城市规划提供高度相关的数据，但是，在解释这些数据时需要克服几个挑战，包括缺乏元数据和可能存在社会经济偏见的站分布不均。可用的众包气象站的绝对数量可以提供对城市热量变化的高分辨率理解，这是通过传统网络无法获得的。众包气象站为健康监测和城市规划提供高度相关的数据，但是，在解释这些数据时需要克服几个挑战，包括缺乏元数据和可能存在社会经济偏见的站分布不均。可用的众包气象站的绝对数量可以提供对城市热量变化的高分辨率理解，这是通过传统网络无法获得的。