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Estimation of urban air temperature spatial patterns based on sensors network observations and satellite derived predictors

机译:基于传感器网络观测和卫星衍生预测因子的城市空气温度空间模式估算

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Besides new economical, managerial and social challenges associated with growing cities, the modifications caused in the energy budget of the urban surface intensifies the existing urban heat island (UHI). UHI can vary temporally and spatially according to meteorological conditions, landscape and urban typologies. Urban cover and form, as well as anthropogenic activities, pose an important effect on the city's thermal behaviour that influence UHI and therefore the quality of life of the citizens. In this study, we focus on quantifying the air temperature spatiotemporal patterns across the urban and peri-urban area of Heraklion, Greece at a grid of 100 m x 100 m cells. We use point air temperature observations from the Wireless Sensors Network of Heraklion and interpolate spatially by means of sophisticated geo-statistical modelling parameterized with satellite derived predictors. Regression kriging interpolation technique is implemented over the study area, using different predictors to minimize the uncertainty in air temperature estimation. We deal for multicollinearity between predictors and spatio-temporal correlations between measurements. A maximum magnitude of UHI~ 4 °C has been observed between 04:00-05:00 (UTC+3). Cross-validations indicate a mean MAE -0.86 °C in the estimated air temperature maps.
机译:除了与日益增长的城市相关的新经济,管理和社会挑战之外,城市表面的能源预算中造成的修改都会加强现有的城市热岛(UHI)。根据气象条件,景观和城市类型,UHI可以在时间和空间上变化。城市封面和形式以及人为活动,对城市的热行为产生了重要影响,影响了UHI,因此是公民的生活质量。在这项研究中,我们专注于量化赫拉克利昂城市城市和城市地区的空气温度时空模式,希腊在100米×100米细胞的网格上。我们使用来自赫拉克利的无线传感器网络的点空气温度观察,并通过使用卫星导出的预测器的复杂地理统计建模在空间内插入。回归Kriging插值技术在研究区域上实现,使用不同的预测器来最小化空气温度估计中的不确定性。我们在测量之间的预测器和时空相关性之间的多色性。在04:00-05:00(UTC + 3)之间观察到UHI〜4°C的最大幅度。交叉验证在估计的空气温度图中表示平均MAE -0.86°C。

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