Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (T_a) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate T_a, high-resolution hourly T_a estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h T_a observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban T_a at 1-km resolution. This approach leverages a limited number of diurnal T_a observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km T_a into estimating 1-km DTC model parameters, primarily daily maximum and minimum T_a values. This method capitalizes on MODIS LST's ability to estimate daily T_a extremes and requires only four diurnal T_a observations within a daily cycle to estimate monthly mean hourly 1-km T_a. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal T_a observations rivals that obtained using continuous 24-h T_a observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km T_a under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km T_a, which can be readily applied to other cities and holds potential for various applications.

中文翻译：

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使用昼夜温度循环模型对月平均每小时 1 km 城市气温进行卫星估计


世界各地的城市都面临着不断升级的气候变化风险，这凸显了对空间和时间分辨率城市气温 （T_a） 数据的需求。虽然卫星衍生的地表温度 （LST） 数据已被广泛用于估计 T_a，但城市地区的高分辨率小时 T_a 估计仍未得到充分开发。传统方法通常依赖于来自对地静止卫星的 LST 数据和来自气象站的 24 小时 T_a 连续观测。为了解决这些限制，我们引入了一种方法，该方法将昼夜温度循环 （DTC） 模型与随机森林模型相结合，以 1 公里的分辨率估计每月平均每小时城市 T_a。这种方法利用了来自气象站的有限数量的昼夜 T_a 观测、MODIS LST 数据和辅助信息。所提出的方法的核心思想是将月平均每小时 1 km T_a 的估计转化为估计 1 km DTC 模型参数，主要是日最大值和最小值 T_a 值。该方法利用了 MODIS LST 估计每日 T_a 极端值的能力，并且只需要在日周期内进行四次昼夜 T_a 观测即可估计每月平均每小时 1 公里 T_a。基于站点的五重交叉验证在具有不同地理和气候背景的 9 个城市中得出的总体 RMSE 值始终低于 1.0 °C。仅用四次昼夜 T_a 观测就达到了准确性，而使用连续 24 小时 T_a 观测所获得的准确性则不相上下。即使使用有限的 10 个站点训练集，大多数城市的整体 RMSE 仍低于 1.0 °C。 所提出的方法被证明对单城市和多城市建模都有效，并且可以在晴空条件下估计每天每小时 1 km T_a。综上所述，本研究为准确估计月平均每小时 1 km T_a 提供了一种可行、高效、通用的方法，可应用于其他城市，具有多种应用潜力。