A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site–year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling. Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).

中文翻译：

---

来自 20 个城市通量塔站点的协调空白数据集

共有 20 个城市街区尺度的涡度协方差通量塔数据集在协调创建一个 50 个站点年的集合后公开可用，这些集合在气候和城市表面特征方面具有广泛的多样性。作为地表模型输入所需的变量（入射辐射、温度、湿度、气压、风和降水）经过质量控制、填补空白，并预先添加了 10 年的再分析衍生本地数据，从而能够扩展到平衡模型与当地的气候条件。对于间隙填充和旋转，ERA5 再分析气象数据使用基于塔的观测进行偏差校正，考虑了 ERA5 中未建模的昼夜、季节和当地城市效应。与其他数据集中使用的方法（例如 WFDE5 或 FLUXNET2015）。其他变量（湍流和上升流辐射通量）在没有间隙填充的情况下得到协调和质量控制。场地描述元数据包括当地土地覆盖率（建筑物、道路、树木、草地等）、建筑物高度和形态、空气动力学粗糙度估计、人口密度和卫星图像。这个开放的集合可以通过观察综合研究或在广泛的城市环境中评估地表环境模型来帮助扩展我们对城市环境过程的理解。这些数据可以从 空气动力学粗糙度估计、人口密度和卫星图像。这个开放的集合可以通过观察综合研究或在广泛的城市环境中评估地表环境模型来帮助扩展我们对城市环境过程的理解。这些数据可以从 空气动力学粗糙度估计、人口密度和卫星图像。这个开放的集合可以通过观察综合研究或在广泛的城市环境中评估地表环境模型来帮助扩展我们对城市环境过程的理解。这些数据可以从 https://doi.org/10.5281/zenodo.7104984 （利普森等人，2022 年）。