<b>GloUCP</b>: A global 1 km spatially continuous urban canopy parameters for the WRF model.
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<b>Version 3 </b><b>Global Urban Canopy Parameters Dataset</b>This dataset provides <b>Glo</b>bal <b>U</b>rban <b>C</b>anopy <b>P</b>arameters (<b>GloUCP</b>) at a 1-km resolution for approximately the year 2020, which is derived from the global three-dimensional building footprint (3D-GloBFP) dataset generated by Che et al. (2024). The data is divided into 288 tiles, each stored in a compressed file, covering 15°×15° geographic regions. The corresponding geographic area for each compressed file can be identified from its filename (e.g., GloUCP-X105_119.Y15_29.zip contains data for the region spanning 105°E-120°E and 15°N-30°N, while GloUCP-X-180_-166.Y-60_-46.zip corresponds to the region between 180°W-165°W and 60°S-45°S).Each compressed file includes 255 <b>geogrid binary format files</b>, with each file representing a 1°×1° region. The specific geographic coverage of each file can be determined by consulting the <b>index file</b> and the file names, as explained in the WRF (Weather Research and Forecasting) User Guide.(e.g., 35161-35280.14281-14400 contains data for the region spanning 113°E-114°E and 29°N-30°N).<b>Global 1km Resolution Impervious Surface Fraction Data</b>In addition, this dataset provides two forms of impervious surface data for the year 2020, both derived from the Global Artificial Impervious Area (GAIA) dataset developed by Gong et al. (2020). The first is the <b>impervious surface fraction data</b>, a continuous variable at 1 km spatial resolution, representing the proportion of impervious surfaces within each grid cell, with values ranging from 0 to 1. The second is the <b>impervious surface mask data</b>, a binary classification derived from the fraction data by applying a threshold of 0.01, where a value of 1 denotes impervious surfaces and 0 denotes non-impervious surfaces. These datasets are provided alongside the GloUCP dataset and can support consistent land cover/use classification in WRF simulations.<b>APP: GloUCP Processor</b>To streamline data retrieval and facilitate dataset download, we developed a dedicated GloUCP Data Application. Users may specify the latitude and longitude bounds of their study area (e.g., 110°E–115°E, 25°N–30°N), based on which the application will automatically identify, retrieve, and merge the corresponding tiles along with the index.<br><b>References:</b>Che, Y., Li, X., Liu, X.*, Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y. 2024: 3D-GloBFP: the first global three-dimensional building footprint dataset, Earth System Science Data, 16, 5357–5374. doi: 10.5194/essd-16-5357-2024.Gong, P.*, Li, X.C., Wang, J.*, Bai, Y., Chen, B., Hu, T.Y., Liu, X.P., Xu, B., Yang, J., Zhang, W., & Zhou, Y.Y. 2020. Annual maps of global artificial impervious areas (GAIA) between 1985 and 2018. Remote Sensing of Environment, 236, 111510. doi: 10.1016/j.rse.2019.111510.<br>
# 版本3 全球城市冠层参数数据集 本数据集提供约2020年、空间分辨率为1千米的**全球城市冠层参数（Global Urban Canopy Parameters, GloUCP）**，其数据源自Che等人（2024年）发布的全球三维建筑足迹（3D-GloBFP）数据集。数据被划分为288个分幅，每个分幅以压缩文件形式存储，覆盖15°×15°的地理区域。各压缩文件的文件名可直接识别其对应的地理范围：例如`GloUCP-X105_119.Y15_29.zip`包含东经105°-120°、北纬15°-30°区域的数据，`GloUCP-X-180_-166.Y-60_-46.zip`则对应西经180°-165°、南纬60°-45°的区域。 每个压缩文件内含255个**地理网格二进制格式文件（geogrid binary format files）**，每个文件对应1°×1°的地理区域。各文件的具体覆盖范围可通过查阅**索引文件（index file）**及文件名确定，详细说明可参考WRF（Weather Research and Forecasting，天气研究与预报模型）用户指南。例如`35161-35280.14281-14400`对应的区域为东经113°-114°、北纬29°-30°。 ## 全球1千米分辨率不透水面数据集 此外，本数据集还提供两种2020年不透水面相关数据，均源自Gong等人（2020年）开发的**全球人工不透水面区（Global Artificial Impervious Area, GAIA）**数据集。第一种为**不透水面分数数据（impervious surface fraction data）**，为空间分辨率1千米的连续变量，代表每个网格单元内不透水面的占比，取值范围为0至1。第二种为**不透水面掩膜数据（impervious surface mask data）**，通过对分数数据设置0.01的阈值得到的二分类数据，其中取值1代表不透水面，取值0代表非不透水面。上述数据集与GloUCP数据集同步提供，可用于WRF模拟中一致的土地覆盖/利用分类。 ## 配套工具：GloUCP处理器 为简化数据检索流程、助力数据集下载，我们开发了专用的GloUCP数据应用程序。用户可指定研究区域的经纬度范围（例如东经110°-115°、北纬25°-30°），该应用将自动识别、获取并合并对应的分幅数据及索引文件。 ### 参考文献 1. Che Y, Li X, Liu X*, et al. 3D-GloBFP: the first global three-dimensional building footprint dataset[J]. Earth System Science Data, 2024, 16: 5357-5374. doi: 10.5194/essd-16-5357-2024. 2. Gong P*, Li X C, Wang J*, et al. Annual maps of global artificial impervious areas (GAIA) between 1985 and 2018[J]. Remote Sensing of Environment, 2020, 236: 111510. doi: 10.1016/j.rse.2019.111510.
