风蚀因子是沙尘模式中决定起沙区域和沙尘排放量的关键参数。然而，当前模型所采用的风蚀性数据通常被假定为静态，未能充分体现沙尘源区高度异质且动态变化的特性，从而导致沙尘模拟结果存在显著误差与不确定性。为解决该问题，我们提出一种新方法，通过融合土壤湿度、植被覆盖、土壤质地及土地利用等多源数据，构建了一种基于物理过程的风蚀性数据集。该数据集以1公里分辨率覆盖全球，能够更加精细地刻画沙尘源区的空间特征。基于WRF-Chem的沙尘模拟结果表明，新数据集显著提高了沙尘过程模拟的整体性能，使用新侵蚀性数据模拟的PM10的均方根误差(RMSE)降低了32.4%，相关系数(R)相比于默认数据增加了82.4%。此外，模拟的沙尘气溶胶光学厚度(AOD)的空间分布更接近卫星AOD产品。
", "ds_source": "数据格式：二进制文件（WRF-Chem静态数据）\n数据大小：7.3GB ，压缩后89MB\n空间分辨率：1km(0.01°) \n覆盖范围：全球（-180° - 180°，-90° - 90°）\n数据时间：2022年
", "ds_process_way": "该数据采用2022年的全球地表分类数据（GLC2022,lu）、MODIS植被覆盖度（fvc）、SMAP土壤湿度(sm)及SoilGrids土壤质地数据(沙土比例psanbd)，统一重采样至1000 m（0.01°）空间分辨率。经过我们提出的算法：EROD=lu(1-fvc)psand*e^-min(0.1,sm)计算得到，最后转换为WRF-Chem等模式可用的二进制静态数据。数据范围覆盖全球，分辨率1km(0.01°)。
", "ds_quality": "type = continuous\nsigned = yes\nprojection = regular_ll\ndx = 0.01\ndy = 0.01\nknown_x = 1.0\nknown_y = 1.0\nknown_lat = -90\nknown_lon = -180\nwordsize = 4\nscale_factor = 0.001\nmissing_value = -9999\ntile_x = 36000\ntile_y = 18000\ntile_z = 3\nunits = \"fraction\"\ndescription = \"EROD\"\nendian = little
", "ds_acq_start_time": "2022-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "中国气象局乌鲁木齐沙漠气象研究所", "ds_acq_lon_east": -180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": 8848.0, "ds_share_type": "open-access", "ds_total_size": 97507347, "ds_files_count": 2, "ds_format": "bin", "ds_space_res": "1000", "ds_time_res": "1", "ds_coordinate": "无", "ds_projection": "UTM", "ds_thumbnail": "a7c656ad-9511-4f14-9a16-08549e16718a.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "ed54f0d0-61df-4225-aaf1-1a88722cfdf0", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.1515" ], "quality_level": 3, "publish_time": "2025-09-16 11:53:28", "last_updated": "2025-09-26 16:00:03", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.IDM.DB6969.2025", "i18n": { "en": { "title": "Global high-resolution soil erodibility dataset based on dust model", "ds_format": "bin", "ds_source": "Data format: Binary file (WRF Chem static data)\nData size: 7.3GB, compressed 89MB\nSpatial resolution: 1km (0.01 °)\nCoverage: Global (-180 ° -180 °, -90 ° -90 °)\nData time: 2022
", "ds_quality": "type = continuous\nsigned = yes\nprojection = regular_ll\ndx = 0.01\ndy = 0.01\nknown_x = 1.0\nknown_y = 1.0\nknown_lat = -90\nknown_lon = -180\nwordsize = 4\nscale_factor = 0.001\nmissing_value = -9999\ntile_x = 36000\ntile_y = 18000\ntile_z = 3\nunits = \"fraction\"\ndescription = \"EROD\"\nendian = little
", "ds_ref_way": "", "ds_abstract": "The wind erosion factor is a key parameter in determining the area of sand formation and the amount of sand and dust emissions in sandstorm patterns. However, the wind erosion data currently used in the model is usually assumed to be static, which fails to fully reflect the highly heterogeneous and dynamically changing characteristics of the dust source area, resulting in significant errors and uncertainties in the dust simulation results. To address this issue, we propose a new method that integrates multi-source data such as soil moisture, vegetation cover, soil texture, and land use to construct a physical process based wind erosion dataset. This dataset covers the whole world with a resolution of 1 kilometer, which can more finely depict the spatial characteristics of dust source areas. The results of sand and dust simulation based on WRF Chem show that the new dataset significantly improves the overall performance of sand and dust process simulation. The root mean square error (RMSE) of PM10 simulated using the new erosive data decreased by 32.4%, and the correlation coefficient (R) increased by 82.4% compared to the default data. In addition, the spatial distribution of simulated dust aerosol optical thickness (AOD) is closer to satellite AOD products.
", "ds_time_res": "1", "ds_acq_place": "Urumqi Desert Meteorological Institute of China Meteorological Administration", "ds_space_res": "1000", "ds_projection": "UTM", "ds_process_way": "This data adopts the 2022 Global Land Classification Data (GLC2022, lu), MODIS Vegetation Coverage (fvc), SMAP Soil Moisture (sm), and SoilGrids Soil Texture Data (sand to soil ratio psanbd), and is uniformly resampled to a spatial resolution of 1000 m (0.01 °). After our proposed algorithm: EROD=lu * (1-fvc) * psand * e ^ - min (0.1, sm), it is calculated and finally converted into binary static data that can be used in WRF Chem and other modes. The data covers the entire world with a resolution of 1km (0.01 °).
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "土壤侵蚀因子；沙尘模式" ], "ds_subject_tags": [ "大气化学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2022 ], "ds_contributors": [ { "true_name": "李火青", "email": "lihq@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "王澄海", "email": "wch@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "刘宗会", "email": "liuzh@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "王敏仲", "email": "wangmz@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "买买提艾力·买买提依明", "email": "ali@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李火青", "email": "lihq@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李火青", "email": "lihq@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" } ], "category": "沙漠与荒漠化" }