细模态比例（FMF）对于区分天然气溶胶和人为气溶胶非常重要。然而，目前大多数基于卫星的陆地气溶胶细模分数产品都非常不可靠。在此，我们开发了一种新的基于卫星的全球陆地每日 FMF 数据集（Phy-DL FMF），该数据集在 1°空间分辨率下，通过协同物理方法和深度学习方法的优势，覆盖了 2001 至 2020 年期间。基于对来自全球 1170 个 AERONET 站点的 361089 个数据样本的分析，Phy-DL FMF 数据集与气溶胶机器人网络（AERONET）的测量结果具有可比性。总体而言，Phy-DL FMF 的均方根误差 (RMSE) 为 0.136，相关系数为 0.68，在 ±20 % 预期误差 (EE) 范围内的结果比例为 79.15 %。此外，地表辐射预算（SURFRAD）观测结果的场外验证显示，Phy-DL FMF 的均方根误差为 0.144（72.50% 的结果在 ±20 % 的预期误差（EE）范围内）。Phy-DL FMF 的性能优于其他深度学习或物理方法（如我们之前研究中提出的光谱解卷积算法），尤其是在森林、草地、耕地以及城市和贫瘠土地类型方面。作为一个长期数据集，Phy-DL FMF 能够显示出全球陆地区域总体上显著下降的趋势（显著性水平为95%）。根据对不同国家 Phy-DL FMF 的趋势分析，印度和美国西部的 FMF 上升趋势尤为明显。总之，这项研究为全球陆地地区提供了一个新的气溶胶全因子数据集，有助于提高我们对细模式和粗模式气溶胶时空变化的认识。
", "ds_source": "在本研究中，使用了 2001 至 2020 年 MODIS C6.1 L1B MOD02SSH 数据（即波段 1 至波段 7 的大气顶部（TOA）反射率）、MODIS C6.1 L3 MOD09CMG 数据（波段 1 至波段 7 的地表反射率）和 MODIS C6.1 L3 MOD08 每日数据，以检索 FMF。AERONET 提供了地面气溶胶特性的全球日空光度计网络。五个气象变量（即 2 米气温、PBLH、表面气压、10 米风分量和 2 米露点温度）均来自欧洲中程天气预报中心（ERA5）的第五代产品。
", "ds_process_way": "我们协同物理方法和深度学习的优势，利用 MODIS 数据在全球范围内检索陆地上空的气溶胶 FMF。我们利用二十年（2001-2020年）的数据对这一混合模型进行了测试和验证，并生成了一个新的长期调频数据集，称为物理-深度学习调频（Phy-DL FMF）。与之前的研究不同，所提出的混合模型同时考虑了物理特征和非线性关系，以限制FMF计算。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "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": null, "ds_share_type": "open-access", "ds_total_size": 153038533, "ds_files_count": 2, "ds_format": "Geotiff", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "cf259861-b46d-4fed-aad8-a0d293cd04dd.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2024-05-21 10:45:21", "last_updated": "2025-06-30 16:20:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6467.2024", "i18n": { "en": { "title": "Physical and Deep Learning Retrieval of Aerosol Fine Mode Proportions (Phy-DL FMF) (2001-2020)", "ds_format": "Geotiff", "ds_source": "In this study, MODIS C6.1 L1B MOD02SSH data (i.e. top of atmosphere (TOA) reflectance of bands 1 to 7), MODIS C6.1 L3 MOD09CMG data (surface reflectance of bands 1 to 7), and MODIS C6.1 L3 MOD08 daily data from 2001 to 2020 were used to retrieve FMF. AERONET provides a global network of solar photometers for ground aerosol characteristics. The five meteorological variables (i.e. 2-meter temperature, PBLH, surface pressure, 10 meter wind component, and 2-meter dew point temperature) are all derived from the fifth generation products of the European Centre for Medium Range Weather Forecasts (ERA5).
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "The fine mode ratio (FMF) is crucial for distinguishing between natural aerosols and anthropogenic aerosols. However, currently most satellite based land aerosol fine model fraction products are highly unreliable. Here, we have developed a new satellite based global land daily FMF dataset (Phy DL FMF) that covers the period from 2001 to 2020 at a spatial resolution of 1°, leveraging the advantages of collaborative physics and deep learning methods. Based on the analysis of 361089 data samples from 1170 AERONET sites worldwide, the Phy DL FMF dataset is comparable to the measurement results of the Aerosol Robot Network (AERONET). Overall, the root mean square error (RMSE) of Phy DL FMF is 0.136, the correlation coefficient is 0.68, and the proportion of results within the expected error (EE) range of ± 20% is 79.15%. In addition, off-site validation of the Surface Radiation Budget (SURFRED) observation results showed that the root mean square error of Phy DL FMF was 0.144 (72.50% of the results were within ± 20% of the expected error (EE) range). The performance of Phy DL FMF is superior to other deep learning or physical methods (such as the spectral deconvolution algorithm proposed in our previous research), especially in forest, grassland, cultivated land, as well as urban and barren land types. As a long-term dataset, Phy DL FMF is able to show a significant downward trend in the overall terrestrial regions worldwide (with a significance level of 95%). According to the trend analysis of Phy DL FMF in different countries, the upward trend of FMF is particularly evident in India and the western United States. In summary, this study provides a new aerosol full factor dataset for global land regions, which helps to enhance our understanding of the spatiotemporal variations of fine and coarse aerosol patterns.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "We leverage the advantages of collaborative physics methods and deep learning to retrieve aerosol FMFs over land using MODIS data on a global scale. We tested and validated this hybrid model using data from twenty years (2001-2020) and generated a new long-term frequency modulation dataset called Physics Deep Learning Frequency Modulation (Phy DL FMF). Unlike previous studies, the proposed hybrid model considers both physical characteristics and nonlinear relationships to limit FMF calculations.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation method provided in this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "气溶胶", "MODIS 数据", "细模态比例FMF" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "晏星", "email": "yanxing@bnu.edu.cn", "work_for": " 北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "晏星", "email": "yanxing@bnu.edu.cn", "work_for": " 北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "晏星", "email": "yanxing@bnu.edu.cn", "work_for": " 北京师范大学", "country": "中国" } ], "category": "大气本底" }