本图集应用高时空分辨率的全国地面气象驱动格网数据集和积雪地面调查数据（1980-2020年），通过提取中国区域冬季大气温度、降水量和近地表风速信息，基于冬季气象要素的二叉树积雪类型划分方法，采用Sturm等（1995）提出的季节性积雪类型划分体系，对中国区域的积雪类型进行了划分和验证。相比Sturm等（1995）的积雪分类结果空间分辨率显著提高。积雪分类结果表明：中国区域的积雪类型划分为5种，分别是大草原型、泰加林型、苔原型、高山型及瞬时型，与Sturm等（1995）的分类描述有所不同。本图集共包含1个图件，及对应的1个制图数据，完全开放共享。", "ds_source": "
本文采用的1980-2014年中国区域高时空分辨率地面气象要素驱动数据集，该数据是中国科学院青藏高原研究所提供的多源融合格网数据集。\n
验证数据为项目收集整理的2008-2019中国积雪历史数据集（2008-2019年）及本次地面调查的积雪特性数据（2017-2020）。", "ds_process_way": "
本图集应用1979-2014年高时空分辨率的全国地面气象驱动格网数据集（10km×10km），通过MATLAB软件编程提取中国区域冬季大气温度、降水量和近地表风速信息，采用Sturm等（1995）提出的二叉树季节性积雪类型划分体系，对中国区域的积雪类型进行了划分，在二叉树系统中，为了确定分类使用的气象参数中的冬季大气温度、降水量、风速的临界值，以及表示冬季温度和持续时间的共同作用，定义了冬季温度和持续时间的指数——月冷度（CDM），作为温度划分的指标，如等式（1）所示。\n
式中：m 是一年中的月份（1-12）。 是月平均大气温度， 是临界温度，当月平均温度大于或等于临界温度的时候，月冷度为0；当月平均温度小于临界温度的时候，月冷度为累积的两者差值的累积值。月冷度数值越大，表明冬季大气温度越低。认定临界值为本地积雪能够存在的温度，并用临界值定义冬季月份。基于选定的气象阈值，利用SHL积雪分类方法得到中国区域积雪类型分类图，随后对分类后结进行过滤、平滑和移除小孤立区处理最终得到中国区Sturm-Holmgren-Liston体系积雪分类图，再利用ArcGIS软件对数据集进行专题图的绘制。", "ds_quality": "
数据质量控制：相比于Sturm等（1995）应用60年月平均全球气象资料绘制的0.5°×0.5°北半球积雪类型分类图，本图集的结果在空间分辨率上得到了显著提高，其空间分辨率为10km×10km，在空间上对积雪类型有了更细致的划分，尤其在新疆塔克拉玛干沙漠地区基，Sturm等（1995）人的全球积雪分类结果显示为大草原型积雪，本图集数据的积雪类型是瞬时型积雪，这更符合实际情况。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 546643, "ds_files_count": 2, "ds_format": "tif.geottiff", "ds_space_res": "0.1°×0.1°", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "51507b48-f3f0-4e2e-ac5e-e5fa3e84f3bd.png", "ds_thumb_from": 0, "ds_ref_way": "李晓峰，中国1:100万Sturm-Holmgren体系积雪分类图（1980-2020年），国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2023，doi：10.12072/ncdc.isnow.db2709.2023", "paper_ref_way": "", "ds_ref_instruction": "
本文采用的1980-2014年中国区域高时空分辨率地面气象要素驱动数据集，该数据是中国科学院青藏高原研究所提供的多源融合格网数据集。\r\n验证数据为项目收集整理的2008-2019中国积雪历史数据集（2008-2019年）及本次地面调查的积雪特性数据（2017-2020）。", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "09314967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.isnow.db2709.2023", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-01-25 16:49:26", "last_updated": "2023-02-16 08:50:21", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2709.2023", "i18n": { "en": { "title": "Snow classification map of Sturm-Holmgren system in China (1980-2020)", "ds_format": "", "ds_source": "
\nIn this paper, the high spatial-temporal resolution ground meteorological element driven data set of China from 1980 to 2014 is used. This data is a multi-source fusion grid data set provided by the Institute of Qinghai-Tibet Plateau, Chinese Academy of Sciences.\n
The verification data are the historical data set of snow cover in China (2008-2019) collected and sorted by the project and the snow cover characteristics data of this ground survey (2017-2020).", "ds_quality": "
\nData quality control: compared with 0.5 ° plotted by Sturm et al × 0.5 ° classification map of snow cover types in the northern hemisphere. The results of this atlas have been significantly improved in spatial resolution, with a spatial resolution of 10km × 10km, there is a more detailed division of snow cover types in space, especially in the Taklimakan Desert region of Xinjiang. The global snow cover classification results of Jiji Sturm et al. (1995) show that it is prairie type snow cover. The snow cover type of this atlas data is instantaneous type snow cover, which is more in line with the actual situation.", "ds_ref_way": "", "ds_abstract": "
\nThis atlas applies the national surface meteorological driving grid data set with high spatial and temporal resolution and snow cover ground survey data (1980-2020) to divide and verify the snow cover types in China by extracting the winter atmospheric temperature, precipitation and near-surface wind speed information in China, and the binary tree snow type division method based on winter meteorological elements, using the seasonal snow cover type division system proposed by Sturm et al. (1995). Compared with Sturm et al. (1995), the spatial resolution of snow classification results is significantly improved. The snow cover classification results show that the snow cover types in China are divided into five types, namely, the grass type, the Taijialin type, the moss type, the alpine type and the instantaneous type, which are different from the classification description of Sturm et al. (1995). This atlas contains a total of 1 map and 1 corresponding mapping data, which is completely open and shared.
", "ds_time_res": "", "ds_acq_place": "", "ds_space_res": "0.1°×0.1°", "ds_projection": "", "ds_process_way": " <p>This atlas applies the national surface meteorological driving grid data set with high spatial and temporal resolution from 1979 to 2014 (10km × 10km), extract the winter atmospheric temperature, precipitation and near-surface wind speed information in China through MATLAB software programming, and use the binary tree seasonal snow cover classification system proposed by Sturm et al. (1995) to divide the snow cover types in China. In the binary tree system, in order to determine the critical values of winter atmospheric temperature, precipitation and wind speed in the meteorological parameters used for classification, As well as the joint effect of winter temperature and duration, the index of winter temperature and duration - monthly cold degree (CDM) is defined as the index of temperature division, as shown in equation (1).\nWhere: m is the month of the year (1-12). Is the monthly average atmospheric temperature, is the critical temperature. When the monthly average temperature is greater than or equal to the critical temperature, the monthly cooling is 0; When the monthly average temperature is less than the critical temperature, the monthly cooling is the cumulative value of the difference between the two. The higher the monthly cooling value, the lower the atmospheric temperature in winter. The critical value is determined as the temperature that the local snow can exist, and the winter month is defined by the critical value. Based on the selected meteorological threshold, the classification map of snow cover types in China is obtained by using the SHL snow cover classification method, and then the classification results are filtered, smoothed, and small isolated areas are removed. Finally, the snow cover classification map of Sturm-Holmgren-Liston system in China is obtained, and then the thematic map of the data set is drawn by using ArcGIS software.", "ds_ref_instruction": "
In this paper, the high spatial-temporal resolution ground meteorological element driven data set of China from 1980 to 2014 is used. This data is a multi-source fusion grid data set provided by the Institute of Qinghai-Tibet Plateau, Chinese Academy of Sciences.\r\nThe verification data are the historical data set of snow cover in China (2008-2019) collected and sorted by the project and the snow cover characteristics data of this ground survey (2017-2020)." } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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, "belong_to_nieer": false, "ds_topic_tags": [ "积雪类型" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国陆域" ], "ds_time_tags": [ 1956, 1980, 1981, 1982, 1983, 1984, 1985, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" } ], "category": "积雪" }