积雪物候能够反映积雪动态变化，本研究制作2021-2050年崇礼区积雪日数、积雪初日终日以及连续积雪天数等积雪物候数据，对崇礼赛区未来三十年的积雪状况进行预测，经Landsat 8 参考值验证得到总体精度为77%。
\n本数据集可为冬奥赛事的开展提供人工降雪量的参考，同时可指导未来滑雪场运营，积雪资源管理、灾害监测和预警、滑雪场建设及极端天气应对，也有利于进一步开展崇礼区未来积雪物候与气候变化等研究。
", "ds_source": "本数据集基于GBEHM模型，结合CMIP6(Coupled Model Intercomparison Project Phase 6)未来气候预报数据制备了北京冬奥会崇礼赛区未来三十年的积雪面积数据集，并在此基础上制作了相应地区和时间序列下的积雪物候数据集。
\nGBEHM模型所需的CMIP6气候预报参量为：下行短波辐射通量、下行长波辐射通量、大气压、相对湿度、比湿、降水、气温、风速。CMIP6气候预报数据采用统计降尺度方法处理成时间分辨率为逐小时，空间分辨率为1km的驱动数据。
", "ds_process_way": "积雪面积数据的生产基于GBEHM模型，充分考虑了积雪的积累和消融过程，能够较为精准的模拟积雪过程，将CMIP6气候预报模式作为驱动数据，采用降尺度方法将各个参量处理为1 km空间分辨率，1 h时间分辨率，并经过重采样和异常值的剔除后输入模型得到2021-2050年崇礼赛区的积雪面积数据产品。
", "ds_quality": "参考值数据的选择条件为图像覆盖崇礼赛区且云量小于10%，最终筛选出满足条件的六幅影像，对应日期为2021年2月2日、2021年4月7日和23日。分别提取参考值影像与待验证影像对应赛区内气象站点的栅格值（22个气象站分布于两个赛区内，平均海拔1200 m，具有良好的代表性），最后共有66条数据可用于验证。检验方法使用1.4.1中的混淆矩阵方法，验证情况如表3所示，考虑到是未来时间序列的数据，验证资料缺乏，以及积雪识别算法本身的准确性，此检验结果仅供参考。
", "ds_acq_start_time": "2021-01-01 00:00:00", "ds_acq_end_time": "2050-12-31 00:00:00", "ds_acq_place": "北京冬奥崇礼赛区", "ds_acq_lon_east": 115.56666666666666, "ds_acq_lat_south": 40.766666666666666, "ds_acq_lon_west": 114.26666666666667, "ds_acq_lat_north": 41.266666666666666, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 36544584, "ds_files_count": 178, "ds_format": "*.nc", "ds_space_res": "1000", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "2671c3ce-f152-4e43-89a2-66f7968b95fc.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4667592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.I-SNOW.db1807.2022", "subject_codes": [], "quality_level": 3, "publish_time": "2022-03-14 14:21:15", "last_updated": "2023-05-12 15:14:37", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db1807.2022", "i18n": { "en": { "title": "A Dataset of Snow phenology for the next 30 years in Chongli Area of Winter Olympics Division", "ds_format": "", "ds_source": " <pre><code> <pre><code>\nThis data set is based on gbehm model and combined with cmip6 (coupled model intercomparison project phase 6) future climate prediction data. The snow area data set of Chongli competition area of Beijing Winter Olympic Games in the next 30 years is prepared. On this basis, the snow phenological data set under corresponding regions and time series is made.\nCmip6 climate prediction parameters required by gbehm model are: downward short wave radiation flux, downward long wave radiation flux, atmospheric pressure, relative humidity, specific humidity, precipitation, air temperature and wind speed. Cmip6 climate prediction data are processed into driving data with hourly time resolution and 1km spatial resolution by statistical downscaling method.
", "ds_quality": " <pre><code> <pre><code>\nThe selection condition of reference value data is that the image covers Chongli competition area and the cloud amount is less than 10%. Finally, six images meeting the conditions are selected, and the corresponding dates are February 2, 2021, April 7 and 23, 2021. The grid values of the meteorological stations in the competition area corresponding to the reference value image and the image to be verified are extracted respectively (22 meteorological stations are distributed in the two competition areas, with an average altitude of 1200 m, which is well representative). Finally, a total of 66 data can be used for verification. The test method uses the confusion matrix method in 1.4.1. The verification is shown in Table 3. Considering that it is the data of future time series, the lack of verification data and the accuracy of snow recognition algorithm, the test results are only for reference.
", "ds_ref_way": "", "ds_abstract": " <pre><code> <pre><code>\nSnow phenology can reflect the dynamic changes of snow. In this study, snow phenology data such as snow days, first and last days of snow and continuous snow days in Chongli district from 2021 to 2050 are prepared to predict the snow situation in Chongli District in the next 30 years. The overall accuracy is 77% verified by Landsat 8 reference value.\nThis data set can provide the reference of artificial snowfall for the development of Winter Olympic events, and guide the future ski resort operation, snow resource management, disaster monitoring and early warning, ski resort construction and extreme weather response. It is also conducive to further research on the future snow phenology and climate change in Chongli district.
", "ds_time_res": "年", "ds_acq_place": "Chongli competition area of Beijing Winter Olympics", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": " <pre><code> <pre><code>\nThe production of snow area data is based on gbehm model, which fully considers the process of snow accumulation and melting, can accurately simulate the snow process, takes cmip6 climate prediction model as the driving data, and uses the downscaling method to process each parameter into 1 km spatial resolution and 1 h time resolution, After resampling and outliers elimination, the snow area data products of Chongli competition area from 2021 to 2050 are input into the model.
", "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": [ "冬奥赛场", "积雪物候", "未来三十年", "CMIP6" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "北京", "冬奥赛场", "崇礼赛区" ], "ds_time_tags": [ 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050 ], "ds_contributors": [ { "true_name": "杨雅茹", "email": "", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "赵春雷", "email": "Z920604@163.com", "work_for": "河北省气象科学研究所", "country": "中国" }, { "true_name": "邵东航", "email": "shaodonghang@lab.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "纪文政", "email": "jiwenzheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李弘毅", "email": "lihongyi@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "杨雅茹", "email": "", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "李弘毅", "email": "lihongyi@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }