本数据集数据范围为中国地区，时间段为1986-2098年，空间分辨率为6.25×6.25公里，包括气温、降水、平均风速、最大风速、风向、风向频率、总辐射、入射太阳辐射、日照时数，数据大小合计约3GB，每个变量1个文件。\n
单位：气温（℃）、降水（毫米每天）、平均风速(米每秒)、最大风速(米每秒)、风向(度)、风向频率（无量纲）、总辐射（瓦每平方米）、入射太阳辐射（瓦每平方米）、日照时数（小时）", "ds_source": "
通过区域气候模式RegCM4.4动力降尺度的模拟能力改进、联合统计降尺度等技术研制高时空分辨率的气候变化情景数据，开展风能和太阳能资源、风电和光伏发电量变化的多模式集合预估研究。本数据基于意大利理论物理研究中心开发的RegCM4.4区域气候模式完成的动力降尺度模拟结果研制。", "ds_process_way": "
在进行长期模拟预估之前，首先对新版RegCM4.4区域气候模式的不同物理参数化方案进行大量组合试验，对比分析不同参数化方案的模拟效果，最终形成对中国当代气温和降水模拟效果最好的参数化组合CLM+Emanuel，随后基于以上组合通过改善地表发射率和引入中国实际植被覆盖数据等对区域模式进行进一步改进和优化，最终明显提升了该模式对中国地区当代气温和降水的模拟效果，尤其是东部季风区，使气温的模拟偏差由±2~3℃减少至±1℃，降水与观测的相关系数也由0.3~0.5提升至0.5~0.7。在更小的局地尺度开展气候变化预估研究时，需要用到比25公里分辨率更高的预估结果，通过统计降尺度获得更高分辨率预估结果可以有效避免计算资源的限制。", "ds_quality": "
基于6.25公里高分辨率CLDAS格点观测数据，利用多变量误差订正方法（MBCSD 方法），即经过订正季节循环及分位数映射方法（QM）的动力-统计联合降尺度，可以有效地减少模拟偏差，最终得到更高分辨率的预估结果，从而为局地尺度气候变化提供更多可靠细节。", "ds_acq_start_time": "2019-04-01 00:00:00", "ds_acq_end_time": "2023-03-01 00:00:00", "ds_acq_place": "中国区域", "ds_acq_lon_east": 135.85999999999999, "ds_acq_lat_south": 6.323333333333333, "ds_acq_lon_west": 73.51138888888889, "ds_acq_lat_north": 53.56083333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 24405158331, "ds_files_count": 2, "ds_format": "nc", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "1f0a8787-1154-40ff-9d78-f9b4c4ac9efb.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "4c743a55-1b12-4680-ba95-1f35a60de164", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-06-30 15:43:48", "last_updated": "2024-12-05 09:16:48", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.RECEEIAWFPPP.DB3916.2023", "i18n": { "en": { "title": "China Regional High Resolution Climate Change Scenario Estimation Dataset (1986-2098)", "ds_format": "", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "", "ds_time_res": "", "ds_acq_place": "China region", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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, "ds_topic_tags": [ "气候变化预估", "高分辨率", "情景", "降尺度" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国区域" ], "ds_time_tags": [ 1986, 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, 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, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098 ], "ds_contributors": [ { "true_name": "吴佳", "email": "wujia@cma.gov.cn", "work_for": "国家气候中心", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴佳", "email": "wujia@cma.gov.cn", "work_for": "国家气候中心", "country": "中国" } ], "ds_managers": [ { "true_name": "吴佳", "email": "wujia@cma.gov.cn", "work_for": "国家气候中心", "country": "中国" } ], "category": "气象" }