气候变化与人类活动息息相关，直接关系到区域的自然环境变化、经济发展和社会进步，是区域可持续发展中不可忽视的挑战。气温变化是全球气候变化中最为显著的指示因子，是分析和预测气候变化的重要基础数据。
\n\n本数据集基于CRU全球格点数据，制备了中巴经济走廊区域1901-2018年间气温变化时空数据集，共计4604个GeoTIFF文件，其中温度单位为℃，空间分辨率为0.5°×0.5°，时间分辨率为月，年以及多年(1901-2018)，本数据为了解中巴经济走廊区域气温的时空变化及趋势预测提供基础资料。
", "ds_source": "英国东安格利亚大学CRU气象格点数据集,版本号: v. 4.03
", "ds_process_way": "对原始数据进行格式转换，区域裁剪，统计分析，生成了7类气温空间数据集：
\n\n(1) 1901-2018年间的日均气温的最小值与最大值，分别生产CPEC_all_tmp_max.tif与CPEC_all_tmp_min.tif两个数据文件;
\n\n(2)年均日均气温值，命名形式为：CPEC_YYYY_tmp.tif；
\n\n(3)月均气温命名形式为：CPEC_YYYYMM_tmp.tif;
\n\n(4) 每月平均每日最低气温,文件命名为：CPEC_YYYYMM_tmn.tif;
\n\n(5) 年均每月平均每日最低气温,文件命名为：CPEC_YYYY_tmn.tif;
\n\n(6) 每月平均每日最高气温,文件命名为：CPEC_YYYYMM_tmx.tif;
\n\n(7) 年均每月平均每日最高气温,文件命名为：CPEC_YYYY_tmx.tif
\n\n其中YYYY 四位数标识年份 MM 两位数标识月份。
", "ds_quality": "本数据集的时空精度与原始CRU数据保持一致，质量良好，适宜于气候变化研究及环境变化分析。
", "ds_acq_start_time": "1901-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中巴经济走廊区域", "ds_acq_lon_east": 79.86666666666666, "ds_acq_lat_south": 23.75, "ds_acq_lon_west": 60.86638888888889, "ds_acq_lat_north": 41.416666666666664, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 10600397, "ds_files_count": 2, "ds_format": "Geotiff", "ds_space_res": "1000.0m", "ds_time_res": "月，年，多年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": null, "ds_thumb_from": 2, "ds_ref_way": "赵国辉，中巴经济走廊气温时空数据集（1901-2018年），国家特殊环境、特殊功能 观测研究台站共享平台，2018，doi：10.12072/NCDC.004.2020.db", "paper_ref_way": "", "ds_ref_instruction": null, "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "myf@lzb.ac.cn", "doi_value": "", "subject_codes": null, "quality_level": 3, "publish_time": "2020-12-21 13:39:19", "last_updated": "2023-05-16 17:35:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.2020.1322", "i18n": { "en": { "title": "Spatio-temporal dataset of temperature in the China-Pakistan Economic Corridor from 1901 to 2018", "ds_format": "", "ds_source": "Cru weather grid data set, University of East Anglia, UK, version number: v. 4.03
", "ds_quality": "The spatial and temporal accuracy of this data set is consistent with the original CRU data, and the quality is good. It is suitable for climate change research and environmental change analysis.
", "ds_ref_way": "\nZhao Guohui, China-Pakistan Economic Corridor temperature spatiotemporal data set (1901-2018), national special environment, special function observation and research station sharing platform, 2018, DOI: 10.12072/ncdc.004.2020.db", "ds_abstract": "Climate change is closely related to human activities, directly related to regional natural environment change, economic development and social progress, and is a challenge that can not be ignored in regional sustainable development. Temperature change is the most significant indicator of global climate change, and it is an important basic data to analyze and predict climate change.
\nBased on the CRU global grid data, this data set has prepared 4604 GeoTIFF files of temperature change in China Pakistan Economic Corridor region from 1901 to 2018, in which the temperature unit is ℃, the spatial resolution is 0.5 ° x 0.5 ° and the temporal resolution is month, year and years (1901-2018). This data provides a reference for understanding the spatial-temporal change and trend prediction of temperature in the China Pakistan Economic Corridor Basic data.
", "ds_time_res": "月，年，多年", "ds_acq_place": "China-Pakistan Economic Corridor Region", "ds_space_res": "1000.0m", "ds_projection": "", "ds_process_way": "Seven types of spatial data sets of air temperature were generated by format conversion, regional clipping and statistical analysis
\n(1) The minimum and maximum daily average temperature from 1901 to 2018 are used to produce CPEC respectively_ all_ tmp_ max.tif And CPEC_ all_ tmp_ min.tif Two data files
\n(2) The annual average daily temperature is named as CPEC_ YYYY_ tmp.tif ；
\n(3) The naming form of monthly average temperature is CPEC_ YYYYMM_ tmp.tif ;
\n(4) The monthly average daily minimum temperature was named CPEC_ YYYYMM_ tmn.tif ;
\n(5) The annual average monthly minimum temperature is named CPEC_ YYYY_ tmn.tif ;
\n(6) The monthly average daily maximum temperature was named CPEC_ YYYYMM_ tmx.tif ;
\n(7) The annual average daily maximum temperature is named CPEC_ YYYY_ tmx.tif
\nAmong them, yyyy four digit mark year, mm two digit mark month.
", "ds_ref_instruction": "\nNone" } }, "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": [ "CRU气象格点数据", "气温" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中巴经济走廊区域" ], "ds_time_tags": [ 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 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 ], "ds_contributors": [ { "true_name": "赵国辉", "email": "zhgh@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": "赵国辉", "email": "zhgh@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_managers": [ { "true_name": "赵国辉", "email": "zhgh@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": "气象" }