China-Pakistan Economic Corridor Zone II Versions EN1 Vol 4 (3) 2019
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A dataset for the extreme temperature-rising process over eastern Pamirs Plateau in northern China-Pakistan economic corridor (1961 – 2017)
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Abstract & Keywords
Abstract: The China-Pakistan Economic Corridor passes through the Pamirs Plateau in the north, a place characteristic of complex terrain, fragile ecological environment as well as recurrent and various disasters. One of the major hydrologic meteorological disasters is glacier melt runoff caused by extreme temperature-rising, which draws the attention from the social and scientific field. Based on the daily maximum temperature data observed by 3 meteorological stations on the eastern Pamirs Plateau, we defined and calculated the indices of the temperature-rising process over the eastern Pamirs Plateau from 1961 to 2017, including the temperature-rising dates, process (starting and ending dates, duration), ranges during different periods of each station. Based on the indices of extreme events, we also analyzed the change characteristics of the process to build the dataset for the extreme temperature-rising process. This dataset could be used for research on the occurrence frequency and development trend of extreme temperature-rising events on Pamirs Plateau. It also provides data support for early warning of extreme climates and flood disasters to decrease the risk of ice-snow melt flood over eastern Pamirs Plateau in northern China-Pakistan Economic Corridor in the background of global warming.
Keywords: China-Pakistan Economic Corridor; Pamirs; temperature-rising process; meteorological stations
Dataset Profile
TitleA dataset for the extreme temperature-rising process over eastern Pamirs Plateau in northern China-Pakistan economic corridor (1961 – 2017)
Data authorsMao Weiyi, Yao Junqiang, Chen Jing
Data corresponding authorMao Weiyi: (mao6991@vip.sina.com)
Time range1961–2017
Geographic scopeChina section of the eastern Pamir Plateau mountainous area at the northern end of the China-Pakistan Economic Corridor, including 3 meteorological stations
Time resolutionDay
Data volume1.22 MB
Data formatCSV
Data service system URL<http://www.sciencedb.cn/dataSet/handle/595>
Sources of fundingNational Natural Science Foundation Project (U1503181, 41605067, 41375101)
Dataset componentsThis data set consists of 12 files (CSV), including 2 data files temperature-increasing process for each station, one for all temperature-increasing process data files of the station, and the other for the data file of extreme temperature-increasing process. In addition, there are 6 files including the daily maximum temperature and daily average temperature of Ulugqat Station and their corresponding anomalies and multi-year averages. The data file of the temperature-rising process covers the results of all the elements related to the temperature-rising process from January 1, 2017 to December 31, 2017. The data file naming rule is as follows: Temperature-rising+meteorological station number + 1961 – 2017. CSV. Each temperature-rising process consists of 13 elements: The starting date of the temperature-rising process (day, month, year), the ending date (day, month, year), the duration of the process, the temperature-rising range, the maximum temperature-rising range within 24 hours, 48 hours and 72 hours during the process (if the process is less than 2 days, the temperature-rising range within 48 hours and 72 hours will be vacant); if less than 3 d, then 72 h warming vacant), the process extreme maximum temperature and the maximum daily temperature anomaly range. The data file naming rule of the temperature-rising process is as follows: temperature_rising_process_XXXXX_1961_2017.CSV (XXXXX indicates the WMO unified meteorological station number.). The data volume of each file varies depending on the number of temperature-rising processes at the station, and the volume of uncompressed data is approximately 253-282 KB. On this basis, we also sorted out the data file of the extreme temperature-rising process of the station. And the data file naming rule of the process is as follows: extreme_temperature_rising_process_PER05_XXXXX_1961_2017.CSV (XXXXX indicates the WMO unified meteorological station number.). The elements of each temperature-rising process in the files are the same as those in the previous file, with one more comprehensive strength index added. The data volume of each file varies depending on the number of temperature-rising processes at the station, and the volume of uncompressed data is approximately 15-17 KB. The file of Ulugqat’s daily average temperature is named as temperature_day_51705-1961_2017.CSV; The file of the daily maximum temperature is named as temperature_day_max_51705-1961_2017.CSV; the volume of uncompressed data is approximately 100-102 KB. The file of daily average temperature anomaly is named as temperature_day_ano_51705-1961_2017.CSV; the file of the daily maximum temperature anomaly is named as emperature_day_max_ano_51705-1961_2017. CSV; the volume of uncompressed data is approximately 95 KB. The file of multi-year daily average temperature is named as temperature_day_ave_51705-1961_2017.CSV; The file of multi-year daily maximum temperature is named as temperature_day_max_ave_51705-1961_2017.CSV; the volume of uncompressed data is approximately 2 KB.
1.   Introduction
The IPCC Fifth Assessment Report focused on the assessment of extreme events and the corresponding risk response. Under the background of global warming, changes in the laws of extreme weather and climate events have led to frequent occurrence of regional meteorological disasters and increased disaster risks[1]. Over the past 5 decades, all regions in Xinjiang have witnessed a significant increase in extreme warm events and a significant decrease in extreme cold events[2-3]. In Xinjiang, a sharp rise in temperature (strong temperature-rising process) which lasts for 3 days or more, could directly cause serious disasters. The sharp rise in summer temperatures has led to increased glacial melting in river source areas, and may even lead to glacial melting floods, the floods of mixed ice and precipitation, and ice dam burst-type floods in special watersheds[4-5]. The Tarim River Basin is China’s largest inland river, originating from Kunlun Mountains, Pamirs and Tianshan Mountains. High-temperature snowmelt (ice) floods frequently occur in these source streams. With the aggravation of global warming, snowmelt (ice) floods in the Tarim River Basin has increased and intensified in the past decades[6-7]. In the summer of 2015, a large-scale high-temperature process occurred in Xinjiang, which triggered floods in the source stream areas of the Tarim River. The Kunlun Mountains–the Pamir Plateau area was also affected by hazardous events[8]. The extreme warm events that occur in winter also have a significant impact on Xinjiang’s ice and snow resources, and may even cause floods. In January 2010, an extreme temperature process occurred in the northern part of Xinjiang, which caused the snow in the Ta’e Basin of northern Xinjiang to rapidly ablate during the winter season, leading to a winter snowmelt flood[9].
Recent years have seen abundant research results of extreme events such as regional extreme temperature and high temperature heat waves[10-11], but there are rare results of studying extreme characteristics from the perspective of temperature-changing processes (whether it is the temperature-rising process or the reducing process). To further strengthen the hazards and risk response brought by regional temperature changes, it is necessary to carry out research on the extreme temperature-rising process in the China-Pakistan Economic Corridor. It will provide support for further research on extreme temperature change events and serve specific initiatives to actively respond to the new challenges brought about by changes in extreme weather and climate events along the China-Pakistan Economic Corridor in the background of global warming.
This dataset is the data of the temperature-rising process and the extreme temperature-rising process of the station in China on the Pamir Plateau in the northern China-Pakistan Economic Corridor. This dataset used the maximum daily temperature to build the data of the temperature-rising process at the station. Based on the analysis of the single-element strength index and the strength ranking during the temperature-rising process, it defined a comprehensive strength index of the temperature-rising process. According to the percentile ranking method of the intensity index, it selected the processes whose intensity ranking in the top 5% to collect the data of extreme temperature-rising processes. We also sorted out the temperature-rising process at the station of the Pamir Plateau in the northern part of the China-Pakistan Economic Corridor, which could provide reliable data support for further analysis of the long-term extreme temperature-rising process in the Corridor.
2.   Data collection and processing
2.1   Station distribution
The original observational data from meteorological data used in this paper are the climate business database from the Xinjiang Climate Center. The meteorological stations are mainly distributed in the Pamir Plateau area at the northern end of the China-Pakistan Economic Corridor. The elevation range of the meteorological stations is 2177–3507 meters. The stations spread over the Xinjiang Uygur Autonomous Region of the People’s Republic of China, mainly including three meteorological stations in Tashkurgan, Torghut and Ulugqat. The information on the meridian, latitude and altitude of each station is shown in Table 1.
Table 1   Geographical parameters of the China Regional Meteorological Station on the East Pamir Plateau at the northern end of the China-Pakistan Economic Corridor
Station nameWeather station number (WMO unified)Latitude (N)Longitude (E)Altitude (m)Year
Tashkurgan5180437°47′75°14′3093. 71961–2017
Torghut5170140°31′75°24′3507. 21961–2017
Ulugqat5170539°43′75°15′2177. 51961–2017
2.2   The process of data preparation
2.2.1 Definition of basic elements of the temperature-rising process
We determined the indices of the temperature-rising process indices[12-13]: the single-station temperature-rising day, the single-station temperature-rising process (the starting day, the ending day and the duration), the maximum temperature-rising range within 24 hours, 48 hours and 72 hours during the process, as well as the process extreme maximum temperature and the maximum daily temperature anomaly range. The definition of indices is shown in Table 2.
Table 2   Indices of temperature rise process and their meanings
ConceptDefinition
Temperature-rising dayThe maximum temperature of the station is higher than that of the previous day, that is, the daily maximum temperature difference between the day and the previous day ΔT24 >0℃, defined as a temperature-rising day, referred to as the temperature-rising day.
Temperature-rising process, the process starting day (GCCR) and the ending day (GCZR)The first day when the temperature change ΔT24 of the station changes from ≤0℃ to>0℃ is defined as the first day of the temperature-rising process, and the day before the ΔT24 ≤0℃ occurs again is defined as the ending day of the process; the duration from the starting day to the ending day is referred to as one temperature-rising process.
Duration (CXRS)The days between the starting day and the ending day of the temperature-rising process is defined as the duration (including the starting and ending days).
Temperature-rising range (FD)The daily maximum temperature difference between the ending day and the day before the starting day of the process is defined as the temperature-rising range.
The maximum 24 h temperature-rising range (FD24)The maximum rise in all ΔT24 in the process is called the maximum 24 h temperature rise of the process.
The maximum 48 h temperature-rising range (FD48)The maximum rise in all ΔT48 in the process is called the maximum 28 h temperature rise of the process; If the temperature-rising process lasts less than 2 days, the maximum 48 h temperature rise will not be counted.
The maximum 72 h temperature-rising range (FD72)The maximum rise in all ΔT72 in the process is called the maximum 72 h temperature rise of the process; If the temperature-rising process lasts less than 3 days, the maximum 72 h temperature rise will not be counted.
The Maximum Temperature (TG)The daily maximum temperature of the ending day
The daily maximum temperature anomaly (JP)The maximum difference between the daily maximum temperature and the multi-year average of the same day
2.2.2   The calculation of the time-rising process
1. Basic data preparation: (1) Using the daily temperature data of the quality control station, calculate the temperature change at 24 h, 48 h and 72 h, respectively; (2) Calculate the average daily temperature of 57 years from 1961 to 2017, and get the moving average out of the average daily temperature for 5 days. (3) Calculate the daily temperature anomaly value based on the daily maximum temperature data and the multi-year average data.
Temperature-changing data based on the maximum daily temperature. When calculating the daily 24 h temperature change data, the daily maximum temperature of the day is subtracted from the maximum temperature of the previous day, and the difference obtained is recorded as the 24 h temperature change data of the day; the 24 h temperature change data of January 1, 1961 adopted the daily maximum temperature data of December 31, 1960. Similarly, calculate the difference between the daily maximum temperature of the day and that of the previous second day and that of the third day respectively. The temperature change data of 48 h and 72 h on January 1, 1961 used the daily maximum temperature data on December 29, 30, and 31, 1960. We finally get the daily 4 h, 48 h and 72 h temperature change data from January 1, 1961 to December 31, 2017.
The data of the multi-year daily maximum temperature. Take the average value of 57 years as the data of the multi-year daily maximum temperature for each station from January 1, 1961 to December 31, 2017, and calculate the average value from January 1 to December 31 of the 57 years (1961–2017). For the February 29 of the leap year, take the average of the 14-year sample data for 57 years. Then, calculate the daily average value from January 1 to December 31 again with the 5-day moving average of the two days before and after, and finally get the multi-year average data of 57 years from January 1 to December 31.
The data of daily maximum temperature anomaly. Using the daily maximum temperature data from January 1, 1961 to December 31, 2017, subtract the multi-year average of the day, and get the data of the daily maximum temperature anomaly from January 1, 1961 to December 31, 2017.
2. Identification of the temperature-rising process: (1) Based on the definition of the temperature-rising process shown in Table 2, use the daily 24 h temperature change data to identify the starting and ending days of the temperature-rising process; (2) combine the daily maximum temperature and its anomaly, as well as the daily 24 h, 48 h and 72 h temperature change values to determine the maximum temperature rises of the process during the temperature-rising process of 24 h, 48 h and 72 h, and obtain the maximum value of the process extreme maximum temperature and process daily maximum temperature anomaly range. Then we get the values of the various elements of the successive temperature-rising processes to form the dataset file of the temperature-rising process for each station.
Various elements of one temperature-rising process are composed of the specific date of the start and end of the temperature-rising process described above, and the number of process durations, the identified process temperature rise, the maximum 24 h, 48 h, 72 h temperature rise, as well as the maximum anomaly range and extreme maximum in the process. We sorted out the above-mentioned elements of all temperature-rising processes from January 1, 1961 to December 31, 2017 collected by the stations to form the temperature-increasing process dataset.
2.2.3 The calculation of the extreme temperature-rising process
The calculation of single-element strength. Based on the dataset of the temperature-rising process of the station, we analyzed the normalized values of the following four elements: the process temperature-rising range, the maximum 24 h temperature rise, the process extreme maximum temperature and the process daily maximum temperature anomaly range. The four single-element indices could quantitatively characterize the temperature rise process from different angles.
Comprehensive strength index calculation based on the four elements. Among the four single-element strength indices of each process, the index of the process temperature-rising range (IZFD) reflects the overall strength of the temperature-rising process; the index of the process maximum 24 h temperature-rising range (IZFD24) reflects the short-term temperature-rising strength during the process; the index of the process extreme maximum temperature (IZTG) reflects the absolute maximum temperature strength during the temperature-rising process; the index of the process daily maximum temperature anomaly range (IZJP) reflects the deviation of the daily maximum temperature from the average of the same period in the temperature-rising process over the years. The sum of the equal weights of the above four single-element strength feature normalization indices is used as the comprehensive evaluation index IZ4 of the single-station temperature-rising process, and the calculation formula is:
IZ4=IZFD+IZFD 24+IZTG+IZJP (1)
According to the comprehensive index IZ4 from high to low, the first 5% of the temperature-rising processes would be selected to form the dataset of the extreme temperature-rising process.
3.   Sample description
This dataset eventually contains the temperature-rising process data and the extreme temperature-rising process data of the three stations in China over eastern Pamirs Plateau in northern China-Pakistan Economic Corridor, saved as a CSV file. Each temperature-rising process consists of 13 elements: the starting day (day, month, year), the ending day (day, month, year) and the duration of the temperature-rising process, the process temperature-rising range, the maximum temperature-rising range within 24 hours, 48 hours and 72 hours, as well as the process extreme maximum temperature and the maximum daily temperature anomaly range. The data file of the extreme temperature-rising process also includes the extreme temperature-rising process data by the percentile ranking method based on the four single elements, namely the process temperature-rising range, the maximum 24 h temperature rise, the process extreme maximum temperature and the process daily maximum temperature anomaly range. The four single-element indices could quantitatively characterize the temperature rise process from different angles. In addition to the 13 elements above, each process also includes one more indicator value of comprehensive strength. The first line of the file shows the field names corresponding to each column of data. There is a total of 14 field names from left to right. The meanings indicated by these field names are shown in Table 3. The data files are named after the English name. For example, the data file of temperature-rising process in Tashkurgan Station (station number: 51804) is named temperature_ rising_ process_ 51804_ 1961_ 2017.CSV, and the data file of the extreme temperature-rising process is named extreme_ temperature_ rising_process_PER05_51804_1961_2017.CSV. In addition, there are 6 files including the daily maximum temperature, the daily average temperature and the corresponding anomalies and multi-year averages in Ulugqat Station. The data file of the daily maximum temperature is named as: temperature_ day_ max_ 51705_ 1961_2017.CSV; the data file of the daily maximum temperature anomaly is named as: temperature_day_max_ano_51705_1961_2017.CSV; and the data file of the multi-year average of the daily maximum temperature is named as: temperature_day_max_ave_51705_1961_2017.CSV. The data file of daily average temperature is named as: temperature_day_51705_1961_2017.CSV; the data file of the daily average temperature anomaly is named as: temperature_day_ano_51705_1961_2017.CSV; and the data file of the multi-year average of the daily average temperature is named as: temperature_day_ave_51705_1961_2017.CSV.
Table 3   Description of 14 field names in the data file of the extreme temperature-rising process
Field nameDescriptionField nameDescription
INDEXComprehensive strength indexFUDUProcess temperature-rising range
YEAR1Temperature-rising process starting date - yearTTGGThe process maximum temperature
MON1Temperature-rising process starting date - monthTTJPThe process maximum temperature anomaly
DAY1Temperature-rising process starting date - dayFD24The maximum 24 h temperature-rising range
YEAR2Temperature-rising process starting date - yearFD48The maximum 48 h temperature-rising range
MON2Temperature-rising process starting date - monthFD72The maximum 72 h temperature-rising range
DAY2Temperature-rising process starting date - day
DAYSThe duration of temperature-rising process
In the data file of Ulugqat’s 1961–2017 daily maximum temperature and daily average temperature, each row has 32 data. The first row indicates the station number and the date sequence from the 1st to the 31st; starting from the 2nd row, every 12 rows represent a full year, one row per month. The field in the first column identifies the year and month (e.g. 196101, indicating January 1961), and then gives a value from the 1st to the 31st day in one month. The data for the whole months (31 days) in January, March, May, July, August, October and December are complete. The corresponding assignments on the 31st of April, June, September and November are 9999, and the corresponding positions of February 29, 30 and 31 of the non-leap year as well as February 30, 31 of the leap year are assigned to 9999. The data formats of the daily maximum temperature anomaly and daily average temperature anomaly are the same as above. The file formats of the daily maximum temperature multi-year average and the daily average temperature are the same as the leap year format in the above document. The first line indicates the station number and the 1–31 date sequence, and the next 12 lines indicate every day from January to December for many years. The format of each line is “0000” + month code. The month code is the number of the current month represented by 2 digits, and the month number of less than 2 digits is complemented by “0” on the tenth place (for example, January is indicated as “000001”, and October is indicated “000010”).
4.   Quality control and assessment
4.1   Preliminary quality control of data
Data quality control is a necessary step to calculate the temperature-rising process of stations. The abnormal values and errors of the original observation data from the station will lead to an error in the calculation of the temperature-rising process and affect the subsequent application and analysis of the dataset. The meteorological stations in the Pamirs are sparse. The record of long-sequence observation is only available in three meteorological stations in China (51804, 51701, 51705), which is beyond the ability of the other countries in the Pamirs. The Pamirs and the Qinghai-Tibet Plateau are getting more and more attention. Zhou Yuke et al.[14] used the daily data of the Tashkurgan (station No. 51804) Station, and focused on the quality analysis of the daily temperature and precipitation data of the station while preparing the dataset of climate extreme indices over the Tibetan Plateau (1960 – 2012). The daily temperature data of the three long-sequence meteorological stations we selected were from the Xinjiang Climate Center and passed the preliminary quality control of the climate. First, the observation data of the daily temperature from January 1, 1961 to December 31, 2017 are complete in the three stations; second, the logical value was judged abnormally, and a day-by-day comparison was made among the daily maximum temperature of each station (Tmax ), the daily average temperature (Tave ) and the daily minimum temperature (Tmin ). The logical relationship between the three daily temperature data is Tmax , Tave and Tmin in descending order. Therefore, the quality of the initial observation data of the China’s meteorological stations in the region is reliable.
4.2 The product evaluation of the extreme temperature-rising process
With the comprehensive strength index (IZ4) as the index, the 10 strongest temperature-rising processes at the 3 stations of Tashkurgan, Torghut and Ulugqat in 1961–2017 mostly occurred in winters and springs (Table 4–6). The strongest temperature-rising process at Tashkurgan station occurred on February 20-21, 2008. The strongest temperature-rising process at Ulugqat station occurred on April 18–20, 1976. The strongest temperature-rising process at the Torghut station occurred in March 9-16, 1971
Table 4   Top 10 temperature-rising processes with the strongest comprehensive strength at the Tashkurgan station in 1961–2017
Comprehensive indexStarting dateEnding dateDuration /dProcess strength elements /℃
YearMonthDayYearMonthDayRising rangeMaximummaximum anomaly24 h rising range48 h rising range72 h rising range
13.59320082202008221215.38.27.715.215.30.0
12.50820061272006128218.63.16.612.918.60.0
10.7912016122720161229314.88.912.58.713.114.8
10.5082008122320081224216.33.37.010.816.30.0
10.0731969281969210317.72.74.410.616.217.7
9.97019951261995126112.52.86.312.50.00.0
9.883200436200438312.017.612.97.311.312.0
9.26719922251992227318.03.61.810.213.718.0
9.0201974227197432416.57.54.38.910.615.6
8.90220053122005313210.218.812.46.810.20.0
Table 5   Top 10 temperature-rising processes with the strongest comprehensive strength at the Ulugqat station in 1961–2017
Comprehensive indexStarting dateEnding dateDuration /dProcess strength elements /℃
YearMonthDayYearMonthDayRising rangeMaximummaximum anomaly24 h rising range48 h rising range72 h rising range
17.05519764181976420324.024.06.322.322.924.0
12.74819661281966128116.013.314.116.00.00.0
10.283196945196947317.622.49.210.714.217.6
9.85519661211966123317.711.57.712.916.317.7
9.788200434200438517.120.614.17.58.49.9
9.3611988111119881114415.519.211.29.613.114.2
9.32619774271977430416.821.52.813.113.513.8
9.319198822198824315.118.117.96.111.915.1
9.2851993122319931225314.510.69.812.714.214.5
9.28119964131996416420.024.57.57.613.618.2
Table 6   Top 10 temperature-rising processes with the strongest comprehensive strength at the Torghut station in 1961–2017
Comprehensive indexStarting dateEnding dateDuration /dProcess strength elements /℃
YearMonthDayYearMonthDayRising rangeMaximummaximum anomaly24 h rising range48 h rising range72 h rising range
11.4361971391971316820.49.211.37.510.611.7
10.57820062172006218215.20.27.313.515.20.0
9.90019871221987125419.60.48.98.516.319.2
9.82619743171974320415.710.212.26.710.112.3
9.405196945196946211.410.910.49.511.40.0
9.06120093112009312213.35.78.99.713.30.0
8.96819871241987128515.72.58.19.211.111.8
8.69819802101980213415.73.310.27.39.711.9
8.6951982102819821030316.91.50.612.514.716.9
8.5931969112919691129112.90.04.512.90.00.0
5.   Value and significance
The frequency and development trend of extreme climate events have a direct effect on the analysis and evaluation of climate change. This dataset can be used in combination with conventional meteorological observation data to explore the change trend and spatial feature of the extreme temperature-rising climate change in China’s section of the China-Pakistan Economic Corridor. Correlated with local statistics of snow-and-ice-melting disaster statistics, this dataset can also be analyzed to assess the possible influence of extreme weather events on glaciers and meltwater.
This dataset shares the 14 elements in the temperature-rising process of the three stations in China over eastern Pamirs Plateau in northern China-Pakistan Economic Corridor from 1961 to 2017, saved as a CSV file for the convenience of subsequent processing and application. Users can selectively download data based on the actual needs.
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Data citation
Mao WY, Yao JQ, Chen J. A dataset for the extreme temperature-rising process over eastern Pamirs Plateau in northern China-Pakistan economic corridor (1961 – 2017). Science Data Bank, 2018. (May 15, 2018). DOI: 10.11922/sciencedb.595.
Article and author information
How to cite this article
Mao WY, Yao JQ, Chen J. A dataset for the extreme temperature-rising process over eastern Pamirs Plateau in northern China-Pakistan economic corridor (1961 – 2017). China Scientific Data 4 (2019). (April 29, 2019). DOI: 10.11922/csdata.2018.0019.zh.
Mao Weiyi
Main responsibilities: data product design and key technology implementation, paper writing.
mao6991@vip.sina.com
researcher, research direction: short-term climate prediction.
Yao Junqiang
Main responsibilities: basic data collection and paper writing.
doctor, associate researcher, research direction: climate change and water cycle.
Chen Jing
Main responsibilities: basic data collection and paper writing.
master, research assistant, research direction: extreme weather and climate events and changes.
National Natural Science Foundation Project (U1503181, 41605067, 41375101)
Publication records
Published: Aug. 26, 2019 ( VersionsEN1
Released: July 5, 2018 ( VersionsZH2
Published: Aug. 26, 2019 ( VersionsZH4
References
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