The high-resolution atmosphere-hydrologic simulation dataset over Tibetan Plateau is prepared by WRFv4.1.1 model with grids of 191 * 355 and spatial resolution of 9 km, and a spatial range covering the entire plateau. The main physics schemes are configured with Thompson microphysics scheme, the rapid radiative transfer model (RRTM), and the Dudhia scheme for longwave and shortwave radiative flux calculations, respectively, the Mellor-Yamada-Janjic (MYJ) TKE scheme for the planetary boundary layer and the Unified Noah Land Surface Model. The time resolution is 3h and the time span is 2000-2010. Variables include: precipitation (Rain), temperature (T2) and water vapor (Q2) at 2m height on the ground, surface skin temperature (TSK), ground pressure (PSFC), zonal component (U10) and meridional component (V10) at 10m heigh on the ground, downward long-wave flux (GLW) and downward short-wave flux (SWDOWN) at surface, ground heat flux (GRDFLX), sensible heat flux (HFX), latent heat flux (LH), surface runoff (SFROFF) and underground runoff (UDROFF). The data can effectively support the study of regional climate characteristics, climate change and its impact over the Tibet Plateau, which will provide scientific basis for the sustainable development of the TP under the background of climate change.
MENG Xianhong, MA Yuanyuan
Data content: atmospheric pressure data of Nukus irrigation area from January 2021 to December 2021, unit: PA. Data source and processing method: this data is collected from the automatic groundwater monitoring station in Nukus irrigation area. Data quality description: this data is site data with a time resolution of 3 hours. Data application achievements and prospects: in the context of climate change, it can be used to analyze the correlation between meteorological elements and groundwater characteristics, and can also be combined with other hydrometeorological data to analyze the temporal and spatial distribution and change characteristics of groundwater. At the same time, it can also be used as basic data for research in related fields such as extreme climate, food production reduction and human health.
LIU Tie
Tajikistan West Pamir Glacier Meteorological Station (38°3′15″N, 72°16′52″E, 3730m), the station is the Urumqi Desert Meteorological Institute of the China Meteorological Administration and the Tajikistan National Academy of Sciences for Water Issues, Water Energy and Ecology The Institute and the Tajikistan Hydrological and Meteorological Service are jointly constructed. Observation data includes hourly meteorological elements (average wind direction (°), average wind speed (m/s), wind direction at maximum wind speed (°), maximum wind speed (m/s), average temperature (°C), maximum Air temperature (°C), minimum air temperature (°C), average relative humidity (%), minimum relative humidity (%), average atmospheric pressure (hPa), maximum atmospheric pressure (hPa), minimum atmospheric pressure (hPa)). The data period is from December 10, 2020 to October 13, 2021 Meteorological observation data can provide important basic data for studying the relationship between climate change, glaciers and water resources in the West Pamir Mountains, and provide important data for the economic construction of the lower reaches of the Amu Darya River Basin in Tajikistan.
HUO Wen
This meteorological data is the basic meteorological data of air temperature, relative humidity, wind speed, precipitation, air pressure, radiation, soil temperature and humidity observed in the observation site (86.56 ° e, 28.21 ° n, 4276m) of the comprehensive observation and research station of atmosphere and environment of Qomolangma, Chinese Academy of Sciences from 2019 to 2020. Precipitation is the daily cumulative value. All data are observed and collected in strict accordance with the instrument operation specifications, and some obvious error data are eliminated when processing and generating data The data can be used by students and scientific researchers engaged in meteorology, atmospheric environment or ecology (Note: when using, it must be indicated in the article that the data comes from Qomolangma station for atmospheric and environmental observation and research, Chinese Academy of Sciences (QOMS / CAS))
XI Zhenhua
The observation data set of field meteorological stations in Central Asia and Western Asia (2019-2020) includes the monthly meteorological data of 12 field meteorological stations in Kazakhstan (5 stations), Kyrgyzstan (1 station), Tajikistan (3 stations), Uzbekistan (1 station) and Iran (2 stations), involving 21 observation indicators: Monthly average temperature (TA), monthly average pressure (PA) Monthly average relative humidity (RH), monthly total rainfall (PR), monthly average wind speed (WS), monthly average wind direction (WD), 0cm monthly average soil temperature (TS1), 5cm monthly average soil temperature (TS2), 10cm monthly average soil temperature (Ts3), 15cm monthly average soil temperature (ts4), 20cm monthly average soil temperature (ts5), 40cm monthly average soil temperature (TS6) 60cm monthly average soil temperature (ts7), 100cm monthly average soil temperature (ts8), monthly total solar radiation (SR), monthly total reflected radiation (GR), monthly total ultraviolet radiation (UVR), monthly total net radiation (NR), monthly total photosynthetic effective radiation (PAR), monthly total soil heat flux (HF) and monthly total sunshine duration (SD). The 12 field stations cover farmland, forest, grassland, desert, desert, wetland, plateau, mountain and other different ecosystem types. The data length starts from October 2019 to December 2020. The original meteorological data collected by the ground meteorological observation station is obtained after format conversion after screening and review, and the data quality is good. Central Asia has diverse climate types, fragile ecological environment and frequent meteorological disasters. The establishment of this data set provides data support for long-term research in the fields of ecological environment monitoring, disaster prevention and reduction, climate change and ecological environment in Central Asia. At present, it has been applied in the research of ecological environment monitoring in Central Asia.
LI Yaoming LI Yaoming
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02℃) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements. The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems.
QIU Yuan QIU Yuan
Kara batkak glacier weather station in Western Tianshan Mountains of Kyrgyzstan (42 ° 9'46 ″ n, 78 ° 16'21 ″ e, 3280m). The observational data include hourly meteorological elements (hourly rainfall (mm), instantaneous wind direction (°), instantaneous wind speed (M / s), 2-minute wind direction (°), 2-minute wind speed (M / s), 10 minute wind direction (°), 10 minute wind speed (M / s), maximum wind direction (°), maximum wind speed (M / s), maximum wind speed time, maximum wind direction (°), maximum wind speed (M / s), maximum wind speed time, maximum instantaneous wind speed within minutes) Direction (°), maximum instantaneous wind speed in minutes (M / s), air pressure (HPA), maximum air pressure (HPA), time of maximum air pressure, time of minimum air pressure (HPA), time of minimum air pressure. Meteorological observation elements, after accumulation and statistics, are processed into climate data to provide important data for planning, design and research of agriculture, forestry, industry, transportation, military, hydrology, medical and health, environmental protection and other departments.
HUO Wen
This data set is the data set of climate elements in Hoh Xil area of Qinghai Province, covering the data of 14 observation stations, recording the climate observation data in 1990 in detail. Hoh Xil area in Qinghai Province has a high terrain with an average altitude of over 5000m. The climate is cold, the air is thin and the natural environment is bad. The vast area is still no man's land, known as "forbidden zone for human beings". Due to less interference from human activities, most of the area still maintains its original natural state. Its special geographical location, crustal structure and natural environment, as well as the unique composition of the biological flora, have been the focus of domestic surgical circles. The original data of the data set is digitized from the book "natural environment of Hoh Xil, Qinghai Province". The climate observation data include solar radiation, temperature, precipitation, air pressure, wind speed, etc. This data set provides basic data for the study of Hoh Xil area in Qinghai Province, and has reference value for the research in related fields.
LI Bingyuan
When using the 3DVAR for data assimilation, it is necessary to use error covariance to determine the contribution of background field and observation. Among them, the background field error covariance depends not only on the atmospheric prediction model (such as resolution, parameterization scheme, etc.), but also on the simulation area. Based on the Weather Forecast and Research (WRF) model, this data is estimated by NMC method through the simulation of the Central Asian Great Lakes region (27 km horizontal resolution) in 2017. The variables include stream function, velocity potential function, temperature, relative humidity and surface pressure. This data can be applied to the study and application of data assimilation in the Central Asia Great Lakes region based on WRF model.
YAO Yao
The observation data set of Central Asia field meteorological station includes the field observation data of temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation, soil heat flux, sunshine time and soil temperature of 10 Central Asia field meteorological stations. 10 field stations cover farmland, forest, grassland, desert, desert, wetland, plateau, mountain and other ecosystem types. The original meteorological data collected by the ground meteorological observation station is obtained after screening and review, and format conversion. Data quality is good. Central Asia has a variety of climate types, fragile ecological environment and frequent meteorological disasters. The establishment of this data set provides data support for long-term research in the fields of Central Asia ecological environment monitoring, disaster prevention and mitigation, climate change and ecological environment in Central Asia, and has been applied in the research of Central Asia ecological environment monitoring.
LI Yaoming LI Yaoming
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
YANG Kun
（1）This data set provides atmospheric temperature (2 meters above land surface), vapor content, precipitation, press, wind velocity and solar radiation (since 2015). （2）All data were generated using AWS (auto weather station), and been calculated their daily average. （3）All data are presented here are raw data, after being evaluated regarding their quality. （4）This data set could be used in background description for related studies.
Da Wei, WANG Xiaodan
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
WANG Lei
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
YANG Kun, HE Jie, WENJUN TANG , LU Hui, QIN Jun , CHEN Yingying, LI Xin
The data set contains the meteorological element observation data of the upper reaches of the heihe hydrological meteorological observation network of daxun station on January 1, 2015 and December 31, 2017.The site is located in the western side of qilian county, qinghai province.The longitude and latitude of the observation point are 98.9406°E, 38.8399°N and 3739m above sea level.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plate (3 pieces) is buried in the underground 6cm successively and is 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:C), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: wattage/m2), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: water content by volume, percentage). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
The data set contains the meteorological element observation data of jingyangling station in the upper reaches of heihe hydrometeorological observation network on January 1, 2015 and December 31, 2017.The site is located in pass, jingyangling mountain, qilian county, qinghai province.The longitude and latitude of the observation point are 101.1160E, 37.8384N and 3750m above sea level.The air temperature and relative humidity sensor is set up at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tipping bucket rain gauge is installed at 10m;The wind speed and direction sensor is mounted at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing south, with the probe facing vertically downward;The soil temperature probe is buried at the surface of 0cm and underground of 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil moisture probe is buried underground at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm, 2m south of the meteorological tower.The soil heat flow plates (3 pieces) are successively buried 6cm underground, 2m south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: percent). Processing and quality control of observation data :(1) 144 data per day (every 10min) should be ensured.(2) eliminate the moments with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letters in the data is questionable data;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2015-9-10 10:30;(6) naming rules: AWS+ site name. For information of hydrometeorological network or site, please refer to Li et al. (2013), and for data processing, please refer to Liu et al. (2011).
CHE Tao, LIU Shaomin, LI Xin, XU Ziwei, ZHANG Yang, TAN Junlei
Based on the long-term observation data of each field station in the alpine network and overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; the inversion of data products such as meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacial and frozen soil changes are completed through enhanced observation and sample site verification in key regions; based on the IOT Network technology, the development and establishment of multi station network meteorological, hydrological, ecological data management platform, to achieve real-time access to network data and remote control and sharing. The data includes the daily meteorological observation data sets (air temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) of the Qinghai Tibet Plateau in 2014-2017 from 17 stations of China Alpine network. The data of the three river sources are missing.
ZHU Liping,
This dataset is derived from the global atmospheric reanalysis dataset, ERA-Interim, based on the 4-dimensional variational analysis (4D-Var) released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim represents a major undertaking by ECMWF (European Centre for Medium-Range Weather Forecasts) to produce a reanalysis with an improved atmospheric model and assimilation system which replaces those used in ERA-40, particularly for the data-rich 1990s and 2000s, and to be continued as an ECMWF Climate Data Assimilation System (ECDAS) until superseded by a new reanalysis. Through systematic increases in computing power, 4-dimensional variational assimilation (4D-Var) became feasible and part of ECMWF operations since 1997. Enhanced computing power also allowed horizontal resolution to be increased from T159 to T255, and the latest Integrated Forecasting System(IFS CY31r1 and CY31r2) to be used, taking advantage of improved model physics. ERA-interim retains the same 60 model levels used for ERA-40 with the highest level being 0.1 hPa. Besides, data assimilation of ERA-Interim also benefits from quality control that draws on experience from ERA-40 and JRA-25, variational bias correction of satellite radiance data, and more extensive use of radiances with an improved fast radiative transfer model. In addition, ERA-Interim uses the new ERS (European Remote Sensing Satellite) altimeter wave heights, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) reprocessed winds and clear-sky radiances, GOME (Global Ozone Monitoring Experiment) ozone data from the Rutherford Appleton Laboratory, and CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation measurements processed and archived by UCAR (University Corporation for Atmospheric Research).
DENG Chuangwu
Shergyla Mountain meteorological data, Record the surface near Linzhi(1.2-1.5m) conventional meteorological observation.The dataset records the meteorological data at the eastern slope of Shergyla Mountain from 2005 to 2016, and North-facing slope from 2005 to 2012.Including daily average data of temperature, relative humidity, precipitation. Data collected near the eastern slope timberline of Shergyla Mountain, Latitude:29°39′25.2″N; Longitude:94°42′25.62″E; Altitude:4390m, and collected near the north-facing slope of Shergyla Mountain, Latitude:29°35′50.9″N; Longitude:94°36′42.7″E; Altitude:4390m. Collector: Campbell Co CR1000. Collection time interval：30min. Digital automatic data collection, daily average value of artificial calculation. It includes the following basic meteorological parameters: North-facing slope data: Wind speed，Unit m/s Temperature，Unit ℃ Relative Humidity，Unit % Atmospheric pressure，Unit hPa Global radiation，Unit w/m2 Soil heat flux，Unit w/m2 Soil temperature，Unit ℃ Soil moisture，Unit % Precipitation，Unit mm Thickness of snow, Unit cm Ecology station data: Temperature，Unit ℃ Relative Humidity，Unit % Atmospheric pressure，Unit hPa Wind speed，Unit m/s Precipitation，Unit mm Snow Depth，Unit cm Radiation，Unit w/m2 Soil moisture content，Unit % Soil heat flux，Unit w/m2
Luo Lun
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