(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
WEI Da
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 Linze Station from January 1 to December 31, 2019. The site (100.060° E, 39.237° N) was located on a cropland (maize surface) in the Guzhai Xinghua, which is near Zhangye city, Gansu Province. The elevation is 1400 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation; -0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4m), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_3 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 long wave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5cm, Gs_10cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential(SWP_5cm, SWP_10cm), soil conductivity (Ec_5cm,Ec_10cm) (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 precipitation and 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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi
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 Liancheng Station from January 1 to November 2, 2019. The site (102.737E, 36.692N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2912 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4 m, towards south),infrared temperature sensors (2 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation;-0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m and Ta_8 m; RH_4 m and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and 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_5 cm, Gs_10 cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential (SWP_5cm,SWP_10cm)(kpa), soil conductivity (EC_5cm,EC_10cm)(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. (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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi
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 Guazhou Station from January 1 to December 31, 2019. The site (95.673E, 41.405N) was located on a desert in the Liuyuan Guazhou, which is near Jiuquan city, Gansu Province. The elevation is 2016 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, 8, 16, 32, and 48 m, towards north), wind speed and direction profile (windsonic; 2, 4, 8, 16, 32, and 48 m, towards north), air pressure (1.5 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m, -0.6m and -0.8m in south of tower), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, Ta_8 m, Ta_16 m, Ta_32 m, and Ta_48 m; RH_2 m, RH_4 m, RH_8 m, RH_16 m, RH_32 m, and RH_48 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, Ws_8 m, Ws_16 m, Ws_32 m, and Ws_48 m) (m/s), wind direction (WD_2 m, WD_4 m, WD_8 m, WD_16 m, WD_32 m, and WD_48 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_5 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_80 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_80 cm) (%, volumetric water content),soil water potential (SWP_5cm, SWP_10cm, SWP_20cm, SWP_40cm, SWP_60cm, and SWP_80cm)(kpa), soil conductivity (Ec_5cm, Ec_10cm, Ec_20cm, Ec_40cm, Ec_60cm, and Ec_80cm)(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 during August 3 to 24 were missing because the power supply failure; From April 4, 2019, 2m air temperature and humidity sensor failure; from May.10, 2019, 48m wind speed and direction sensor failure; from July, 2019, 10cm soil moisture sensor failure. (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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi
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, 2019. 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 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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi
This dataset includes data recorded by Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dayekou Station from January 1 to December 31, 2019. The site (100.285° E, 38.555° N) was located on a glassland in the Dayekou, which is near Zhangye city, Gansu Province. The elevation is 2694 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (8 m), air pressure (2 m), rain gauge (2 m), infrared temperature sensors (2 m, towards south, vertically downward), soil heat flux (below the vegetation, -0.05 m; towards south), soil soil temperature/moisture/electrical conductivity profile (-0.05 m) photosynthetically active radiation (2 m, towards south), four-component radiometer (2 m, towards south), sunshine duration sensor(2 m, towards south). The observations included the following: air temperature and humidity (Ta_8m; RH_3m, RH_5 m, RH_8m) (℃ and %, respectively), wind speed (Ws_8m) (m/s), wind direction (WD_8m) (°), 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 (℃), photosynthetically active radiation (PAR) (μmol/ (s m^2)), soil heat flux (Gs_5 cm) (W/m^2), soil temperature (Ts_5cm)(℃), soil moisture (Ms_5cm)(%, volumetric water content), photosynthetically active radiation (μmol/ (s m^2)), soil water potential (Swp_5cm)(kpa), soil conductivity (Ec_5cm)(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. (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: 2019-6-10 10:30.
ZHAO Changming ZHANG Renyi
1) The data set driven by the surface meteorological elements of the surface meteorological observation data product (2017-2018) of the Qinghai Tibet Plateau includes four elements: near surface temperature, surface precipitation rate, short wave radiation and long wave radiation. 2) The data set is based on the existing Princeton reanalysis data, GLDAS data, gewex-srb radiation data and TRMM Precipitation Data in the world as the background field, and integrates the conventional meteorological observation data of China Meteorological Administration, and is formed by spatial interpolation. 3) The data is TIFF format, the temporal resolution is daily value, and the spatial resolution is 0.1 °. 4) It is convenient for researchers and students who do not use such assimilation data in NC format. 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.
ZHU Liping PENG Ping
This dataset includes the monthly precipitation data with 0.0083333 arc degree (~1km) for China from Jan 1901 to Dec 2017. The data form belongs to NETCDF, namely .nc file. The unit of the data is 0.1 mm. The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on Delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. The dataset covers the main land area of China, including Hong Kong, Macao and Taiwan regions, and excluding islands and reefs in South China Sea.
PENG Shouzhang
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The percentage of precipitation anomaly is the percentage of the precipitation between a certain period of time and the average climate precipitation of the same period divided by the average climate precipitation of the same period.Based on the daily rainfall data of GPM IMERG Final Run(GPM), this data set calculates the precipitation of the corresponding region, adopts the evaluation index of precipitation anomaly percentage grade, and analyzes the distribution characteristics of drought of different grades. The data area is 34 key nodes of the pan-third pole (Abbas, Astana, Colombo, Gwadar, Mamba, Tehran, Vientiane, etc.).
WU Hua
1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station（ Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.
ZHU Liping PENG Ping
This data set includes precipitation data from a total of nine ground-based precipitation observation stations located in the Yadong River Valley in the middle of the Himalayas. The observation data was collected by the Hobo tumbler rain gauge developed by Onset company and exported through supporting data reading software. Accumulated counts, the rain gauge tipped once, indicating that 0.2 mm of precipitation was recorded, and the default value of -999 was used when no precipitation event occurred. We screened the collected data and eliminated abnormal values to ensure its quality. This data set has made some progress in the analysis of precipitation characteristics, satellite data verification and model simulation evaluation in this area and two academic papers have been published, which provides strong support for the analysis of precipitation characteristics in the high-altitude valleys of the Himalayas lacking ground observation data.
YANG Kun
This data set is output from WRF model. The data include ‘LU_INDEX’ (land use category), ‘ZNU’(eta values on half (mass) levels), ‘ZNW’(eta values on full (w) levels)，’ZS’(depths of centers of soil layers), ‘DZS’ (thicknesses of soil layers), ‘VAR_SSO’ (variance of subgrid-scale orography), ‘U’(x-wind component), ‘V’(y-wind component),’W’(z-wind component),’T’(perturbation potential temperature (theta-t0)), ‘Q2’ ('QV at 2 M), ‘T2’ (TEMP at 2 M), ‘TH2’ ('POT TEMP at 2 M), ‘PSFC’ (SFC pressure), ‘U10’ (U at 10 M), ‘V10’ (V at 10 M), ‘QVAPOR’ (Water vapor mixing ratio), ‘QLOUD’ (Cloud water mixing ratio),’QRAIN’ (Rain water mixing ratio), ‘QICE’ (Ice mixing ratio), ‘QSNOW’ (Snow mixing ratio), ‘SHDMAX’ (annual max veg fraction), ‘SHDMIN’ (annual min veg fraction), ‘SNOALB’ (annual max snow albedo in fraction), ‘TSLB’ (soil temperature), ‘SMOIS’ (soil moisture), ‘GRDFLX’ (ground heat flux), ‘LAI’ (Leaf area index),’ HGT’ (Terrain Height), ‘TSK’ (surface skin temperature), ‘SWDOWN’ (downward short wave flux at ground surface), ‘GLW’ (downward long wave flux at ground surface), ‘HFX’ (upward heat flux at the surface), ‘QFX’ (upward moisture flux at the surface), ‘LH’ (latent heat flux at the surface), ‘SNOWC’ (flag indicating snow coverage (1 for snow cover)), and so on. The data is in netCDF format with a spatial resolution of 10 km.
LI Maoshan CHEN Xuelong
Based on 100m risk assessment data set and 100m vulnerability assessment data set, this data set respectively gives different weights to the risk and vulnerability (the risk weight is 0.8, and the vulnerability weight is 0.2), and 34 key node 100m risk assessment data sets are obtained by adding. One belt, one road area, is evaluated for flood risk in extreme areas. The data provide basis for local government departments to make decisions, and early warning before flood disasters, so that we can gain valuable time to take measures to prevent and reduce disasters, and to reduce the loss of lives and property of people caused by floods.
GE Yong LI Qiangzi LI Yi
Vulnerability assessment dataset of hectometre level for 34 key nodes assessment the flood risk of key nodes in the Belt and Road under the extreme precipitation events, in order to provide basis for decision-making for the local government department, at the same time before flood disaster early warning, which may take the disaster prevention and mitigation measures for the precious time, reduce people's lives and property damage brought by the flood. Based on the data of GDP, population, land ues, road density and river density in the Belt and Road, this dataset combined with the methods of spatial analysis of ArcGIS, assigning different weights to each indicator and building assessment 34 key nodes under the condition of extreme precipitation in flood vulnerability level, which was divided into 5 levels by using natural break point method, representing no vulnerability, low vulnerability, middle vulnerability, high vulnerability, extreme high vulnerability, respectively.
GE Yong LI Qiangzi LI Yi
Based on the world surface water data (wod) from 1984 to 2018, this data set selects several indexes of precipitation, topography and land use type, and combines with the spatial analysis method in ArcGIS, constructs and evaluates the risk level of flood disaster in 34 key nodes under extreme precipitation conditions. One belt, one road, 34 critical nodes, is evaluated for the risk of flooding in the key areas along the extreme precipitation events. It provides a basis for local government departments to make decisions and early warning before the flood. Thus, we can gain valuable time to take measures to prevent and reduce disasters, and to reduce people's lives and property losses caused by floods. Loss.
GE Yong LI Qiangzi LI Yi
Data set of surface inundation caused by historical extreme precipitation evaluated the surface inundation range of One Belt And One Road key areas under extreme precipitation, providing a basis and reference for the decision-making of local government departments, so as to give early warning before the occurrence of extreme precipitation and reduce the loss of life and property caused by extreme precipitation.This data set to the extreme precipitation threshold set "and" the extreme precipitation recognition "as the foundation, to confirm the extreme precipitation time node and the area, and then to NASA's web site to download the submerged range products corresponding to the time and region, combining ArcGIS spatial analysis was used to connect the above data, build the data sets of historical extreme precipitation caused surface submerged range for 34 key nodes. The data mainly includes 34 key nodes (Vientiane, China-Myanmar oil and gas pipeline, China-Laos Thai-Cambodia railway, Alexandria, Yangon, Kwantan, Kolkata, Warsaw, Karachi, Yekaterinburg, Yekaterinburg and other regions).
HE Yufeng
Under the background of global warming, the frequency and intensity of drought are increasing. The lack of water resources, food crisis and ecological deterioration (such as desertification) caused by drought disasters directly threaten the national food security and social and economic development. The technical level of drought disaster risk assessment and emergency management needs to be improved. One belt, one road area has one belt, one road area is fragile, agricultural land is concentrated and drought is frequent. Monitoring the drought level and its temporal and spatial changes in large areas by using remote sensing satellites is of great scientific and practical significance for scientifically grasping the drought pattern, regional differentiation characteristics and its impact on agricultural land in the "one belt and one road" area. The percentage of precipitation anomaly reflects the deviation degree between the precipitation of a certain period and the average state of the same period, expressed as a percentage. Based on the daily rainfall data of GPM imerg final run (GPM), the precipitation of corresponding area is calculated. The distribution characteristics of drought of different grades are analyzed by using the grade evaluation index of precipitation anomaly percentage. The spatial resolution is 200m. The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).
WU Hua
In April 2014 and may 2016, 21 Lakes (7 non thermal lakes and 14 thermal lakes) were collected in the source area of the Yellow River (along the Yellow River) respectively. The abundance of hydrogen and oxygen allogens was measured by Delta V advantage dual inlet / hdevice system in inno tech Alberta laboratory in Victoria, Canada. The isotope abundance was expressed in the form of δ (‰) (relative to the average seawater abundance in Vienna) ）Test error: δ 18O: 0.1 ‰, δ D: 1 ‰. The data also includes Lake area and lake basin area extracted from Landsat 2017 image data in Google Earth engine.
WAN Chengwei
This data set describes the temporal and spatial distribution of precipitation in the Upper Brahmaputra River Basin. We integrate (CMA, GLDAS, ITP-Forcing, MERRA2, TRMM) five sets of reanalysis precipitation products and satellite precipitation products, and combine the observation precipitation of 9 national meteorological stations from China Meteorological Administration (CMA) and 166 rain gauges of the Ministry of Water Resources (MWR) in the basin. The time range is 1981-2016, the time resolution is 3 hours, the spatial resolution is 5 km, and the unit is mm/h. The data will provide better data support for the study of Upper Brahmaputra River Basin, and can be used to study the response of hydrological process to climate change. Please refer to the instruction document uploaded with the data for specific usage information.
WANG Yuanwei WANG Lei LI Xiuping ZHOU Jing
Meteorological data is a group of data reflecting the weather, which can be divided into climate data and weather data. This data set mainly includes the rainfall data and temperature data in the meteorological data (pre represents rainfall and T2 represents temperature in the data set). This data set is from the CRU (climate research unit) global grid data provided by the University of East Anglia in the UK (http://www.cgiar-csi.org/). Cru data set is obtained by interpolating the data of 365 observation stations in Central Asia, which has high accuracy in Central Asia. This data set uses CRU to obtain rainfall and temperature data of five Central Asian countries through ArcGIS batch cutting. Data format: GeoTIFF; spatial resolution: 0.5 °; time scale: Monthly Scale. The meteorological data is widely used and can be integrated with resources in different fields. It plays an important role in the development and construction of transportation, new energy, agriculture, mobile Internet software development and service, public management, smart city based on big data technology, smart transportation, smart food and other fields.
Climatic Research Unit CRU
