Meteorological elements of the dataset include the near-surface land-air exchange parameters, such as downward/upward longwave/shortwave radiation flux, momentum flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the tropopause are also included. Independent evaluations were conducted for the dataset by comparison between the observational data and the most recent ERA5 reanalysis data. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.
LI Fei, Ma Shupo, ZHU Jinhuan, ZOU Han , LI Peng , ZHOU Libo
The Tibetan Plateau Subregional Dynamical Downscaling Dataset-Standard Year (TPSDD-Standard) is a high spatial-temporal resolution gridded dataset for the study of land-air exchange processes and lower atmospheric structure over the entire Tibetan Plateau, taking into account the climatic characteristics of each subregion of the Tibetan Plateau. Based on the 500 hPa multi-year average of the geopotential height field over the Tibetan Plateau, the year (2014) with the largest pattern correlation coefficient with this geopotential height field is selected as the standard year, which means that it can roughly reflect the multi-year average status of the atmosphere over the Tibetan Plateau. The temporal resolution of this data is 1 hour and the spatial resolution is 5 km. Meteorological elements of the dataset include near-surface land-air exchange parameters such as downward/upward long-wave/short-wave radiation fluxes, sensible heat fluxes, latent heat fluxes, etc. In addition, the 3-dimensional vertical distribution of wind, temperature, humidity, and pressure from the surface to the top of the troposphere is also included. The dataset was independently evaluated by comparing the observed data with the latest ERA5 reanalysis data. The results demonstrate the accuracy and superiority of the dataset, which offers great potential for future climate change studies.
LI Fei, Ma Shupo, ZHU Jinhuan, ZHOU Libo , LI Peng , ZOU Han
This data set is the conventional meteorological observation data of Maqu grassland observation site in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
This data set is the conventional meteorological observation data of the Ngoring Lake Grassland Observation site (GS) in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity（specific humidity in 2020）, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
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
This data set includes the daily average values of air temperature, air pressure, relative humidity, wind speed, precipitation, total radiation, p2.5 concentration, short wave radiation, etc. observed by the comprehensive observation and research station of atmosphere and environment of Everest from 2017 to 2018.
MA Yaoming
This data set includes the daily average data of air temperature, relative humidity, precipitation, wind speed, wind direction, net radiation, air pressure, etc. of Southeast Tibet station from January 1, 2017 to December 31, 2018.
Luo Lun, ZHU Liping
Near surface atmospheric forcing data were produced by using Wether Research and Forecasting (WRF) model over the Heihe River Basin at hourly 0.05 * 0.05 DEG resolution, including the following variables: 2m temperature, surface pressure, water vapor mixing ratio, downward shortwave & upward longwave radiation, 10m wind field and the accumulated precipitation. The forcing data were validated by observational data collected by 15 daily Chinese Meteorological Bureau conventional automatic weather station (CMA), a few of Heihe River eco-hydrological process comprehensive remote sensing observation (WATER and HiWATER) site hourly observations were verified in different time scales, draws the following conclusion: 2m surface temperature, surface pressure and 2m relative humidity are more reliable, especially 2m surface temperature and surface pressure, the average errors are very small and the correlation coefficients are above 0.96; correlation between downward shortwave radiation and WATER site observation data is more than 0.9; The precipitation agreed well with observational data by being verified based on rain and snow precipitation two phases at yearly, monthly, daily time scales . the correlation coefficient between rainfall and the observation data at monthly and yearly time scales were up to 0.94 and 0.84; the correlation between snowfall and observation data at monthly scale reached 0.78, the spatial distribution of snowfall agreed well with the snow fractional coverage rate of MODIS remote sensing product. Verification of liquid and solid precipitation shows that WRF model can be used for downscaling analysis in complex and arid terrain of Heihe River Basin, and the simulated data can meet the requirements of watershed scale hydrological modeling and water resources balance. The data for 2000-2012 was provided in 2013. The data for 2013-2015 was updated in 2016. The data for 2016-2018 was updated in 2019. The data for 2019-2021 was updated in 2021.
PAN Xiaoduo
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.
CHEN Xuelong
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 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_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. The missing data were denoted by -6999. (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. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This data set includes the daily values of temperature, air pressure, relative humidity, wind speed, precipitation, total radiation, etc. observed at Namuco station from January 1, 2017 to December 31, 2018.
WANG Junbo, WU Guangjian
The dataset records the Ali Desert Environment Integrated Observation and Research Station, the meteorological dataset for 2017-2018, and the time resolution of the data is days. It includes the following basic meteorological parameters: temperature (1.5 meters from the ground, once every half hour, unit: Celsius), relative humidity (1.5 meters from the ground, half an hour, unit: %), wind speed (1.5 meters from the ground, half an hour) , unit: m / s), wind direction (1.5 meters from the ground, once every half hour, unit: degrees), air pressure (1.5 meters from the ground, once every half hour, unit: hPa), precipitation (24 hours, unit: mm ), water vapor pressure (unit: Kpa), evaporation (unit: mm), downward short-wave radiation (unit: W/m2), upward short-wave radiation (unit: W/m2), downward long-wave radiation (unit: W/m2) ), upward long-wave radiation (unit: W/m2), net radiation (unit: W/m2), surface albedo (unit: %). Data collection location: Observation Field of Ali Desert Environment Comprehensive Observation and Research Station, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Longitude: 79°42'5"; Latitude: 33°23'30"; Altitude: 4264 meters.
ZHAO Huabiao
Based on the WRF model, using ERA5 reanalysis data as the initial and boundary fields, the high-resolution low-level atmospheric structure and the earth atmosphere exchange data set of the Qinghai Tibet Plateau are preliminarily obtained by the method of dynamic downscaling. The time range of this data set is from August 1 to August 31, 2014, with a time resolution of 1 hour, a horizontal range of 25 °N-40 °N, 70oE-105oE, and a horizontal resolution of 0.05 °. The data format is NetCDF, and one file is output every hour. The file is named after the date. The lower atmospheric structure data includes temperature, relative humidity, water vapor mixing ratio, potential height, meridional wind and latitudinal wind meteorological elements, with 34 isobaric surfaces in the vertical direction; the surface air exchange data set includes the upward / downward short wave radiation, upward / downward long wave radiation, surface sensible heat and flux, 2m air temperature and water vapor mixing ratio, 10m wind, etc. The data set can provide data support for the study of weather process and climate environment in the Tibetan Plateau.
Ma Shupo
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
1) Data content : total column water / precipitable water; 2) Data sources and processing methods: ECMWF-interm monthly mean analysis; 3) Data quality description: time resolution: monthly, spatial resolution: 0.7°*0.7°; 4) Data application results and prospects: this data can be used for analysis of water resources in the air.
YAN Hongru
Central Asian meteorological station observation data set includes field observation data of temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation, soil heat flux, sunshine time and soil temperature at 10 field weather stations in central Asia. The 10 field stations cover different ecosystem types such as farmland, forest, grassland, desert, desert, wetland, plateau and mountain. The original meteorological data collected by the ground meteorological observation stations in this data set are obtained after format conversion after screening and auditing. The data quality is good. Various types of climate in the Middle East, fragile ecological environment, the frequent meteorological disasters, the establishment of the data set for long-term ecological environment monitoring, disaster prevention and mitigation in central Asia, central Asia, climate change and ecological environment in the areas of study provides data support, ecological environment monitoring in central Asia has been obtained in the study of the application.
LI Yaoming LI Yaoming
1. Data content: air temperature, relative humidity, precipitation, air pressure, wind speed, average total radiation, total net radiation value and daily average water vapor pressure data. 2. Data source and processing method: Observed by American campel high-altitude automatic weather station, air temperature and humidity sensor model HMP155A; wind speed and wind direction model: 05103-45; net radiometer: CNR 4 Net Radiometer four component; atmospheric pressure sensor: CS106; Rain gauge: TE525MM. The automatic weather station automatically collects data every 10 minutes, and collects daily statistical data to obtain daily average weather data. 3. Data quality description: Data is automatically acquired continuously. 4. Data application results and prospects: The weather station is located in the middle of the glacier, and the meteorological data can provide data guarantee for simulating the response of oceanic glacier changes to global climate change in the context of future climate change.
LIU Jing
CMADS V1.0(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.0)Version of the data set introduces the technology of STMAS assimilation algorithm . It was constructed using multiple technologies and scientific methods, including loop nesting of data, projection of resampling models, and bilinear interpolation. The CMADS series of datasets can be used to drive various hydrological models, such as SWAT, the Variable Infiltration Capacity (VIC) model, and the Storm Water Management model (SWMM). It also allows users to conveniently extract a wide range of meteorological elements for detailed climatic analyses. Data sources for the CMADS series include nearly 40,000 regional automatic stations under China’s 2,421 national automatic and business assessment centres. This ensures that the CMADS datasets have wide applicability within the country, and that data accuracy was vastly improved. The CMADS series of datasets has undergone finishing and correction to match the specific format of input and driving data of SWAT models. This reduces the volume of complex work that model builders have to deal with. An index table of the various elements encompassing all of East Asia was also established for SWAT models. This allows the models to utilize the datasets directly, thus eliminating the need for any format conversion or calculations using weather generators. Consequently, significant improvements to the modelling speed and output accuracy of SWAT models were achieved. Most of the source data in the CMADS datasets are derived from CLDAS in China and other reanalysis data in the world. The integration of air temperature, air pressure, humidity, and wind velocity data was mainly achieved through the LAPS/STMAS system. Precipitation data were stitched using CMORPH’s global precipitation products and the National Meteorological Information Center’s data of China (which is based on CMORPH’s integrated precipitation products). The latter contains daily precipitation records observed at 2,400 national meteorological stations and the CMORPH satellite’s inversion precipitation products.The inversion algorithm for incoming solar radiation at the ground surface makes use of the discrete longitudinal method by Stamnes et al.(1988)to calculate radiation transmission. The resolutions for CMADS V1.0, V1.1, V1.2, and V1.3 were 1/3°, 1/4°, 1/8°, and 1/16°, respectively. In CMADS V1.0 (at a spatial resolution of 1/3°), East Asia was spatially divided into 195 × 300 grid points containing 58,500 stations. Despite being at the same spatial resolution as CMADS V1.0, CMADS V1.1 contains more data, with 260 × 400 grid points containing 104,000 stations. For both versions, the stations’ daily data include average solar radiation, average temperature, average pressure, maximum and minimum temperature, specific humidity, cumulative precipitation, and average wind velocity. The CMADS comprises other variables for any hydrological model（under 'For-other-model' folder ）: Daily Average Temperature, Daily Maximum Temperature, Daily Minimum Temperature, Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind, and Daily average Atmospheric Pressure. Introduction to metadata of CMADS CMADS storage path description:(CMADS was divided into two datesets) 1.CMADS-V1.0\For-swat\ --specifically driving the SWAT model 2.CMADS-V1.0\For-other-model\ --specifically driving the other hydrological model(VIC,SWMM,etc.) CMADS--\For-swat-2009\ folder contain:(Station\ and Fork\) 1).Station\ Relative-Humidity-58500\ Daily average relative humidity(fraction) Precipitation-58500\ Daily accumulated 24-hour precipitation(mm) Solar radiation-58500\ Daily average solar radiation(MJ/m2) Tmperature-58500\ Daily maximum and minimum temperature(℃) Wind-58500\ Daily average wind speed(m/s) Where R, P, S, T, W+ dimensional grid number - the number of longitude grid is the station in the above five folders respectively.(Where R,P,S,T,W respective Daily average relative humidity,Daily cumulative precipitation(24h),Daily mean solar radiation(MJ/m2),Daily maximum and minimum temperature(℃) and Daily mean wind speed (m/s)) respectively.Data format is (.dbf) 2).Fork\ (Station index table over East Asia) PCPFORK.txt (Precipitation index table) RHFORK.txt (Relative humidity index table) SORFORK.txt (Solar radiation index table) TMPFORK.txt (Temperature index table) WINDFORK.txt (Wind speed index) CMADS--\For-swat-2012\ folder contain:(Station\ and Fork\) Storage structure is consistency with \For-swat- 2009\.However, all the data in this directory are only available in TXT format and can be readed by SWAT2012. 3)\For-other-model\ (Includes all weather input data required by the any hydrologic model (daily).) Atmospheric-Pressure-txt\ Daily average atmospheric pressure(hPa) Average-Temperature-txt\ Daily average temperature(℃) Maximum-Temperature-txt\ Daily maximum temperature(℃) Minimum-Temperature-txt\ Daily minimum temperature(℃) Precipitation-txt\ Daily accumulated 24-hour precipitation (mm) Relative-Humidity-txt\ Daily average relative humidity(fraction) Solar-Radiation-txt\ Daily average solar radiation(MJ/m2) Specific-Humidity-txt\ Daily average Specific Humidity(g/kg) Wind-txt\ Daily average wind speed(m/s) Data storage information: data set storage format is .dbf and .txt Other data information: Total data: 33.6GB Occupied space: 35.2GB Time: From year 2008 to year 2016 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58500 stations Spatial resolution: 1/3 * 1/3 * grid points Vertical range: None
Meng Xianyong, Wang Hao
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
