Zoige Wetland observation point is located at Huahu wetland (102 ° 49 ′ 09 ″ E, 33 ° 55 ′ 09 ″ N) in Zoige County, Sichuan Province, with an initial altitude of 3435 m. The underlying surface is the alpine peat wetland, with well-developed vegetation, water and peat layer. This data set is the meteorological observation data of Zoige Wetland observation point from 2017 to 2019. It is obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments. The time resolution is half an hour, mainly including wind speed, wind direction, air temperature, relative humidity, air pressure, downward short wave radiation, downward long wave radiation.
MENG Xianhong, LI Zhaoguo
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
Photosynthetically active radiation (PAR) is fundamental physiological variable driving the process of material and energy exchange, and is indispensable for researches in ecological and agricultural fields. In this study, we produced a 35-year (1984-2018) high-resolution (3 h, 10 km) global grided PAR dataset with an effective physical-based PAR model. The main inputs were cloud optical depth from the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, the routine variables (water vapor, surface pressure and ozone) from the ERA5 reanalysis data, aerosol from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products and albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) product after 2000 and CLARRA-2 product before 2000. The grided PAR products were evaluated against surface observations measured at seven experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). The instantaneous PAR was validated at the SURFRAD and NEON, and the mean bias errors (MBEs) and root mean square errors (RMSEs) are 5.6 W m-2 and 44.3 W m-2, and 5.9 W m-2 and 45.5 W m-2, respectively, and correlation coefficients (R) are both 0.94 at 10 km scale. When averaged to 30 km, the errors were obviously reduced with RMSEs decreasing to 36.3 W m-2 and 36.3 W m-2 and R both increasing to 0.96. The daily PAR was validated at the SURFRAD, NEON and CERN, and the RMSEs were 13.2 W m-2, 13.1 W m-2 and 19.6 W m-2, respectively at 10 km scale. The RMSEs were slightly reduced to 11.2 W m-2, 11.6 W m-2, and 18.6 W m-2 when upscaled to 30 km. Comparison with the other well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES) reveals that our PAR product was a more accurate dataset with higher resolution than the CRERS. Our grided PAR dataset would contribute to the ecological simulation and food yield assessment in the future.
TANG Wenjun
1) The Qinghai Tibet plateau surface meteorological driving data set (2019-2020) includes four meteorological elements: land surface temperature, mean total precipitation rate, mean surface downward long wave radiation flux and mean surface downward short wave radiation flux. 2) The data set is based on era5 reanalysis data, supplemented by MODIS NDVI, MODIS DEM and fy3d mwri DEM data products. The era5 reanalysis data were downscaled by multiple linear regression method, and finally generated by resampling. 3) All data elements of the Qinghai Tibet plateau surface meteorological driving data set (2019-2020) are stored in TIFF format. The time resolution includes (daily, monthly and annual), and the spatial resolution is unified as 0.1 ° × 0.1°。 4) This data is convenient for researchers and students who will not use such assimilated data in. NC format. Based on the long-term observation data of field stations of the alpine network and overseas stations in the pan third pole region, a series of data sets of meteorological, hydrological and ecological elements in the pan third pole region are established; Complete the inversion of meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacier and frozen soil change and other data products through intensive observation in key areas and verification of sample plots and sample points; Based on the Internet of things technology, a multi station networked meteorological, hydrological and ecological data management platform is developed to realize real-time acquisition, remote control and sharing of networked data.
ZHU Liping, DU Baolong
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
As an important part of global semi-arid grassland, adequately understanding the spatio-temporal variability of evapotranspiration (ET) over the temperate semi-arid grassland of China (TSGC) could advance our understanding of climate, hydrological and ecological processes over global semi-arid areas. Based on the largest number of in-situ ET measurements (13 flux towers) within the TSGC, we applied the support vector regression method to develop a high-quality ET dataset at 1 km spatial resolution and 8-day timescale for the TSGC from 1982 to 2015. The model performed well in validation against flux tower‐measured data and comparison with water-balance derived ET.
LEI Huimin
Surface solar irradiance (SSI) is one of the products of FY-4A L2 quantitative inversion. It covers a full disk without projection, with a spatial resolution of 4km and a temporal resolution of 15min (there are 40 observation times in the whole day since 20180921, except for the observation of each hour, there is one observation every 3hr before and after the hour）, and the spectral range is 0.2µ m～5.0 µ m. The output elements of the product include total irradiance, direct irradiance on horizontal plane and scattered irradiance, the effective measurement ranges between 0-1500 w / m2. The qualitative improvement of FY-4A SSI products in coverage, spatial resolution, time continuity, output elements and other aspects makes it possible to further carry out its fine application in solar energy, agriculture, ecology, transportation and other professional meteorological services. The current research results show that the overall correlation of FY-4A SSI product in China is more than 0.75 compared with ground-based observation, which can be used for solar energy resource assessment in China.
SHEN Yanbo, HU Yueming, HU Xiuqing
Terrestrial actual evapotranspiration (ETa) is an important component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. However, accurately monitoring and understanding the spatial and temporal variability of ETa over the Tibetan Plateau (TP) remains very difficult. Here, the multiyear (2000-2018) monthly ETa on the TP was estimated using the MOD16-STM model supported by datasets of soil properties, meteorological conditions, and remote sensing. The estimated ETa correlates very well with measurements from 9 flux towers, with low root mean square errors (average RMSE = 13.48 mm/month) and mean bias (average MB = 2.85 mm/month), and strong correlation coefficients (R = 0.88) and the index of agreement values (IOA = 0.92). The spatially averaged ETa of the entire TP and the eastern TP (Lon > 90°E) increased significantly, at rates of 1.34 mm/year (p < 0.05) and 2.84 mm/year (p < 0.05) from 2000 to 2018, while no pronounced trend was detected on the western TP (Lon < 90°E). The spatial distribution of ETa and its components were heterogeneous, decreasing from the southeastern to northwestern TP. ETa showed a significantly increasing trend in the eastern TP, and a significant decreasing trend throughout the year in the southwestern TP, particularly in winter and spring. Soil evaporation (Es) accounted for more than 84% of ETa and the spatial distribution of temporal trends was similar to that of ETa over the TP. The amplitudes and rates of variations in ETa were greatest in spring and summer. The multi-year averaged annual terrestrial ETa (over an area of 2444.18×103 km2) was 376.91±13.13 mm/year, equivalent to a volume of 976.52±35.7 km3/year. The average annual evapotranspirated water volume over the whole TP (including all plateau lakes, with an area of 2539.49×103 km2) was about 1028.22±37.8 km3/year. This new estimated ETa dataset is useful for investigating the hydrological impacts of land cover change and will help with better management of watershed water resources across the TP.
MA Yaoming, CHEN Xuelong,
This data set is a national high-resolution solar radiation data set covering 34 years (1983.7-2017.6), with a resolution of 10 km. The data unit is W / m2. The data set is developed by merging the global high-resolution (3 hours, 10 km) surface solar radiation data set (1983-2017) with isccp-hxg cloud products as the main input, with ground based sunshine duration derived surface solar raidation data from 2261 meteorological stations in China by using the geographic weighted regression method. The validation results show that this dataset can provide more accurate simulation of long-term variability of surface solar radiation than that of gewex-srb, cmsaf-clara-a2 and the isccp-hxg based surface solar radiation product. This data can provide favorable data support for the application and research of long-term change of hydrology in land surface process simulation.
FENG Fei, WANG Kaicun
This data set includes the monthly average actual evapotranspiration of the Tibet Plateau from 2001 to 2018. The data set is based on the satellite remote sensing data (MODIS) and reanalysis meteorological data (CMFD), and is calculated by the surface energy balance system model (SEBS). In the process of calculating the turbulent flux, the sub-grid scale topography drag parameterization scheme is introduced to improve the simulation of sensible and latent heat fluxes. In addition, the evapotranspiration of the model is verified by the observation data of six turbulence flux stations on the Tibetan Plateau, which shows high accuracy. The data set can be used to study the characteristics of land-atmosphere interaction and the water cycle in the Tibetan Plateau.
HAN Cunbo, MA Yaoming, WANG Binbin, ZHONG Lei, MA Weiqiang*, CHEN Xuelong, SU Zhongbo
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
The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau.The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5)Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange.(6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling(NEE_fsd) as a measure for spatial and temporal variation.
Felix Nieberding, MA Yaoming, Cristian Wille, Gerardo Fratini, Magnus Ole Asmussen, Yuyang Wang*, MA Weiqiang*, Torsten Sachs
Solar global and direct radiation are measured by radiation sensors (Model TBQ-4-1, TBS-2, China), and temperature and humidity are measured by a HOBO weather station (Model H21, onset company, USA). This dataset is solar radiation and meteorological variables, including solar globla and direct radiation in the wavelength range of 270-3200nm, unit: w/m2. The units of temperature, humidity and water vapor pressure are ℃, %, hPa, respectively. The dataset of solar radiation and meteorological elements come from the measurements of data providers. Data coverage time is 2013-2016. The data set can be used to study the solar radiation and its change mechanism in a subtropical region, China.
BAI Jianhui
The temporal resolution of temperature and radiation data in Central Asia is monthly scale, and the spatial resolution is 0.5 degree and 0.05 degree, respectively. The GCS_WGS_1984 projection coordinate system was used. Among them, the downward short wave radiation, air temperature and vapor pressure data of GLDAS, surface temperature / emissivity data of MOD11C3, surface albedo data of MCD43C3 and ASTER_GEDv4.1 are used for radiation data calculation; the temperature data was calculated by MOD06_ L2 cloud products and MOD07_ L2 atmospheric profile data was calculated. This data is based on the advanced remote sensing algorithm and makes full use of the current high-precision remote sensing data and products, which is different from the traditional climate model for the estimation of climate elements. The data can be used to analyze the spatial and temporal variation characteristics of water resources in Central Asia, analyze the supply-demand relationship of agricultural water resources and evaluate the development potential of water resources.
SONG Jinxi, JIANG Xiaohui
The dataset is a nearly 36-year (1983.7-2018.12) high-resolution (3 h, 10 km) global SSR (surface solar radiation) dataset, which can be used for hydrological modeling, land surface modeling and engineering application. The dataset was produced based on ISCCP-HXG cloud products, ERA5 reanalysis data, and MODIS aerosol and albedo products with an improved physical parameterization scheme. Validation and comparisons with other global satellite radiation products indicate that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). This SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The unit is W/㎡, instantaneous value.
TANG Wenjun
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
The total solar radiation and the total radiation of absorption and scattering material attenuation are measured by the international general solar radiation meter (li200sz, li-cor, Inc., USA). The measured data are total solar radiation, including direct and diffuse solar radiation, with a wavelength range of 400-1100nm. The unit of measurement is w / m2, and the typical error is ± 3% (incidence angle is within 60 °) under natural lighting. The data of sodankyl ä station in the Arctic comes from cooperation with the site and website download. The coverage time of sodankyl ä station in the Arctic is updated to 2018.
BAI Jianhui
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Sidaoqiao Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2018. There were one German BLS900 at Sidaoqiao Superstation. The north tower was set up with the BLS900 receiver and the south tower was equipped with the BLS900 transmitter. The site (north: 101.137° E, 42.008° N; south: 101.131° E, 41.987 N) was located in Ejinaqi, Inner Mongolia. The underlying surfaces between the two towers were tamarisk, populus, bare land and farmland. The elevation is 873 m. The effective height of the LAS was 25.5 m, and the path length was 2350 m. The data were sampled 1 minute. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (Cn2>7.58E-14). (2) The data were rejected when the demodulation signal was small (Average X Intensity<1000). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 was selected. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The missing data from the BLS900 instrument were denoted by -6999. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. 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.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
As the third pole of the Earth, the Tibetan Plateau has a significant impact on regional and global weather and climate as a heat source in spring and summer. In order to explore the temporal and spatial variation characteristics of multi-scale thermal forcing in different time on the plateau, it is necessary to establish a set of plateau heat source (collection) data based on observation data of continuous and reliable long-term observation. Based on the meteorological elements (surface temperature, surface air temperature, wind speed at the height of 10m, daily cumulative precipitation, etc.) of the 80 (32) observation stations on the Tibetan Plateau from 1979 to 2016 (1960-2016) of China Meteorological Bureau, the sensible heat(SH) and latent heat(LH) was calculated. Meanwhile, using satellite data processing to obtain the net radiation flux (RC) from 1984 to 2015 on the plateau, and then a set of quality controlled long-term plateau heat source data was obtained. This data set considers the diurnal variation of the overall heat transfer coefficient when calculating the surface sensible heat flux.
HU Wenting
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Arou Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2018. There were two types of LASs at Arou Superstation: BLS450 and zzlas, produced by Germany and China, respectively. The north tower was set up with the zzlas receiver and the BLS450 transmitter, and the south tower was equipped with the zzlas transmitter and the BLS450 receiver. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 9.5 m, and the path length was 2390 m. The data were sampled 1 minute at both BLS450 and zzlas. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS450: Cn2>7.25E-14, zzlas: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS450: Mininum X Intensity<50; zzlas: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 and Andreas, 1988 were selected for BLS450 and zzlas, respectively. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The data were primarily obtained from BLS450 measurements, and missing flux measurements from the BLS450 instrument were substituted with measurements from the zzlas instrument. The missing data were denoted by -6999. Due to the problems of storing and wireless transmission, data from 5 July to 24 August, were not collected. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. 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, ZHANG Yang, TAN Junlei
This dataset contains the flux measurements from the Sidaoqiao superstation eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (101.1374° E, 42.0012° N) was located in the Ejina Banner in Inner Mongolia Autonomous Region . The elevation is 873 m. The EC was installed at a height of 3.2 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Latent heat flux during November 9 to 21, 2018 were missing due to the sensor malfunction of CO2/H2O gas analyzer. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
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
This dataset contains the flux measurements from the Jingyangling station eddy covariance system (EC) in the upperstream reaches of the Heihe integrated observatory network from August 28 to December 31 in 2018. The site (101.1160E, 37.8384N) was located in the Jingyangling, near Qilian County in Qinghai Province. The elevation is 3750 m. The EC was installed at a height of 4.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during insufficient power supply, data were missing occasionally. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Daman Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2018. There were two types of LASs at Daman Superstation: BLS450 and BLS900, produced by Germany. The north tower was set up with the BLS450 receiver and the BLS900 transmitter, and the south tower was equipped with the BLS450 transmitter and the BLS900 receiver. The site (north: 100.379° E, 38.861° N; south: 100.369° E, 38.847° N) was located in Daman irrigation district, which is near Zhangye, Gansu Province. The underlying surfaces between the two towers were corn, orchard, and greenhouse. The elevation is 1556 m. The effective height of the LASs was 22.45 m, and the path length was 1854 m. The data were sampled 1 minute at both BLS450 and BLS900. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (Cn2>1.43E-13). (2) The data were rejected when the demodulation signal was small (Average X Intensity<1000). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 was selected. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The data were primarily obtained from BLS900 measurements, and missing flux measurements from the BLS900 instrument were substituted with measurements from the BLS450 instrument. The missing data were denoted by -6999. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. 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 dataset contains the flux measurements from the Alpine meadow and grassland ecosystem Superstation superstation eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from September 2 to December 18 in 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The EC was installed at a height of 4.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3A &EC150) was about 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Data during December 18 to December 24, 2018 were missing due to the data collector failure. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references.
Li Xiaoyan
This dataset contains the flux measurements from the Huazhaizi station eddy covariance system (EC) in the midstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (100.3201° E, 38.7659° N) was located in the Zhangye City in Gansu Province. The elevation is 1731 m. The EC was installed at a height of 4.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during May 13 to July 12 and July 16 to August 21, 2018 were missing due to the malfunction of CO2/H2O gas analyzer. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
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 Tibetan Plateau (TP), acting as a large elevated land surface and atmospheric heat source during spring and summer, has a substantial impact on regional and global weather and climate. To explore the multi-scale temporal variation in the thermal forcing effect of the TP，The data set of atmospheric heat source/sink in Tibetan Plateau was prepared as a quantitative analysis tool for calculating heat budget of gas column. the atmospheric heat source/sink dataset consists of three variables: surface sensible heat flux SH, latent heat release LH and net radiation flux RC. here we calculated the surface sensible heat and latent heat release based on 6-h routine observations at 80 (32) meteorological stations during the period 1979–2016：air temperature at 1.5 m and surface temperature and wind speed at 10 m are used to calculate surface sensible heat flux,the latent heat release is estimated precipitation data.The satellite datasets used to calculate the net radiation flux were the Global Energy and Water Cycle Experiment surface radiation budget satellite radiation(GEWEX/SRB) and Clouds and Earth’s Radiant Energy Systems/Energy Balanced And Filled (CERES/EBAF). The monthly shortwave and longwave radiation fluxes at the surface and at the top of the atmosphere (TOA) in GEWEX/SRB and CERES/EBAF were utilized to obtain the net radiation flux for the period 1984–2015 via statistical methods。
DUAN Anmin
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
PAN Xiaoduo
Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
FANG Huajun, Ranga Myneni
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
The data set contains eddy covariance System observation data of Daman super station which is located in the middle reaches of the Heihe Hydro-meteorological Observation Network from January 1, 2017 to December 31, 2017. The site is located in Daman Irrigation District, Zhangye, Gansu Province, and the underlying surface is corn. The latitude and longitude of the observation point is 100.37223E, 38.85551N, and the altitude is 1556.06m. The mount height of the Eddy Covariance System is 4.5 m, the sampling frequency is 10 Hz, the ultrasonic orientation is positive North, and the distance between the ultrasonic wind speed temperature meter (CSAT3) and the CO2/H2O analyzer (Li7500) is 17 cm. The original observation data of the Eddy Covariance System is 10 Hz, and the released data is a 30-minute data processed by Eddypro software. The main steps of the processing include: outlier eliminating, delay time correction, coordinates rotation (secondary coordinates rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. Meanwhile, the quality evaluation of each flux value was performed,mainly includes atmospheric stability (Δst) test and turbulence similarity (ITC) test. The 30-min flux value output of Eddypro software was also screened: (1) Data from the instrument error was eliminated; (2) Data obtained with one hour before and after precipitation was removed; (3) Data with a deletion rate greater than 10% of the 10 Hz raw data every 30 minutes was eliminated; (4) Observation data of weak turbulence at night (u* less than 0.1 m/s) was excluded. The average period of observation data is 30 minutes, 48 data per day, and the missing data is marked as -6999. The data of April 3 and 4 was missing due to Li7500 calibration of the eddy system; data from August 29 to September 5 was missing due to collector problem. Published observation data include: Date/Time, wind direction(°), horizontal wind speed(m/s), lateral wind speed standard deviation(m/s), ultrasonic virtual temperature (°C), water vapor density (g/m3), carbon dioxide concentration(mg/m3), friction velocity (m/s), length (m), sensible heat flux(W/m2), latent heat flux (W/m2), carbon dioxide flux (mg/(m2s)), sensible heat flux quality identification QA_Hs, latent heat flux quality identification QA_LE, carbon dioxide flux quality identification QA_Fc. The quality identification of sensible heat, latent heat, and carbon dioxide flux is divided into three levels (quality mark 0: (Δst <30, ITC<30); 1: (Δst <100, ITC<100); the rest is 2). The meaning of the data time, such as 0:30 represents an average data of 0:00-0:30; the data is stored in *.xls format. For hydro-meteorological network or station information, please refer to Liu et al. (2018). For observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The data set contains the observation data of the eddy covariance system of Sidaoqiao superstation which is located along the lower reaches of the Heihe Hydrometeorological observation network, and the data set covers data from January 1, 2017 to December 31, 2017. The station is located in Sidao Bridge, Ejina Banner, Inner Mongolia, and the underlying surface is Tamarix. The latitude and longitude of the observation station is 101.1374E, 42.0012N, and the altitude is 873 m. The height of the eddy covariance system is 8 meters, the sampling frequency is 10Hz, the ultrasonic orientation is positive north, and the distance between the ultrasonic wind speed and temperature monitor (CSAT3) and the CO2/H2O analyzer (Li7500) is 15cm. The original observation data of the eddy covariance system is 10 Hz, and the released data is a 30-minute data processed by Eddypro software. The main steps of the processing include: outlier eliminating, delay time correction, coordinates rotation (secondary coordinates rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. Meanwhile, the quality evaluation of each flux value was performed,mainly includes atmospheric stability (Δst) test and turbulence similarity (ITC) test. The 30-min flux value output of Eddypro software was also screened: (1) Data from the instrument error was eliminated; (2) Data obtained with one hour before and after precipitation was removed; (3) Data with a deletion rate greater than 10% of the 10 Hz raw data every 30 minutes was eliminated; (4) Observation data of weak turbulence at night (u* less than 0.1 m/s) was excluded. The average period of observation data is 30 minutes, 48 data per day, and the missing data is marked as -6999. The data was missing due to Li7500 calibration of the eddy system on April 7 and 8; the suspicious data caused by instrument drift and other reasons was marked by red fonts. Published observation data include: date/time Date/Time, wind direction(°), horizontal wind speed(m/s), lateral wind speed standard deviation(m/s), ultrasonic virtual temperature (°C), water vapor density (g/m3), carbon dioxide concentration(mg/m3), friction velocity (m/s), length (m), sensible heat flux(W/m2), latent heat flux (W/m2), carbon dioxide flux (mg/(m2s)), sensible heat flux quality identification QA_Hs, latent heat flux quality identification QA_LE, carbon dioxide flux quality identification QA_Fc. The quality identification of sensible heat, latent heat, and carbon dioxide flux is divided into three levels (quality mark 0: (Δst <30, ITC<30); 1: (Δst <100, ITC<100); the rest is 2). The meaning of the data time, such as 0:30 represents an average data of 0:00-0:30; the data is stored in *.xls format. For hydrometeorological network or station information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This data set contains the observation data of vortex correlativity instrument at yakou station upstream of heihe hydrometeorological observation network from January 1, 2017 to December 31, 2017.The station is located in qilian county, qinghai province.The latitude and longitude of the observation point is 100.2421, 38.0142N, and the altitude is 4148 m.The height of the vortex correlation instrument is 3.2m, the sampling frequency is 10Hz, the ultrasonic direction is due to the north, and the distance between the ultrasonic wind speed and temperature instrument (CSAT3) and the CO2/H2O analyzer (Li7500A) is 15cm. The original observation data of vorticity correlativity is 10Hz, and the released data is the data of 30 minutes processed by Eddypro software. The main steps of its processing include: outfield value elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened.(2) data of 1h before and after precipitation were excluded;(3) the missing rate of 10Hz original data is more than 10% every 30min;(4) the observed data of weak turbulence at night were excluded (u* less than 0.1m/s).The average period of observation data was 30 minutes, 48 data a day, and the missing data was marked as -6999.April 13-14, instrument calibration, data missing.Suspicious data caused by instrument drift shall be identified in red.The eddy current correlator will be short of electricity at night in winter, resulting in the loss of data. Observations published include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Carbon dioxide flux mass identification QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest are 2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Liu et al. (2018) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
This data set contains the eddy correlativity observation data of the downstream desert station of heihe hydrometeorological observation network from January 1, 2017 to December 31, 2017.The station is located in ejin banner, Inner Mongolia.The longitude and latitude of the observation point are 100.9872e, 42.1135n and 1054m above sea level.The frame height of the vortex correlativity instrument is 4.7m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.The vortex correlator's Li7500 was calibrated on April 7-8, and the tower was re-reinforced on September 18-23, with data missing.Suspicious data caused by instrument drift, etc., shall be marked in red font.When 10Hz data is missing, the missing data is replaced by the 30-minute data output of the collector. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Li et al. (2013), and for observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The data set contains the vortex correlativity instrument observation data of zhangye wetland station in the middle reaches of heihe hydrometeorological observation network from January 1, 2017 to December 31, 2017.The site is located in zhangye city, gansu province.The latitude and longitude of the observation point is 100.44640E, 38.97514N, and the altitude is 1460.00m.The height of the vortex correlation instrument is 5.2m, the sampling frequency is 10Hz, the ultrasonic direction is due to the north, and the distance between the ultrasonic wind speed and temperature instrument (Gill) and the CO2/H2O analyzer (Li7500A) is 25cm. The original observation data of vorticity correlativity is 10Hz, and the released data is the data of 30 minutes processed by Eddypro software. The main steps of its processing include: outfield value elimination, delay time correction, Angle correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened.(2) data of 1h before and after precipitation were excluded;(3) the missing rate of 10Hz original data is more than 10% every 30min;(4) the observed data of weak turbulence at night were excluded (u* less than 0.1m/s).The average period of observation data was 30 minutes, 48 data a day, and the missing data was marked as -6999.Suspicious data caused by instrument drift and other reasons are marked in red. From April 3 to 12, due to instrument calibration, data is missing.When 10Hz data is missing due to a memory card storage problem, the data is replaced by 30min flux data output from the collector. Observations published include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Carbon dioxide flux mass identification QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest are 2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This data set contains the eddy correlation-meter observation data from January 1, 2017 to December 31, 2017 at the upper reaches of the heihe hydrometeorological observation network.The station is located in qilian county, qinghai province.The longitude and latitude of the observation point are 98.9406e, 38.8399N and 3739 m above sea level.The frame of the vortex correlator is 4.5m high, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500, replaced with Li7500RS in April 2017) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift, etc., shall be marked in red font.The eddy current system Li7500 was calibrated from April 13 to 15, and the collector's data storage problem occurred from July 8 to 12, resulting in missing data.When 10Hz data is missing due to a problem with the memory card storage data, the data is replaced by the 30min flux data output by the collector. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
This data set contains the eddy correlativity observation data from January 1, 2017 to December 31, 2017 at the super station at the upper reaches of heihe hydrometeorological observation network.The station is located in caoban village, aru township, qilian county, qinghai province.The longitude and latitude of the observation point are 100.4643e, 38.0473n and 3033m above sea level.The rack height of the vortex correlativity meter is 3.5m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500A) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift and other reasons are marked with red font, in which the calibration data of the vortex system Li7500A from April 13 to April 14 is missing;When 10Hz data is missing due to a problem with the storage card (2.17-2.23, 3.3-4.12), the data will be replaced by the 30-min flux data output by the collector. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
The data set contains the flux observation data of large aperture scintillator from daman station in the middle reaches of heihe hydrometeorological observation network.Large aperture scintillators of BLS450 and BLS900 models were installed at daman station in the middle reaches of China. The north tower was the receiving end of BLS900 and the transmitting end of BLS450, and the south tower was the transmitting end and the receiving end of BLS900.The observation period is from January 1, 2017 to December 31, 2017.The station is located in dazman irrigation district, zhangye city, gansu province.The latitude and longitude of the north tower is 100.3785 E, 38.8607 N, and the latitude and longitude of the south tower is 100.3685 E, 38.8468 N, with an altitude of about 1556m.The effective height of the large aperture scintillator is 22.45m, the optical diameter length is 1854m, and the sampling frequency is 1min. Large aperture flicker meter raw observation data for 1 min, data released for after processing and quality control of data, including sensible heat flux is mainly combined with the automatic meteorological station observation data, based on similarity theory alonzo mourning - Mr. Hoff is obtained by iterative calculation, the quality control of the main steps include: (1) excluding Cn2 reach saturation data (Cn2 e-13 > 1.43);(2) data with weak demodulation signal strength (Average X Intensity<1000) were eliminated;(3) data at the time of precipitation were excluded;(4) data of weak turbulence under stable conditions were excluded (u* < 0.1m/s).In the iterative calculation process, the stability universal function of Thiermann and Grassl(1992) was selected. Please refer to Liu et al(2011, 2013) for detailed introduction.Due to instrument failure, data of large aperture scintillator was missing from June 6 to July 2, 2017. Some notes on the released data :(1) the middle LAS data is mainly BLS900, the missing time is supplemented by BLS450 observation, and the missing time of both is marked with -6999.(2) data table head: Date/Time: Date/Time (format: yyyy/m/d h:mm), Cn2: structural parameters of air refraction index (unit: m-2/3), H_LAS: sensible heat flux (unit: W/m2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The data set contains the flux observation data of scintillator with large aperture from sidaoqiao station downstream of heihe hydrometeorological observation network.A large aperture scintillator of BLS900 type is installed in the downstream. The north tower is the receiving end and the south tower is the transmitting end.The observation period is from January 1, 2017 to December 31, 2017.The site is located in ejin banner, Inner Mongolia, with tamarix chinensis, populus populus, bare land and cultivated land under it.The latitude and longitude of the north tower is 101.137e, 42.008n, and the latitude and longitude of the south tower is 101.131e, 41.987 N, with an elevation of about 873m.The effective height of the large aperture scintillator is 25.5m, the optical diameter length is 2350m and the sampling frequency is 1min. Large aperture flicker meter raw observation data for 1 min, data released for after processing and quality control of data, including sensible heat flux is mainly combined with the automatic meteorological station observation data, based on similarity theory alonzo mourning - Mr. Hoff is obtained by iterative calculation, the quality control of the main steps include: (1) excluding Cn2 reach saturation data (e-14 Cn2 > 7.58);(2) data with weak demodulation signal strength (Average X Intensity<1000) were eliminated;(3) data at the time of precipitation were excluded;(4) data of weak turbulence under stable conditions were excluded (u* < 0.1m/s).During the iterative calculation, the stability universal function of Thiermann and Grassl(1992) was selected.Please refer to Liu et al(2011, 2013) for detailed introduction.Due to the problem of data storage unit, data of large aperture scintillator was missing from February 21 to March 5, and July 10 to August 18, 2017. A few notes on published data :(1) data missing time is marked by -6999.(2) data table head: Date/Time: Date/Time (format: yyyy/m/d h:mm), Cn2: structural parameters of air refraction index (unit: m-2/3), H_LAS: sensible heat flux (unit: W/m2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The data set contains the flux observation data of large aperture scintillator at areau station upstream of heihe hydrometeorological observation network.Two large aperture scintillation devices of BLS450 and zzlas type were set up in the upstream areau station respectively. The north tower was the receiving end of zzlas and the transmitting end of BLS450, and the south tower was the transmitting end of zzlas and the receiving end of BLS450.The observation time is January 1, 2017, solstice, December 31, 2017.The station is located in the grass daban village, a soft township, qilian county, qinghai province.The latitude and longitude of the north tower is 100.4712e, 38.0568n, and the latitude and longitude of the south tower is 100.4572e, 38.0384 N, with an altitude of about 3033m.The effective height of the large aperture scintillator is 9.5m, the optical diameter length is 2390m, and the sampling frequency is 1min. Large aperture flicker meter raw observation data for 1 min, data released for after processing and quality control of data, including sensible heat flux is mainly combined with the automatic meteorological station observation data, based on similarity theory alonzo mourning - Mr. Hoff is obtained by iterative calculation, the quality control of the main steps include: (1) excluding Cn2 reach saturation data (BLS450: Cn2 > 7.25 e-14, zzlas: Cn2 > 7.84 E - 14).(2) data with weak demodulation signal strength (BLS450: Mininum X Intensity <50) were eliminated;Zzlas: Demod>-20mv);(3) data at the time of precipitation were excluded;(4) data of weak turbulence under stable conditions were excluded (u* < 0.1m/s).In the iterative calculation process, for BLS450, Thiermann and Grassl(1992) stability universal function was selected.For zzlas, select Andreas 1988's stability universal function.Please refer to Liu et al(2011, 2013) for detailed introduction.From April 16 to May 26, 2017, the measurement signal of large aperture scintillator was relatively small, resulting in a large number of missing data. Several notes on the released data :(1) the upstream LAS data is mainly BLS450, the missing time is supplemented by zzlas observation, and the missing time of both is marked by -6999.(2) data table head: Date/Time: Date/Time (format: yyyy/m/d h:mm), Cn2: structural parameters of air refraction index (unit: m-2/3), H_LAS: sensible heat flux (unit: W/m2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format, please refer to the references for details. Please refer to Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
This data set contains the eddy correlation-meter observation data of the mixed forest station downstream of heihe hydrometeorological observation network from January 1, 2017 to December 31, 2017.The station is located in Inner Mongolia ejin banner four road bridge, under the surface is populus and tamarix.The longitude and latitude of the observation point are 101.1335e, 41.9903n and 874 m above sea level.The rack height of the vortex correlativity instrument is 22m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500) is 17cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.2m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.April 7 solstice April 8 due to instrument calibration, 3.24-4.08 infrared gas analyzer error, data missing.Suspicious data caused by instrument drift, etc., are identified in red font.When 10Hz data is missing due to a problem with the memory card storage data, the data is replaced by the 30min flux data output by the collector. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Li et al. (2013), and for observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
This data set contains the eddy correlativity observation data of huachaizi desert station in the middle reaches of heihe hydrological meteorological observation network from January 1, 2017 to December 31, 2017.The station is located in zhangye city, gansu province.The longitude and latitude of the observation point are 100.3201E, 38.7659N and 1731.00m above sea level.The rack height of the vortex correlator is 4.5m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500A) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift, etc., shall be marked in red font.April 3 solstice on April 4, due to the calibration of vortex correlator Li7500A, data was missing.When the 10Hz data of the vortex correlator is missing (1.1-1.22), the data will be filled by the 30-minute data output of the collector. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Li et al. (2013), and for observation data processing, please refer to Liu et al. (2011).
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
CMADS V1.1(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.1) Version of the data set introduced the 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 (2m), air pressure, humidity, and wind speed data (10m) 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 (2m), average pressure, maximum and minimum temperature (2m), specific humidity, cumulative precipitation, and average wind speed (10m). The CMADS comprises other variables for any hydrological model(under 'For-other-model' folder): Daily Average Temperature (2m), Daily Maximum Temperature (2m), Daily Minimum Temperature (2m), Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind (10m), 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 2m temperature(℃) Wind-58500 Daily average 10m 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 2m temperature(℃) Maximum-Temperature-txt Daily maximum 2m temperature(℃) Minimum-Temperature-txt Daily minimum 2m 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 10m wind speed(m/s) Data storage information: data set storage format is .dbf and .txt Other data information: Total data:45GB Occupied space: 50GB Time: From year 2008 to year 2014 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
The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.
MA Yaoming
The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
HU Zeyong
This data set includes meteorological data observed by the carbon flux station in the Guoluo Army Ranch in Qinghai. The temporal coverage is from 2005 to 2009, and the temporal resolution is 1 day. Meteorological and carbon flux data observation methods: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. Both carbon flux and meteorological data were automatically recorded by the instruments and manually checked. During the data observation process, the operation of the instrument and the selection of the observation objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and productivity estimation. This data contains observation items as follows: Temperature °C Precipitation mm Wind speed m/s Soil temperature at 5 cm depth °C Photosynthetically active radiation µmol/m²s Total radiation W/m²
ZHAO Xinquan
In east Asia, institute of atmospheric physics, Chinese Academy of Sciences key laboratory of regional climate and environment development of regional integration environment with independent copyright system model RIEMS 2.0, on the basis of the regional climate model RIEMS 2.0 in the United States center for atmospheric research and the development of the university of binzhou mesoscale model (MM5) is a static dynamic framework, coupled with some physical processes needed for the study climate solutions.These processes include the biosphere - atmosphere transmission solutions, using FC80 closed Grell cumulus parameterization scheme, MRF planetary boundary condition and modify the CCM3 radiation, such as the heihe river basin observation and remote sensing data of important parameters in the model for second rate, USES the heihe river basin vegetation data list data of land use in 2000 and 30 SEC DEM data in heihe river basin, build up suitable for the study of heihe river basin ecological - hydrological processes of the regional climate model. Drive field: ERA-INTERIM reanalysis data Spatial scope: the grid center of the simulation area is located at (40.30n, 99.50e), the horizontal resolution is 3 km, and the number of simulated grid points in the model is 161 (meridional) X 201 (zonal). Projection: LAMBERT conformal projection, two standard latitudes of 30N and 60N. Time range: from January 1, 2011 to December 31, 2016, with an interval of 6 hours Description of file contents: monthly storage by grads without format.Except the maximum and minimum temperature as the daily scale, the other variables are all 6-hour data. MATLAB can be used to read, visible tmax_erain_xiong_heihe.m file description. Data description of heihe river basin: 1) Anemometer west wind (m/s) college usurf for short 2) Anemometer south wind(m/s), vsurf for short College 3) Anemometer temperature (deg) K tsurf College 4) maximal temperature (deg) K tmax 5) minimal temperature (deg K) abbreviated as tmin 6) college Anemom specific humidity (g/kg) college qsurf for short 7) value (mm/hr) is simply value p College 8) Accumulated evaporation (mm/hr) evap 9) sensible heat (watts/m**2/hr) for short College 10) Accumulated net infrared radiation (watts/m * * 2 / hr) netrad for short College definition file name: -erain-xiong. Month and year
XIONG Zhe
Solar radiation data were obtained using the internationally accepted solar radiation meter (LI200SZ, LI-COR, Inc., USA). The measured data are total solar radiation, including direct and diffuse solar radiation, with a wavelength range of 400-1100 nm. The units of the measurement results are W/㎡, and the typical error under natural lighting is ±3% (within an incident angle of 60°). Data from different locations in the three poles (Everest Station and Namco Station on the Tibetan Plateau, Sodankylä Station in the Arctic, and Dome A Station in the Antarctic) are derived from site cooperation and website downloads. The temporal coverage of data from the Everest Station and Namco Station on the Tibetan Plateau is from 2009 to 2016, that from the Sodankylä Station in the Arctic is from 2001 to 2017, and that from the Dome A Station in the Antarctic is from 2005 to 2014.
BAI Jianhui
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