A long-term dataset of integrated land-atmosphere interaction observations on the Tibetan Plateau (2005-2016)

The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.

0 2020-11-24

Dataset of reconstructed terrestrial water storage in China based on precipitation(2002-2019)

This data set takes China as the research area, and the data set includes "decimal_ time”, "lat”, "lon”, "time”, "time_ bounds”, "TWSA_ REC" and "uncertainty" 7 parameters in total. Among them, "decimal_ time” corresponds to decimal time. There are 191 months (163 months for grace data, 17 months for grace-fo data, and 11 months for grace-fo interval) from April 2002 to December 2019. We have not made up for the missing data of individual months between grace or grace-fo data; "LAT" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the annual product day of the data from January 1, 2002; and "time" corresponds to the annual product day of the data from January 1, 2002_ Bounds "; corresponding to the product day of the year corresponding to the start date and end date of each data month. “TWSA_ "REC" is the monthly change of China's regional land water reserves from April 2002 to December 2019; "uncertainty" is the uncertainty between the data and CSR rl06 mascon products. Using grace satellite gravity data CSR grace / grace-fo rl06 mascon solutions (version 02), China Daily grid precipitation real-time analysis system (version 1.0) data, and cn05.1 temperature data sets, the precipitation reconstruction model was established, and the seasonal and trend terms of CSR rl06 mascon products were considered to obtain the data set of land water storage change based on precipitation reconstruction in China. The data quality is good as a whole, and the error of most regions in China is within 5cm. This data set complements the more than one year data gap between grace and grace-fo satellites, and provides a complete time series for long-term land water storage change analysis in China. As the CSR rl06 mascon product, the average value between 2004.0000 and 2009.999 is deducted from this data set. Therefore, the data of 164-174 months (i.e. July 2017 to may 2018) of this data set can be directly extracted as the estimation of land water storage change in the intermittent period.

0 2020-11-23

Monthly mean evapotranspiration data set of the Tibet Plateau (2001-2018)

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.

0 2020-11-18

PML_V2 global evapotranspiration and gross primary production (2002.07-2019.08)

PML_V2 terrestrial evapotranspiration and total primary productivity dataset, including gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall , Ei) and water body, ice and snow evaporation (ET_water), a total of 5 elements. The data format is tiff, the space-time resolution is 8 days, 0.05°, and the time span is 2002.07-2019.08. Based on the Penman-Monteith-Leuning (PML) model, PML_V2 is coupled to the GPP process based on stomatal conductance theory. GPP and ET mutually restrict and restrict each other, which makes PML_V2 in ET simulation accuracy, which is greatly improved compared with the previous model. The parameters of PML_V2 are divided into different vegetation types and are determined on 95 vorticity-related flux stations around the world. The parameters were then migrated globally according to the MODIS MCD12Q2.006 IGBP classification. PML_V2 uses GLDAS 2.1 meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 terrestrial evapotranspiration and total primary productivity data sets.

0 2020-11-17

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Large aperture scintillometer of Daman Superstation, 2019)

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: BLS900 and RR-RSS460, produced by Germany. The north tower was set up with the BLS900 receiver and the RR-RSS460 transmitter, and the south tower was equipped with the BLS900 transmitter and the RR-RSS460 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 24.1 m, and the path length was 1854 m. The data were sampled 1 minute at both BLS900 and RR-RSS460. 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 (BLS900:Cn2>7.25E-14,RR-RSS460:Cn2>7.84 E-14). (2) The data were rejected when the demodulation signal was small (BLS900:Average X Intensity<1000;RR-RSS460: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 BLS900 and RR-RSS460, respectively. Detailed can refer to Liu et al. (2011, 2013). Due to instrument adjustment and inadequate power supply, the date of missing data for the large aperture scintillator is: 2019.01.22-2019.01.24; 2019.03.01-2019.04.26; 2019.10.28-2019.11.14; 2019.11.29-2019.12.20。 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 RR-RSS460 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, 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. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.

0 2020-11-11

Terrestrial evapotranspiration dataset across China (1982-2017)

This dataset (version 1.5) is derived from the complementary-relationship method, with inputs of CMFD downward short- and long-wave radiation, air temperature, air pressure, GLASS albedo and broadband longwave emissivity, ERA5-land land surface temperature and humidity, and NCEP diffuse skylight ratio, etc. This dataset covers the period of 1982-2017, and the spatial coverage is Chinese land area. This dataset would be helpful for long-term hydrological cycle and climate change research. Land surface actual evapotranspiration (Ea),unit: mm month-1. The spatial resolution is 0.1-degree; The temporal resolution is monthly; The data type is NetCDF; This evapotranspiration dataset is only for land surface.

0 2020-11-04

Land Surface Temperature in China(2003-2017)

The Land Surface Temperature in China STC dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.

0 2020-11-01

Land Surface Temperature in China(2003-2017)

The Land Surface Temperature in China STC dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.

0 2020-11-01

Data set of δ18O stable Isotopes in Precipitation from Tibetan Network for Isotopes(1991–2008)

The stable oxygen isotope ratio (δ 18O) in precipitation is a comprehensive tracer of global atmospheric processes. Since the 1990s, efforts have been made to study the isotopic composition of precipitation at more than 20 stations located on the TP of the Tibetan Plateau, which are located at the air mass intersection between westerlies and monsoons. In this paper, we establish a database of monthly precipitation δ 18O over the Tibetan Plateau and use different models to evaluate the climate control of precipitation δ 18O over TP. The spatiotemporal pattern of precipitation δ 18O and its relationship with temperature and precipitation reveal three different domains, which are respectively related to westerly wind (North TP), Indian monsoon (South TP) and their transition.

0 2020-10-29

Precipitation stable isotopes data in Bomi (2008)

The data includes the daily mean value of stable isotope δ18O in precipitation, the air temperature and precipitation amounts in Bomi in 2008; the precipitation samples are collected by Bomi meteorological station, and the stable isotope of precipitation is measured at the Laboratoire des Sciences du Climat et de l’Environnement, France., The δ18O amounts were measured by equilibration on a MAT-252 mass spectrometer, with an analytical precision of 0.05‰. The air temperatures and precipitation amounts were recorded for each precipitation events at Bomi meteorological stations, through the average of the observed temperature before and after the precipitation event, and through the total precipitation amount for each event. The data study has been published in the Journal of Climate, entitled Precipitation Water Stable Isotopes in the South Tibetan Plateau: Observations and Modeling.

0 2020-10-28