Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Guantao site-eddy covariance system) (2008-2010)
  • 2019-10-18
  • 0
  • 1

The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.

More
A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau (1340-2007)
  • 2019-10-03
  • 0
  • 1

This data set is provided by the author of the paper: Huang, R., Zhu, H.F., Liang, E.Y., Liu, B., Shi, J.F., Zhang, R.B., Yuan, Y.J., & Grießinger, J. (2019). A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau since 1340 CE. Climate Dynamics, 53(5-6), 3221-3233. In this paper, in order to understand the past few hundred years of winter temperature change history and its driving factors, the researcher of Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences. Prof. Eryuan Liang and his research team, reconstructed the minimum winter (November – February) temperature since 1340 A.D. on southeastern Tibetan Plateau based on the tree-ring samples taken from 2007-2016. The data set contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf

More
Gridded Monthly Temperature Lapse Rates of the Tibetan Plateau
  • 2019-09-24
  • 0
  • 1

1) Data content (including elements and meanings): Gridded multiyear-average monthly air temperature lapse rate data over the Tibetan Plateau at three kinds of resolutions (i.e. 0.25°, 0.75° and 2°) 2) Data source and processing method: Locally reliable temperature lapse rates are created from filtered MODIS LST-elevation samples by using the thresholds of standard error of elevation and correlation coefficient 3) Data quality description: For ERA-Interim, the validation accuracy (based on 1980-2014 daily mean aire temperature records from 113 stations across the Tibetan Plateau) decreases from ~4℃ to ~2℃ after using the 0.75° temperaturel lapse rate. 4) Data application results and prospects: This dataset can be used for downscaling air temperature from multiple reanalysis datasets.

More
The dataset of spatio-temporal water resources distribution in the source regions of Yangtze River and Yellow River (1998-2017)
  • 2019-09-22
  • 0
  • 1

This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff,soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.

More
The daily microwave precipitation dataset of Tibetan Plateau(2015-2017)
  • 2019-09-22
  • 0
  • 1

The strong spatial and temporal changes of precipitation often make it impossible to accurately know the spatial distribution and intensity changes of precipitation during the precipitation observation of conventional foundation stations. Satellite microwave remote sensing can overcome this limitation and achieve global scale precipitation and cloud observation. Compared with infrared/visible light, which can only reflect cloud thickness and cloud height, microwave can penetrate the cloud, and also use the interaction between precipitation and cloud particles in the cloud and microwave to detect the cloud and rain more directly. This data use the surface precipitation, obtained by the DPR double wave band precipitation radar carried by GPM, as the true value, soil temperature/humidity of NDVI, DEM and ERA5 as reference data. And the multi-band passive brightness temperature data of GMI is used to invert the instantaneous precipitation intensity during the warm season (May-September) in Tibetan Plateau, then the result is re-sampled to the spatial resolution of 0.1°and accumulated them to a day.

More