netcdf GATP_Southeast_1_4514206029151021379 {
dimensions:
y = 150 ;
x = 170 ;
Time = 81 ;
time = UNLIMITED ; // (81 currently)
variables:
float temperature(Time, y, x) ;
temperature:_FillValue = NaNf ;
temperature:standard_name = "air_temperature" ;
temperature:long_name = "corrected_surface_air_temperature" ;
temperature:units = "degrees Celsius" ;
temperature:FillValue = NaNf ;
temperature:missing_value = NaNf ;
temperature:comment = "Temperature data contains missing values: nan, Actual range: 0.0 to 255.0 ." ;
int time(time) ;
time:units = "minutes since 2023-01-01 00:00:00" ;
time:calendar = "gregorian" ;
time:standard_name = "time" ;
time:long_name = "Time duration" ;
time:axis = "T" ;
float Longitude(y, x) ;
Longitude:standard_name = "longitude" ;
Longitude:long_name = "Longitude" ;
Longitude:units = "degrees_east" ;
float Latitude(y, x) ;
Latitude:standard_name = "latitude" ;
Latitude:long_name = "Latitude" ;
Latitude:units = "degrees_north" ;
float surface_bidirectional_reflectance(y, x) ;
surface_bidirectional_reflectance:grid_cell_size_reflective = 5000.f ;
surface_bidirectional_reflectance:units = "percent" ;
surface_bidirectional_reflectance:standard_name = "surface_bidirectional_reflectance" ;
surface_bidirectional_reflectance:long_name = "surface bidirectional reflectance" ;
// global attributes:
string :title = "Monthly Temperature Data in typical glacier Region January–March 2023" ;
:references = "reference_1: Zhang, D. (2024). Typical glacier front meteorological data and typical permafrost temperature data on the Qinghai Tibet Plateau (2023-2024). National Tibetan Plateau / Third Pole Environment Data Center. \nreference_2: Guo, W.Q., Liu, S.Y., Xu, J.L., et al. (2015). The second Chinese glacier inventory: data, methods and results. Journal of Glaciology, 61(226), 357-372. doi: 10.3189/2015JoG14J209 " ;
:institution = "Shenyang University of Technology" ;
:institution_id = "SUT" ;
:institution_url = "https://www.sut.edu.cn/" ;
string :comment = "This dataset contains spatialization of air temperature in China southeast of Qinghai-Tibet plateau glacier. \nIce-covered study region is including typical glaciers: Jiagang Mountain Glacier in Shenzha, Gunyong Glacier in Langka County, and typical permafrost (Cuonanan and Qumalai). \nThe latitude and longitude coordinates were derived from the second glacier inventory dataset of China (version 1.0) (2006-2011). \nThe selected observations were automatic meteorological data: Jiagang Mountain Glacier in Shenzha County: 88.69 ° E, 30.82 ° N, altitude 5362 meters, with surface cover of gravel and weeds; Gunyong Glacier in Langka County: 90.23 ° E, 28.88 ° N, altitude 4898 meters, with surface cover of bedrock; Cuonanan: 27.924956N, 91.868849E, altitude 4400m; Qumalai: 34.111161N, 95.875248E, altitude 4290m. \nCore Variable: Air temperature (daily air temperature at 5000 m resolution) \nDimensions: (821 150, 170)\n81 → Time steps (2023-01-01–2023-03-22, daily) \n150 → Grid rows (latitude/y-axis)\n170 → Grid columns (longitude/x-axis)\nThe data for the typical glacier region covers the period from 1 January 2023 to 22 March 2023." ;
:program = "Single-Channel Algorithm, Voronoi-Delaunay triangulation, Gaussian Filter, Mean Filter" ;
:license = "CC-BY 4.0" ;
:keywords_vocabulary = "Global Change Master Directory (GCMD): https://earthdata.nasa.gov/earth-observation-data/find-data/gcmd/gcmd-keywords" ;
:keywords = "GCMD: Earth Science>Atmosphere>Atmospheric Temperature>Surface Air Temperature" ;
:contact = "Tianyun Wang <>" ;
:product_version = "1.0.0" ;
:standard_name_vocabulary = "CF Standard Name Table v27" ;
:realm = "landIce" ;
string :summary = "This dataset provides spatialized air temperature estimates for the typical glacier Region in Qinghai-Tibet Plateau. \nThe spatialized air temperature is derived solely from ground-based meteorological measurements interpolated within a geographic framework defined by the second glacier inventory dataset of China (version 1.0) (2006-2011). \nThe spatial interpolation relies on Voronoi-Delaunay triangulation, using station measurements as anchors. \nFor simple and fast smoothing of gridded surface temperature data, use the mean filter; for edge-preserving denoising that maintains spatial gradients, choose the Gaussian filter.\nIt uses the second glacier inventory dataset of China as a spatial reference. \nThe resulting product combines the reliability of station observations with the fine-scale spatial structure of the second glacier inventory dataset of China. \nIt is suitable for studies requiring temperature distribution in China southeast of qinghai-tibet plateau glacier.\nThe spatial grid alignment relies on the second glacier inventory dataset of China. \nThe study area, the typical glacier Region (a ice-covered zone on the Tibetan Plateau), was selected for temperature analysis. \nDaily mean air temperature (°C) was derived from temperature of Typical glacier front meteorological data. \nDaily mean air temperature was calculated from 1 January to 22 March 2023. \nThe study period covers January to March 2023 (1 January – 22 March). " ;
:processing_level = "L4" ;
:source = "source_1: Typical glacier front meteorological data and typical permafrost temperature data on the Qinghai Tibet Plateau (2023-2024) \nsource_2: Liu, S., Guo, W., Xu, J. (2012). The second glacier inventory dataset of China (version 1.0) (2006-2011). National Tibetan Plateau / Third Pole Environment Data Center. https://doi.org/10.3972/glacier.001.2013.db." ;
:source_type = "AER" ;
:frequency = "1hr" ;
:history = "Created on 2025-5-29, using Python netCDF4." ;
:creation_date = "2025-05-29T12:30:00Z" ;
:creator = "Tianyun Wang" ;
:Conventions = "CF-1.8 ATMODAT-3.0" ;
:atmodat = "3.0" ;
:MAP_PROJECTION = "UTM" ;
:Projection_Information = "UTM Zone 47N (WGS84)" ;
:im_GeoTransform = -1603410., 4821.0625, 0., 3706040.5, 0., -4821.0625 ;
:UTM_ZONE = "44" ;
:img_proj = "PROJCS[\"unnamed\",GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]],PROJECTION[\"Albers_Conic_Equal_Area\"],PARAMETER[\"latitude_of_center\",0],PARAMETER[\"longitude_of_center\",105],PARAMETER[\"standard_parallel_1\",25],PARAMETER[\"standard_parallel_2\",47],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1,AUTHORITY[\"EPSG\",\"9001\"]],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH]]" ;
:geospatial_vertical_resolution = "Undefined" ;
:nominal_resolution = "5 km" ;
:geospatial_lat_resolution = "4731.57 m" ;
:geospatial_lon_resolution = "5506.86 m" ;
:crs = "WGS84" ;
:featureType = "point" ;
:further_info_url = "https://swiftbrowser.dkrz.de/tcl_s/C17Xs1bKsPDlGS" ;
:metadata_link = "https://swiftbrowser.dkrz.de/tcl_s/C17Xs1bKsPDlGS/ST_RFR_0124_Naimonanyi" ;
:project = "Heat Island Intensity Prediction in an Intelligent Sponge Urban System in the Qinghai-Tibet Plateau" ;
:grid_mapping_name = "latitude_longitude" ;
}