The distribution map of permafrost and ground-ice around the Arctic is the only data map of permafrost compiled by the international permafrost association in collaboration with permafrost research institutes of several countries in 1997. The map describes the distribution and properties of permafrost and subsurface ice conditions in the northern hemisphere (20°N to 90°N). Permafrost was divided into continuous (90-100%), discontinuous (50-90%), sporadic (10-50%), island (<10%) and non-permafrost by continuous division of permafrost scope. The subsurface ice abundance at the top 20 m is divided by the percentage of ice volume (>20%, 10-20%, <10% and 0%). Published ESRI-shape files are based on 1:10 million paper maps (Brown et al. 1997). The map can be used in related research such as global climate change, polar resource development and environmental protection. The China section is shown in thumbnail. See the reference for more information (Heginbottom et al. 1993). The format of the data is the ESRI shapefile, you can download it on the snow and ice data center (http://nsidc.org/data/ggd318.html).
O. Ferrians, J. A. Heginbottom, E. Melnikov
The scanned picture of the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Shi Yafeng, Meidesheng, 1988) is geometrically corrected and then digitized in the data set, and by taking altitude and latitude into account in combination with the continuity of permafrost, the frozen soil is divided into the predominant permafrost of high-latitude permafrost, island talik permafrost and island permafrost; high-altitude permafrost and mountain permafrost (including Altai, Tianshan Mountain, Qilian Mountain, Hengduan, the Himalayas and Taibai Mountain in East China, Huanggangliang and Changbai Mountain), and the plateau permafrost (the Tibetan Plateau), which is divided into predominant permafrost and island permafrost; and seasonal frozen soil, instantaneous frozen soil and nonfrozen areas.
SHI Yafeng, MI Desheng
Global warming and human activities have led to the degradation of permafrost and the collapse of permafrost, which have seriously affected the construction of permafrost projects and the ecological environment. Based on high-resolution satellite images, the permafrost of oboling in Heihe River Basin of Qinghai Tibet Plateau is taken as the research area, and the object-oriented classification technology of machine learning is used to extract the thermal collapse information in the research area. The results show that from 2009 to 2019, the number of thermal collapse increased from 12 to 16, and the total area increased from 14718.9 square meters to 28579.5 square meters, nearly twice. The combination of high spatial resolution remote sensing and object-oriented classification method has a broad application prospect in the monitoring of thermal thawing and collapse of frozen soil.
JIANG Liming
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese Cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic datasets of the Tibet Plateau mainly takes the Tibetan Plateau as the research region, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Information System.doc", "Datasheet of Chinese Cryospheric Information System.DOC", "Database of the Tibetan Plateau.DOC" and "Database of the Tibetan Plateau 2.DOC".
LI Xin
1. Data overview: This data set is the data set of frozen depth of permafrost observed artificially in qilian station from January 1, 2013 to December 31, 2013, and observed at 08 o 'clock every day. 2. Data content: The data content is the frozen depth data set of the tundra.The frozen depth (length) of the water in the inner rubber tube is used as a record to determine the freezing level and the upper and lower depth of the frozen layer according to the freezing position and length of the water in the frozen pot.In centimeters (cm), round off the whole number and round off the decimal.Observe once a day at 0:8. 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, HAN Chuntan, SONG Yaoxuan, LIU Junfeng, YANG Yong, LIU Zhangwen
The permafrost stability map was created based on the classification system proposed by Guodong Cheng (1984), which mainly depended on the inter-annual variation of deep soil temperature. By using the geographical weighted regression method, many auxiliary data was fusion in the map, such as average soil temperature, snow cover days, GLASS LAI, soil texture and organic from SoilGrids250, soil moisture products from CLDAS of CMA, and FY2/EMSIP precipitation products. The permafrost stability data spatial resolution is 1km and represents the status around 2010. The following table is the permafrost stability classification system. The data format is Arcgis Raster.
RAN Youhua
These data are a digitization of the frozen soil distribution map of the Map of the Glaciers, Frozen Ground and Deserts in China (1:4,000,000). Considering the unification with the global frozen soil classification system, the permafrost is divided into the following five types: (1) Discontinuous permafrost: continuous coefficient 50%-90% (2) Island permafrost: continuous coefficient <50% (3) Plateau discontinuous permafrost: continuous coefficient 50%-90% (4) Plateau island permafrost: continuous coefficient 50%-90% (5) Mountain permafrost The compilation basis of the frozen soil map includes (1) the measured field survey data and exploration of frozen soil; (2) aerial image and satellite image interpretation; (3) TOPO30 1-km resolution ground elevation data; and (4) and temperature and ground temperature data. The distribution of frozen soil on the Tibetan Plateau adopted the research results of Zhuotong Nan et al. (2002). Using the average annual temperature data of 76 boreholes along the Qinghai-Tibet Highway, a statistical regression analysis was performed to obtain the relation between annual mean ground temperature, latitude and elevation. Based on the relation combined with GTOPO30 elevation data (global 1-km digital elevation model data developed by the Earth Resources Observation and Technology Center of the U.S Geological Survey), the annual average ground temperature distribution over the entire Tibetan Plateau was simulated. Taking the annual average ground temperature of 0.5 °C as the boundary between permafrost and seasonal frozen soil and the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Yafeng Shi, et al., 1988) as a reference, the boundary between the plateau discontinuous permafrost and plateau island permafrost was determined. In addition, taking the Distributions Map of Permafrost in Daxiao Hinganling Northeast China (Dongxin Guo, et al. 1981), the Distribution Map of Permafrost and Ground Ice in Circum-Arctic (Brown et al. 1997) and the latest field data as references, the permafrost boundary of northeast China has been revised; the mountain permafrost boundary in the northwest mostly adopted the boundary delineated in the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Yafeng Shi, et al., 1988). According to this data set, permafrost area in China is approximately 1.75×106 km2, accounting for 18.25% of the territory of China, among which the mountain permafrost area is 0.29×106 km2, which accounts for 3.03% of the territory of China. For more information, please refer to the Map of the Glaciers, Frozen Ground and Deserts in China (1:4,000,000) specification (Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 2006).
WANG Tao, SHI Yafeng, GUO Dongxin
The data set includes the trends of annual average temperature and rainfall changes at the three meteorological stations in the permafrost section of the Qinghai-Tibet Engineering Corridor over the past 50 years. According to the recorded data, the annual average temperature is experiencing a gradually rising process. The annual average temperature change over the past 56 years in Wudaoliang and Tuotuohe has a good correlation (r2=0.83). In 1957, the average annual temperatures of Wudaoliang and Tuotuohe were -6.6 °C and -5.1 °C, respectively. By 2012, the temperatures of the two stations were -4.6 and -3.1 °C, and the total temperature has risen by approximately 2 °C. The annual average temperature rises by 0.03-0.04 °C. The annual average temperature changes over the past 47 years in Wudaoliang and Anduo also have a good correlation (r2=0.84). In 1966, the average annual temperature in Anduo was -3.0 °C. By 2012, the temperature has risen to -1.8 °C, corresponding to a total temperature rise of approximately 1.2 °C and an annual average temperature rise of 0.02-0.03 °C. The annual average temperature in Wudaoliang and Tuotuohe rose slightly faster than that in Anduo. However, the change in rainfall was more volatile than that of temperature. The correlation between the rainfall change in Wudaoliang and Tuotuohe over the past 56 years is relatively poor (r2=0.60). In 1957, the annual rainfall amounts in Wudaoliang and Tuotuohe were 302 and 309 mm, respectively. By 2012, the annual rainfall amounts at the two stations were 426 and 332 mm. Thus, the rainfall in Wudaoliang had increased by 124 mm, with an annual rainfall increase of approximately 2 mm. In contrast, the annual rainfall in Tuotuohe only increased by 0.4 mm. The correlation between the rainfall change in Wudaoliang and Anduo over the past 47 years is also poor (r2=0.35). In 1966, and 2012, the annual average rainfall amounts in Anduo were 354 and 404 mm. The total increase was approximately 50 mm, and the annual average increase was 1 mm. The annual rainfall in Wudaoliang increased the fastest. The observation data from the three meteorological stations reveal climate changes in the permafrost sections of the Qinghai-Tibet Engineering Corridor. Judging from the overall trend of temperature and rainfall changes, the temperature in the northern and central parts of the corridor has increased rapidly over the past 50 years, exceeding the global average of 0.02 °C/a (IPCC). The rainfall increase in the northern part of the corridor is also obvious, especially the rate of rainfall increase at the Wudaoliang meteorological station. Increases in both temperature and rainfall have a great impact on accelerating the spatial variation in permafrost, and they are the leading cause of permafrost degradation on the Tibetan Plateau.
NIU Fujun, LIN Zhanju, YIN Guoan
This dataset uses daily temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015). It is generated by the dual-index (TB, 37v, SG) freeze-thaw discrimination algorithm. The classification results include the frozen surface, the thawed surface, the deserts and water bodies. The data coverage is the main part of China’s mainland, with a spatial resolution of 25.067525 km via the EASE-Grid projection method, and it is stored in ASCIIGRID format. All the ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the head file, the body content is numerically characterized by the freeze/thaw status of the surface soil: 1 for frozen, 2 for thawed, 3 for desert, and 4 for precipitation. If you want to use the icon for display, we recommend using the ArcView + 3D or Spatial Analyst extension module for reading; in the process of reading, a grid format file will be generated, and the displayed grid file is the graphical expression of the ASCII file. The read method comprises the following. [1] Add the 3D or Spatial Analyst extension module to the ArcView software and then create a new View. [2] Activate View, click File menu, and select the Import Data Source option. When the Import Data Source selection box pops up, select ASCII Raster in the Select import file type box. When the dialog box for selecting the source ASCII file automatically pops up, click to find any ASCII file in the data set, and then press OK. [3] Type the name of the Grid file in the Output Grid dialog box (it is recommended that a meaningful file name is used for later viewing) and click the path to store the Grid file, press OK again, and then press Yes (to select integer data) and Yes (to put the generated grid file into the current view). The generated files can be edited according to the Grid file standard. This completes the process of displaying an ASCII file into a Grid file. [4] In the batch processing, the ASCIGRID command of ARCINFO can be used to write AML files, and then use the Run command to complete the process in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}. The production of this data is supported by the following Natural Science Foundation Projects: Environmental and Ecological Science Data Center of West China (90502010), Land Data Assimilation System of West China (90202014) and Active and Passive Microwave Radiation Transmission Simulation and Radiation Scattering Characteristics of the Frozen Soil (41071226).
LI Xin
A map of the frozen soil distribution in the Republic of Mongolia is digitized from the National Atlas of the Republic of Mongolia (Sodnom and Yanshin, 1990). This data set describes the distribution and general properties of permafrost, seasonally frozen soil, and low-temperature phenomena in the Republic of Mongolia. Two plates were specifically digitized. The first plate, with a scale of 1:12,000,000, describes four general frozen soil regions: (1) continuous and discontinuous permafrost; (2) island-like and sparse island-like permafrost; (3) sporadic permafrost; and (4) seasonally frozen soil. The second plate, with a scale of 1:4,500,000, describes 14 different terrain types. The terrain types are divided based on elevation, annual average temperature, permafrost thickness, melting depth, and freezing depth of seasonally frozen soil. The locations of the six types of low-temperature phenomena in Mongolia are also included: pingos, ice cones, hot karst, detachment failures, solifluction, and cryoplatation processes. The data are provided in the ESRI shape file format and can be downloaded from the US Ice and Snow Data Center.
A. L.Yanshin, Sodnom
1. Data overview: this data set is the data set of artificial observation of frozen soil depth at Qilian station from January 1, 2011 to December 31, 2011, at 08:00 every day. 2. Data content: data content is frozen depth data set of permafrost. Frozen soil observation uses the frozen depth (length) of water poured into the rubber inner tube as a record. According to the position and length of water frozen in the permafrost buried in the soil, the frozen layer and its upper and lower limit depths are measured. In centimeters (CM), rounded to the nearest whole number. Observe once every day at 0.8 o'clock. 3. Space time scope: geographic coordinates: longitude: 99 ° 53 ′ E; latitude: 38 ° 16 ′ n; altitude: 2981.0m
HAN Chuntan, SONG Yaoxuan, LIU Junfeng, YANG Yong, QING Wenwu, LIU Zhangwen
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
This data includes the soil microbial composition data in permafrost of different ages in Barrow area of the Arctic. It can be used to explore the response of soil microorganisms to the thawing in permafrost of different ages. This data is generated by high through-put sequencing using the earth microbiome project primers are 515f – 806r. The region amplified is the V4 hypervariable region, and the sequencing platform is Illumina hiseq PE250; This data is used in the articles published in cryosphere, Permafrost thawing exhibits a greater influence on bacterial richness and community structure than permafrost age in Arctic permafrost soils. The Cryosphere, 2020, 14, 3907–3916, https://doi.org/10.5194/tc-14-3907-2020https://doi.org/10.5194/tc-14-3907-2020 . This data can also be used for the comparative analysis of soil microorganisms across the three poles.
KONG Weidong
This map was compiled by Li Xin and others in 2008 in order to re-count the permafrost area in China and based on the analysis of the existing permafrost map in China. It consists of three parts, of which the Qinghai-Tibet Plateau part uses the simulated permafrost map of the Qinghai-Tibet Plateau (Nanzhuo Copper, 2002), the northeast part comes from the "14 million map of China's Glacier, Frozen Soil and Desert" (Institute of Environment and Engineering in Cold and Arid Regions, Chinese Academy of Sciences, 2006), and the other part uses the map of China's permafrost zoning and types (1: 10 million) (Zhou Youwu and others, 2000). More Information References (Institute of Environment and Engineering in Cold and Arid Regions, Chinese Academy of Sciences, 2006; Nanzhuo Copper, 2002; Zhou Youwu et al., 2000; Li et al, 2008）。
LI Xin, NAN Zhuotong, ZHOU Youwu
This data is obtained by spatial interpolation and permafrost simulation through the surface temperature at 0 cm of nine stations in and outside the source area of the upper reaches of Heihe River. In the figure, 1 represents seasonal frozen soil and 2 represents permafrost. The data is in TIFF format, WGS-84 is used for projection, and the spatial range is 37.7263n-39.0976n, 98.5769e-101.1608e.
GE Shemin
The project “The impact of the frozen soil environment on the construction of the Qinghai-Tibet Railway and the environmental effects of the construction” is part of the “Environmental and Ecological Science in West China” programme supported by the National Natural Science Foundation of China. The person in charge of the project is Wei Ma, a researcher at the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The project ran from January 2002 to December 2004. Data collected in this project included the following: Monitoring data of the active layer in the Beiluhe River Basin (1) Description of the active layer in the Beiluhe River Basin (2) Subsurface moisture data from the Beiluhe River Basin, 2002.9.28-2003.8.10 (Excel file) * Site 1 - Grassland moisture data * Site 2 – Removed turf moisture data * Site 3 - Natural turf moisture data * Site 4 - Gravel moisture data * Site 5 - Insulation moisture data (3) Subsurface temperature data from the Beiluhe River Basin, 0207-0408 Excel file * Temperature data for the ballast surface * Temperature data for insulation materials * Temperature data for a surface without vegetation * Temperature data for a grassland surface * Temperature data for a grit and pebble surface Data on the impact of construction on the ecological environment were obtained at Fenghuoshan, Tuotuohe, and Wudaoliang. Sample survey included plant type, abundance, community coverage, total coverage, aboveground biomass ratio and soil structure. The moisture content at different depths of the soil was detected using a time domain reflectometer (TDR). A set of soil samples was collected at a depth of 0-100 cm at each sample site. An EKKO100 ground-penetrating radar detector was used to continuously sample 1-1.5 km long sections parallel to the road to determine the upper limit depth of the frozen soil. 3. Predicted data: The temperature of the frozen soil at different depths and times was predicted in response to temperature increases of 1 degree and 2 degrees over the next 50 years based on initial surface temperatures of -0.5, -1.5, -2.5, -3.5, and -4.5 degrees. 4. The frozen soil parameters of the Qinghai-Tibet Railway were as follows: location, railway mileage, total mileage (km), frozen soil type mileage, mileage of zones with an average temperature conducive to permafrost, frozen soil with high temperatures and high ice contents, frozen soils with high temperatures and low ice contents, frozen soils with low temperatures and high ice contents, frozen soils with low temperatures and low ice contents, and melting area.
MA Wei, WU Qingbai
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
Li Xinwu, Liang Lei
The compilation basis of frozen soil map includes: (1) frozen soil field survey, exploration and measurement data; (2) aerial photo and satellite image interpretation; (3) topo300 1km resolution ground elevation data; (4) temperature and ground temperature data. Among them, the distribution of permafrost in the Qinghai Tibet Plateau adopts the research results of nanzhuo Tong et al. (2002). Using the measured annual average ground temperature data of 76 boreholes along the Qinghai Tibet highway, regression statistical analysis is carried out to obtain the relationship between the annual average ground temperature and latitude, elevation, and based on this relationship, combined with the gtopo30 elevation data (developed under the leadership of the center for earth resources observation and science and technology, USGS) Global 1 km DEM data) to simulate the annual mean ground temperature distribution over the whole Tibetan Plateau. Taking the annual average ground temperature of 0.5 ℃ as the boundary between permafrost and seasonal permafrost, the boundary between discontinuous Permafrost on the plateau and island Permafrost on the plateau is delimited by referring to the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988); in addition, the division map of Permafrost on the big and small Xing'an Mountains in the Northeast (Guo Dongxin et al., 1981), the distribution map of permafrost and underground ice around the Arctic (b According to rown et al. 1997) and the latest field survey data, the Permafrost Boundary in Northeast China has been revised; the Permafrost Boundary in Northwest mountains mostly uses the boundary defined in the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988). According to the data, the area of permafrost in China is about 1.75 × 106km2, accounting for about 18.25% of China's territory. Among them, alpine permafrost is 0.29 × 106km2, accounting for about 3.03% of China's territory. For more information, please refer to the specification of "1:4 million map of glacial and frozen deserts in China" (Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences, 2006)
WANG Tao
In the permafrost area of the upper reaches of Heihe River, 11 numbered typical boreholes are selected, and the thickness values of permafrost and seasonal permafrost are calculated by the temperature interpolation of boreholes. The 0 degree isothermal surface is set as the bottom plate of permafrost and seasonal permafrost. The data include borehole number, longitude and latitude, thickness of frozen soil and type of frozen soil.
ZHANG Tingjun, GAO Tanguang
The data set includes 1. permaice (map of frozen soil types), 2. subsea (subsea boundary vectorgraph), 3. treeline (timberline vectorgraph), 4. nhipa (grid map) and 5. llipa (grid map). Permaice includes the following attribute fields: Num_code (frozen soil attribute code), Combo (frozen soil attribute), extent (frozen soil coverage) and content (ice content). The attribute comparison is as follows. (1) Frozen soil attribute comparison table: 0 （No information） 1 - chf （Continuous permafrost extent with high ground ice content and thick overburden） 2 - dhf （Discontinuous permafrost extent with high ground ice content and thick overburden） 3 - shf （Sporadic permafrost extent with high ground ice content and thick overburden） 4 - ihf （Isolated patches of permafrost extent with high ground ice content and thick overburden） 5 - cmf （Continuous permafrost extent with medium ground ice content and thick overburden） 6 - dmf （Discontinuous permafrost extent with medium ground ice content and thick overburden） 7 - smf （Sporadic permafrost extent with medium ground ice content and thick overburden） 8 - imf （Isolated patches of permafrost extent with medium ground ice content and thick overburden） 9 - clf （Continuous permafrost extent with low ground ice content and thick overburden） 10 - dlf （Discontinuous permafrost extent with low ground ice content and thick overburden） 11 - slf （Sporadic permafrost extent with low ground ice content and thick overburden） 12 - ilf （Isolated patches of permafrost extent with low ground ice content and thick overburden） 13 - chr （Continuous permafrost extent with high ground ice content and thin overburden and exposed bedrock） 14 - dhr （Discontinuous permafrost extent with high ground ice content and thin overburden and exposed bedrock） 15 - shr （Sporadic permafrost extent with high ground ice content and thin overburden and exposed bedrock） 16 - ihr （Isolated patches of permafrost extent with high ground ice content and thin overburden and exposed bedrock） 17 - clr （Continuous permafrost extent with low ground ice content and thin overburden and exposed bedrock） 18 - dlr （Discontinuous permafrost extent with low ground ice content and thin overburden and exposed bedrock） 19 - slr （Sporadic permafrost extent with low ground ice content and thin overburden and exposed bedrock） 20 - ilr （Isolated patches of permafrost extent with low ground ice content and thin overburden and exposed bedrock） 21 - g （Glaciers） 22 - r （Relict permafrost） 23 - l （Inland lakes） 24 - o （Ocean/inland seas） 25 - ld （Land） （2）The frozen soil coverage attribute comparison table c = continuous (90-100%) d = discontinuous (50-90%) s = sporadic (10-50%) i = isolated patches (0-10%) （3）The ice content comparison table h = high (>20% for "f" landform codes) (>10% for "r" landform codes) m = medium (10-20%) l = low (0-10%) ------------------------------------------------------------ Projection of the shapefiles is: PROJCS["Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area", GEOGCS["GCS_Sphere_ARC_INFO", DATUM["Sphere_ARC_INFO", SPHEROID["Sphere_ARC_INFO",6370997.0,0.0]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["False_Easting",0.0], PARAMETER["False_Northing",0.0], PARAMETER["longitude_of_center",180.0], PARAMETER["latitude_of_center",90.0], UNIT["Meter",1.0]] Projection for the raster (*.byte) files is: Projection: Lambert Azimuthal Units: meters Spheroid: defined Major Axis: 6371228.00000 Minor Axis: 6371228.000 Parameters: radius of the sphere of reference: 6371228.00000 longitude of center of projection: 0 latitude of center of projection: 90 false easting (meters): 0.00000 false northing (meters): 0.00000
O. Ferrians, J. A. Heginbottom, E. Melnikov, ZHANG Tingjun, RAN Youhua
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