The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1979~2017, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1
The dataset is a 34-year (1983.7-2017.6) 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.
The source data for this dataset is derived from world soil maps and multiple regional and national soil databases, including soil attributes and soil maps. We have adopted a unified data structure and data processing process to fuse diverse data. We then used the soil type connection method and the soil variable line connection method to obtain the spatial distribution of soil properties. To aggregate these data, we currently use the area weighting method. The raw data has a resolution of 30 seconds, and aggregated data with a 5-minute resolution (about 10km) is provided here. There are eight vertical layers with a maximum depth of 2.3 meters (ie 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m). 1. Data characteristics: Projection: WGS_1984 Coverage: Global Resolution: 0.083333 degrees (about 10 kilometers) Data format: netCDF 2. The data set contains 11 items of general soil information and 34 properties of soil. (1) The general information of the soil is as follows, the file general.zip: No. Description Units 1 additional property 2 available water capacity 3 drainage class 4 impermeable layer 5 nonsoil class 6 phase1 7 phase2 8 reference soil depth cm 9 obstacle to roots 10 soil water regime 11 topsoil texture (2) The 34 soil properties are as follows, files 1-9.zip, 10-18.zip, 19-26.zip, 27-34.zip Soil organic carbon density: SOCD5min.zip: No. Attrubute units Scale factor 1 total carbon% of weight 0.01 2 organic carbon% of weight 0.01 3 total N% of weight 0.01 4 total S% of weight 0.01 5 CaCO3% of weight 0.01 6 gypsum% of weight 0.01 7 pH (H2O) 0.1 8 pH (KCl) 0.1 9 pH (CaCl2) 0.1 10 Electrical conductivity ds / m 0.01 11 Exchangeable calcium cmol / kg 0.01 12 Exchangeable magnesium cmol / kg 0.01 13 Exchangeable sodium cmol / kg 0.01 14 Exchangeable potassium cmol / kg 0.01 15 Exchangeable aluminum cmol / kg 0.01 16 Exchangeable acidity cmol / kg 0.01 17 Cation exchange capacity cmol / kg 0.01 18 Base saturation% 19 Sand content% of weight 20 Silt content% of weight 21 Clay content% of weight 22 Gravel content% of volume 23 Bulk density g / cm3 0.01 24 Volumetric water content at -10 kPa% of volume 25 Volumetric water content at -33 kPa% of volume 26 Volumetric water content at -1500 kPa% of volume 27 The amount of phosphorous using the Bray1 method ppm of weight 0.01 28 The amount of phosphorous by Olsen method ppm of weight 0.01 29 Phosphorous retention by New Zealand method% of weight 0.01 30 The amount of water soluble phosphorous ppm of weight 0.0001 31 The amount of phosphorous by Mehlich method ppm of weight 0.01 32 exchangeable sodium percentage% of weight 0.01 33 Total phosphorus% of weight 0.0001 34 Total potassium% of weight 0.01
The North American Multi-Model Ensemble (NMME) Forecast is a multi-modal ensemble seasonal forecasting system jointly published by the US Model Center (including NOAA/NCEP, NOAA/GFDL, IRI, NCAR, and NASA) and the Canadian Meteorological Centre. The data include retrieval data from 1982 to 2010 and real-time weather forecast data from 2011 to the present. The forecasting system covers the whole world with a temporal resolution of one month and a horizontal spatial resolution of 1°. NMME has nine climate forecasting models, and each contains 6-28 ensemble members, with a forecasting period of 9-12 months. The name, source, ensemble members, and forecasting period of the climate models are as follows: 1) CMC1-CanCM3, Environment Canada, 10 models, 12 months 2) CMC2-CanCM4, Environment Canada, 10 models, 12 months 3) COLA-RSMAS-CCSM3, National Center for Atmospheric Research, 6 models, 12 months 4) COLA-RSMAS-CCSM34, National Center for Atmospheric Research, 10 models, 12 months 5) GFDL-CM2p1-aer04, NOAA Geophysical Fluid Dynamics Laboratory, 10 models, 12 months 6) GFDL-CM2p5-FLOR-A06, NOAA Geophysical Fluid Dynamics Laboratory, 12 models, 12 months 7) GFDL-CM2p5-FLOR-B01, NOAA Geophysical Fluid Dynamics Laboratory, 12 models, 12 months 8) NASA-GMAO-062012, NASA Global Modeling and Assimilation Office, 12 models, 9 months 9) NCEP-CFSv2, NOAA National Centers for Environmental Prediction, 24/28 models, 10 months With the exception of the CFSv2 model (which includes only precipitation and average temperature), the variables of other models include precipitation, average temperature, maximum temperature, and minimum temperature. Each model ensemble member stores one NC file every month for each variable. The meteorological elements, variable names, units, and physical meanings of each variable are as follows: 1) Average temperature, tref, K, monthly average near-surface (2-m) average air temperature 2) Maximum temperature, tmax, K, monthly average near-surface (2-m) maximum air temperature 3) Minimum temperature, tmin, K, monthly average near-surface (2-m) minimum air temperature 4) Precipitation, prec, mm/day, monthly average precipitation. The dataset has been widely applied in climate forecasting, hydrological forecasting, and quantitatively estimating model forecasting uncertainty.
The MODIS land cover type product is a data classification product (MOD12Q1) with different classification schemes for land cover features extracted from Terra data each year. These data are generated by reprojecting the standard MODIS land cover product MOD12Q1 to geographic coordinates with a spatial resolution of one-half degree. The basic land cover classification comprises the 17 types defined by the International Geosphere Biosphere Programme (IGBP): 11 types of natural vegetation classification, 3 types of land use and land inlays, and 3 types of nonvegetation land classification. It covers a longitude range of -180-180 degrees and a latitude range of -64-84 degrees. The data are in GeoTIFF format. This data are free to use, and the copyright belongs to the University of Maryland Department of Geography and NASA.
The land cover classification product is the second phase product of the ESA Climate Change Initiative (CCI), with a spatial resolution of 300 meters and a temporal coverage of 1992-2015. The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84. The classification of the surface coverage is based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations. When the data are used for scientific research purposes, the ESA CCI Land Cover project should be acknowledged. In addition, the published article should be send to firstname.lastname@example.org.
NCEP/NCAR Reanalysis 1 is an assimilation of data from the past (1948-recent). It was developed by the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP–NCAR) in the US to act as an advanced analysis and prediction system. Most of the data are from the original daily average data of the PSD (Physical Sciences Division). However, the data from 1948 to 1957 are slightly different because these data are conventional (non-Gaussian) grid data. The information published on the official website is generally from 1948 to the present, and the latest information is generally updated every two days. For data on an isostatic surface, the general vertical resolution is 17 layers, from 1000 hPa to 10 hPa. The horizontal resolution is typically 2.5° x 2.5°. The NCEP reanalysis data are systematically comparable among international atmospheric science reanalysis data sets. Compared with the reanalysis data of the European Center, the initial year is earlier, and the latest data updates are more frequent. These two sets of reanalysis data are currently the most widely used data sets in the world. For details of the data, please visit the following website: https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
The Randolph Glacier Inventory (RGI) is a complete inventory of global glacier outlines published by GLIMS (Global Land Ice Measurements from Space). It is currently available in six versions: Version 1.0 was published in February 2012, version 2.0 was published in June 2012, version 3.0 was published in April 2013, version 4.0 was published in December 2014, version 5.0 was published in July 2015, and version 6.0 was published in July 2017. The data sets include four versions, which are 6.0, 5.0, 4.0 and 3.2 (revision, August 2013). The data are organized according to different regions. In each region, each glacier record includes a shape file (.shp file and its corresponding .dbf, .prj, and .shx files) and a .csv file of height measurement data. The data are from GLIMS: Global Land Ice Measurements from Space (http://www.glims.org/RGI/) Data quality checks include geometry, topology, and certain attributes, and the following checks were performed: 1) All polygons were checked by the ArcGIS Repair Geometry tool. 2) Glaciers with areas less than 0.01 square kilometres were removed. 3) The topology was checked with the Does Not Overlap rule. 4) The attribute sheet was checked by Fortran subroutines and Python scripts for data quality.
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.
The data set of ERA-Interim global surface air temperature reanalysis (1979-2016) was obtained from the European Center for Medium-Range Weather Forecasts (ECMWF) by adopting the ECMWF IFS forecasting system (T255, 60 layers) and using the four-dimensional variational assimilation system (8DVAR) with an analysis window of 12 hours to assimilate satellite remote sensing data (TOVS, GOES, Meteosat, etc.) and regular observations of the surface and upper atmosphere in different regions of the world and from different sources. The surface air temperature (2 m air temperature) data span the time range from January 1979 to December 2016 and cover the whole world with the projection of equal latitude and longitude, a temporal resolution of six hours, and a horizontal resolution of 0.75. The data were stored as a NetCDF format file once a month and included longitude, latitude, time, and temperature (t2m, unit: K), with 241 latitudinal grid points and 480 longitudinal grid points.