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Dataset Title:  ISCCP H Global Gridded (HGG) By time, latitude, longitude   RSS
Institution:  International Cloud Climatology Project (ISCPP)   (Dataset ID: iscpp_hgg_by_time_lat_lon)
Information:  Summary ? | License ? | Metadata | Background | Make a graph
 
Dimensions ? Start ? Stride ? Stop ?  Size ?    Spacing ?
 time (UTC) ?      99352    3h 0m 0s (even)
  < slider >
 latitude (degrees_north) ?      180    1.0 (even)
  < slider >
 longitude (degrees_east) ?      360    1.0 (even)
  < slider >
 
Grid Variables (which always also download all of the dimension variables) 
 eqheight (Equal-area cell mean topographic height, m) ?
 satcode (Satellite code number, 1) ?
 cell_origin (Cell origin code, 1) ?
 fill_gmts (Number of missing GMTs in gap, 1) ?
 fill_days (Number of missing days in gap, 1) ?
 scene (Scene identification) ?
 snoice (Snow/ice cover, percent) ?
 n_total (Total number of pixels, 1) ?
 cldamt (Cloud amount, percent) ?
 cldamt_ir (Cloud amount (using IR data), percent) ?
 cldamt_irmarg (Cloud amount uncertainty (using IR data), percent) ?
 pc (Mean cloud-top pressure (PC) for cloudy pixels, hPa) ?
 pc_ir (Mean cloud-top pressure (PC) for IR-cloudy pixels, hPa) ?
 sigma_pc_ir (Standard deviation of cloud-top pressure (PC) for IR-cloudy pixels, hPa) ?
 tc (Mean cloud-top temperature (TC) for cloudy pixels, K) ?
 tc_ir (Mean cloud-top temperature (TC) for IR-cloudy pixels, K) ?
 sigma_tc_ir (Standard deviation of cloud-top temperature (TC) for IR-cloudy pixels, K) ?
 tau (Mean cloud optical depth (TAU) for cloudy pixels, 1) ?
 tau_ir (Mean cloud optical depth (TAU) for IR-cloudy pixels, 1) ?
 sigma_tau_ir (Standard deviation of cloud optical depth (TAU) for IR-cloudy pixels, 1) ?
 wp (Mean cloud water path (WP) for cloudy pixels, cm) ?
 wp_ir (Mean cloud water path (WP) for IR-cloudy pixels, cm) ?
 sigma_wp_ir (Standard deviation of cloud water path (WP) for IR-cloudy pixels, cm) ?

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The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 4.258692e+8, 1.49886e+9;
    String axis "T";
    String bounds "time_bounds";
    String calendar "proleptic_gregorian";
    String ioos_category "Time";
    String long_name "Forecast time for ForecastModelRunCollection";
    Float64 missing_value NaN;
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    Int32 _ChunkSizes 180;
    String _CoordinateAxisType "Lat";
    Float32 actual_range -89.5, 89.5;
    String axis "Y";
    String bounds "lat_bounds";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
    Float32 valid_max 90.0;
    Float32 valid_min -90.0;
  }
  longitude {
    Int32 _ChunkSizes 360;
    String _CoordinateAxisType "Lon";
    Float32 actual_range 0.5, 359.5;
    String axis "X";
    String bounds "lon_bounds";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
    Float32 valid_max 360.0;
    Float32 valid_min 0.0;
  }
  eqheight {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String long_name "Equal-area cell mean topographic height";
    String units "m";
  }
  satcode {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Satellite code number";
    String units "1";
    Byte valid_max 99;
    Byte valid_min 0;
  }
  cell_origin {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String flag_meanings "original single-satellite-fill multi-satellite-fill";
    Byte flag_values 0, 1, 2;
    String long_name "Cell origin code";
    String units "1";
  }
  fill_gmts {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Number of missing GMTs in gap";
    String units "1";
  }
  fill_days {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Number of missing days in gap";
    String units "1";
  }
  scene {
    Int32 _ChunkSizes 8, 180, 360;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String flag_meanings "day-water day-land day-coast night-water night-land night-coast no-data";
    Int16 flag_values 1, 2, 3, 101, 102, 103, -1;
    String long_name "Scene identification";
  }
  snoice {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String description "Mean snow/ice cover for the cell";
    String long_name "Snow/ice cover";
    String units "percent";
    Byte valid_max 100;
    Byte valid_min 0;
  }
  n_total {
    Int32 _ChunkSizes 5, 180, 360;
    Int16 _FillValue 32767;
    String coordinates "time_run time lat lon";
    String long_name "Total number of pixels";
    String standard_name "number_of_observations";
    String units "1";
    Int16 valid_max 32766;
    Int16 valid_min 0;
  }
  cldamt {
    Int32 _ChunkSizes 5, 180, 360;
    String coordinates "time_run time lat lon";
    String description "Cloud detected by either IR or VIS thresholds (IR at night but diurnally corrected), amount determined by ratio with total number of pixels";
    String long_name "Cloud amount";
    String standard_name "isccp_cloud_area_fraction";
    String units "percent";
  }
  cldamt_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String coordinates "time_run time lat lon";
    String description "Cloud detected by IR threshold regardless of VIS threshold";
    String long_name "Cloud amount (using IR data)";
    String standard_name "isccp_cloud_area_fraction";
    String units "percent";
  }
  cldamt_irmarg {
    Int32 _ChunkSizes 5, 180, 360;
    String coordinates "time_run time lat lon";
    String description "Marginal cloud detection by IR threshold regardless of VIS threshold";
    String long_name "Cloud amount uncertainty (using IR data)";
    String note "This is the ISCCP variable: n_irmarg_cloudy represented as a fraction. It represents the fraction of pixels that are colder than clear sky by a smaller amount than what is flagged in cldamt_ir and represents cloud amount uncertainty.";
    String units "percent";
  }
  pc {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell average of cloud top pressure with VIS-adjusted location during daytime and nighttime location diurnally corrected";
    String long_name "Mean cloud-top pressure (PC) for cloudy pixels";
    String standard_name "air_pressure_at_cloud_top";
    String units "hPa";
  }
  pc_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell average of cloud top pressures for clouds detected by IR threshold regardless of VIS threshold, no VIS-adjustment of location";
    String long_name "Mean cloud-top pressure (PC) for IR-cloudy pixels";
    String standard_name "air_pressure_at_cloud_top";
    String units "hPa";
  }
  sigma_pc_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Grid cell spatial standard deviation of cloud top pressure for clouds detected by IR threshold regardless of VIS threshold, no VIS-adjustment of location";
    String long_name "Standard deviation of cloud-top pressure (PC) for IR-cloudy pixels";
    String units "hPa";
  }
  tc {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell average of cloud top temperature with VIS-adjusted location during daytime and nighttime location diurnally corrected";
    String long_name "Mean cloud-top temperature (TC) for cloudy pixels";
    String standard_name "air_temperature_at_cloud_top";
    String units "K";
  }
  tc_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell average of cloud top temperatures for clouds detected by IR threshold regardless of VIS threshold, no VIS-adjustment of location";
    String long_name "Mean cloud-top temperature (TC) for IR-cloudy pixels";
    String standard_name "air_temperature_at_cloud_top";
    String units "K";
  }
  sigma_tc_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Grid cell spatial standard deviation of cloud top temperature for clouds detected by IR threshold regardless of VIS threshold, no VIS-adjustment of location";
    String long_name "Standard deviation of cloud-top temperature (TC) for IR-cloudy pixels";
    String units "K";
  }
  tau {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell (radiatively-weighted) average of cloud visible optical thickness during daytime and diurnally interpolated values during nighttime, liquid and ice clouds combined";
    String long_name "Mean cloud optical depth (TAU) for cloudy pixels";
    String standard_name "atmosphere_optical_thickness_due_to_cloud";
    String units "1";
  }
  tau_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell (radiatively-weighted) average of visible optical thickness for clouds detected by IR threshold regardless of VIS threshold, liquid and ice clouds combined";
    String long_name "Mean cloud optical depth (TAU) for IR-cloudy pixels";
    String standard_name "atmosphere_optical_thickness_due_to_cloud";
    String units "1";
  }
  sigma_tau_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Grid cell spatial standard deviation of visible optical thickness for clouds detected by IR threshold regardless of VIS threshold, liquid and ice clouds combined";
    String long_name "Standard deviation of cloud optical depth (TAU) for IR-cloudy pixels";
    String units "1";
  }
  wp {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell average of cloud water path (linear average of optical thickness times particle radius) during daytime and diurnally interpolated values during nighttime, liquid and ice clouds combined";
    String long_name "Mean cloud water path (WP) for cloudy pixels";
    String units "cm";
  }
  wp_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Grid cell average of cloud water path (linear average of optical thickness times particle radius) for clouds detected by IR threshold regardless of VIS threshold, liquid and ice clouds combined";
    String long_name "Mean cloud water path (WP) for IR-cloudy pixels";
    String units "cm";
  }
  sigma_wp_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Grid cell spatial standard deviation of cloud water path for clouds detected by IR threshold regardless of VIS threshold, liquid and ice clouds combined";
    String long_name "Standard deviation of cloud water path (WP) for IR-cloudy pixels";
    String units "cm";
  }
  NC_GLOBAL {
    String _CoordSysBuilder "ucar.nc2.dataset.conv.CF1Convention";
    String acknowledgement "This project received funding support from NASA REASON PROGRAM, NASA MEASURES PROGRAM and NOAA CLIMATE DATA RECORD (CDR) PROGRAM";
    String cdm_data_type "Grid";
    String comment "---------- TO RE-MAP EQUAL-AREA MAP TO EQUAL-ANGLE (SQUARE LON,LAT) MAP ---------- For display purposes, the ISCCP equal-area map may be converted to an equal-angle map using replication. The variables 'eqlat_index', 'sqlon_beg' and 'sqlon_end'are provided for this purpose. Each equal-area cell is replicated into a specific range of longitude cells in the equal-angle map. For example, to remap an equal-area array eqvar[41252] to an equal-angle array sqmap[360,180], each eqvar[i] should be replicated into the range of cells indicated by sqlon_beg[i] and sqlon_end[i], and the lat index eqlat_index[i]. Using Fortran notation the assignment is: sqmap[sqlon_beg[i]:sqlon_end[i], eqlat_index[i]] = eqvar[i]. ---------- TO CONVERT COUNT UNITS TO PHYSICAL UNITS ---------- When attribute conversion_table is present for any variable, the reported values of count units may be converted to physical quantities by using the specified conversion table variable as a look-up table whose index is count value 0-255. For example, temperature = tmptab(count), temperature_variance = tmpvar(count), pressure = pretab(count), reflectance = rfltab(count), optical_depth = tautab(count), ozone = ozntab(count), humidity = humtab(count), water_path = wpatab(count). ---------- DEFINITION OF CLOUD TYPES ---------- VIS/IR cloud types are defined by a histogram of cloud top pressure and cloud optical depth, for both liquid and ice clouds. IR cloud types are defined by a histogram of cloud top pressure. Identification labels for the 18 VIS/IR cloud types and the 3 IR cloud types are given in the 'cloud_type_label' and 'cloud_irtype_label' variables, which correspond to the order of the cloud type variable arrays. ---------- TO COMPUTE CLOUD TOP HEIGHT ---------- Cloud top height in meters may be computed by applying lapse rate to the difference between cloud top temperature and surface temperature. Given rlapse 6.5, the formula is:  height_meters = (ts - tc) / rlapse * 1000.0. ---------- TO COMPUTE TOTAL IR BRIGHTNESS TEMPERATURE ---------- Using IR cloud/clear categories: n_ir_clear = n_total - n_ir_cloudy, and then total_ir_bt = ( (n_ir_cloudy * ir_ircloudy) + (n_ir_clear * ir_irclear) ) / n_total, where ir_ircloudy and ir_irclear are values of count units. The resulting total_ir_bt can be converted from count units to physical units using the tmptab conversion table. Using VIS/IR cloud/clear categories: n_clear = n_total - n_cloudy, and then total_ir_bt = ( (n_cloudy * ir_visircloudy)+ (n_clear * ir_visirclear) / n_total, where ir_visircloudy and ir_visirclear are values of count units. The resulting total_ir_bt can be converted from count units to physical units using the tmptab conversion table. ---------- TO COMPUTE TOTAL VIS RADIANCE ---------- Using IR cloud/clear categories: n_ir_clear = n_total - n_ir_cloudy, and then total_vis_radiance = ( (n_ir_cloudy * vis_ircloudy) + (n_ir_clear * vis_irclear) ) / n_total. Using VIS/IR cloud/clear categories: n_clear = n_total - n_cloudy, and then total_vis_radiance = ( (n_cloudy * vis_visircloudy)+ (n_clear * vis_visirclear) / n_total.";
    String contributor_name "William B. Rossow, Alison Walker, Violeta Golea, NOAA, EUMETSAT, ESA, JP/JMA, CHINA/CMA, BR/INPE, NASA";
    String contributor_role "principalInvestigator, processor, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider";
    String Conventions "CF-1.4, ACDD-1.3";
    String creator_email "ncdc.isccp.team@noaa.gov";
    String creator_institution "NOAA National Centers for Environmental Information (NCEI)";
    String creator_name "NOAA National Centers for Environmental Information (NCEI); Ken Knapp, Bill Hankins, Alisa Young, Anand Inamdar";
    String creator_type "institution";
    String creator_url "http://www.ncei.noaa.gov";
    String date_created "2019-07-19T07:27:35Z";
    String date_issued "2019-07-19T07:27:35Z";
    String date_metadata_modified "2019-07-19T07:27:35Z";
    String date_modified "2019-07-19T07:27:35Z";
    Float64 Easternmost_Easting 359.5;
    String geospatial_bounds "POLYGON((-90.0 0.0, -90.0 360.0, 90.0 360.0, 90.0 0.0, -90.0 0.0))";
    String geospatial_bounds_crs "EPSG:4326";
    Float64 geospatial_lat_max 89.5;
    Float64 geospatial_lat_min -89.5;
    Float64 geospatial_lat_resolution 1.0;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 359.5;
    Float64 geospatial_lon_min 0.5;
    Float64 geospatial_lon_resolution 1.0;
    String geospatial_lon_units "degrees_east";
    String history 
"Fri Jul 19 07:27:36 2019: ncatted -a units,lon_bounds,c,c,degrees_east -a comment,lon_bounds,c,c,longitude values at east and west bounds of the grid cell -a units,lat_bounds,c,c,degrees_north -a comment,lat_bounds,c,c,latitude values at north and south bounds of the gridcell -a units,pc_bounds,c,c,hPa -a comment,pc_bounds,c,c,Pressure values at bounds of the pressure bins -a units,tau_bounds,c,c,1 -a comment,tau_bounds,c,c,Optical depth values at bounds of the depth bins -a axis,lat,c,c,Y -a axis,lon,c,c,X -a geospatial_lat_min,global,m,f,-90 -a geospatial_lat_max,global,m,f,90 -a geospatial_lat_resolution,global,m,f,1 -a geospatial_lon_min,global,m,f,0 -a geospatial_lon_max,global,m,f,360 -a geospatial_lon_resolution,global,m,f,1 -a geospatial_vertical_min,global,m,f,10 -a geospatial_vertical_max,global,m,f,1025 -a units,satcodes,d,, -a units,satids,d,, -a units,satnames,d,, -a units,cloud_irtype_label,d,, -a units,cloud_type_label,d,, /glfs2/isccp-p/basic/intermediate//temp_file3.nc -O /glfs2/isccp-p/prd/basic/hgg/2017/ISCCP-Basic.HGG.0.GLOBAL.2017.06.30.1800.GPC.10KM.CS00.EA1.00.nc
Fri Jul 19 07:27:35 2019: ncatted -a conversion_table,,d,, -a title,global,a,c, Basic -a description,snoice,m,c,Mean snow/ice cover for the cell -a source,global,o,c,The source for the ISCCP Basic data files are the original ISCCP files. ISCCP Basic represents a subset of variables from ISCCP that have been remapped to equal-angle, do not use table to store data, etc. in order to make the files CF compliant -a product_version,global,m,c,v01r00 Basic -a date_issued,global,m,c,2019-07-19T07:27:35Z -a date_created,global,m,c,2019-07-19T07:27:35Z -a date_modified,global,m,c,2019-07-19T07:27:35Z -a date_metadata_modified,global,m,c,2019-07-19T07:27:35Z -a description,levpc,c,c,Coordinate variable for cloud frequency histograms -a description,levtau,c,c,Coordinate variable for cloud frequency histograms -a units,^cldamt,m,c,percent -a bounds,time,c,c,time_bounds -a valid_min,n_total,m,s,0 -a valid_max,n_total,m,s,32766 -a valid_min,snoice,m,b,0 -a valid_max,snoice,m,b,100 -a long_name,cldamt,m,c,Cloud amount -a long_name,cldamt_ir,m,c,Cloud amount (using IR data) -a long_name,cldamt_irmarg,m,c,Cloud amount uncertainty (using IR data) -a note,cldamt_irmarg,c,c,This is the ISCCP variable: n_irmarg_cloudy represented as a fraction. It represents the fraction of pixels that are colder than clear sky by a smaller amount than what is flagged in cldamt_ir and represents cloud amount uncertainty. -a long_name,cldamt_types,m,c,Cloud amount by cloud type -a long_name,cldamt_irtypes,m,c,Cloud amount by IR cloud type -a calendar,time,c,c,gregorian -a cell_methods,eqheight,c,c,area: mean -a cell_methods,pc,c,c,area: mean -a cell_methods,pc_ir,c,c,area: mean -a cell_methods,pc_pcdist,c,c,area: mean -a cell_methods,pc_type,c,c,area: mean -a cell_methods,^sigma,c,c,area: standard_deviation -a cell_methods,^tc,c,c,area: mean -a cell_methods,tau,c,c,area: mean -a cell_methods,tau_ir,c,c,area: mean -a cell_methods,tau_type,c,c,area: mean -a cell_methods,^wp,c,c,area: mean /glfs2/isccp-p/basic/intermediate//temp_file2.nc -O /glfs2/isccp-p/basic/intermediate//temp_file3.nc
Fri Jul 19 07:27:33 2019: ncks --no-abc -4 -L 5 /glfs2/isccp-p/basic/intermediate//temp_file.nc -O /glfs2/isccp-p/basic/intermediate//temp_file2.nc
2019-05-14T19:51:21.000Z bhankins d1fill_weekly /glfs2/isccp-p/prd/wrkdirs/2017_06 2017 06 01 2017 06 30
2019-05-14T19:46:11.000Z bhankins d1fill_daily /glfs2/isccp-p/prd/wrkdirs/2017_06 2017 06 01 2017 06 30
2019-05-14T19:37:57.000Z bhankins d1fill_diurnal /glfs2/isccp-p/prd/wrkdirs/2017_06 2017 06 01 2017 06 30
2019-03-28T20:12:49.000Z bhankins d1prod /glfs2/isccp-p/prd/wrkdirs/2017_06 2017 06 01 2017 06 30 ;
FMRC Best Dataset
2024-06-24T16:42:01Z https://www.ncei.noaa.gov/thredds/dodsC/cdr/isccp_hgg_agg/ISCCP-H_Aggregation_Basic_Gridded_Global_(HGG)_best.ncd
2024-06-24T16:42:01Z https://www.ncei.noaa.gov/erddap/griddap/iscpp_hgg_by_time_lat_lon.das";
    String id "ISCCP.HGG.0.GLOBAL.2017.06.30.1800.GPC.10KM.CS00.EQ1.00.nc";
    String infoUrl "https://www.ncei.noaa.gov/thredds/catalog/cdr/isccp_hgg_agg/catalog.html?dataset=cdr/isccp_hgg_agg/ISCCP-H_Aggregation_Basic_Gridded_Global_(HGG)_best.ncd";
    String institution "International Cloud Climatology Project (ISCPP)";
    String instrument "Himawari-8 AHI, SEVIRI, GOES-15 Imager, GOES-13 Imager, SEVIRI,, AVHRR-3";
    String instrument_vocabulary "NASA Global Change Master Directory (GCMD) Instruments Keywords Version 8.1";
    Int32 isccp_day 30;
    Int32 isccp_gmt 18;
    String isccp_input_files "nom/ds/ISCCP.HGS.v01r00.MOP2.2017.06.30.1800.NOM.10KM.CS1239012419.EQ1.00.nc ins/ds/ISCCP.HGS.0.MET8.2017.06.30.1800.MTI.10KM.CS1239012419.EQ1.00.nc gow/ds/ISCCP.HGS.0.GOEF.2017.06.30.1800.CSU.10KM.CS1239012419.EQ1.00.nc met/ds/ISCCP.HGS.0.META.2017.06.30.1800.EUM.10KM.CS1239012419.EQ1.00.nc gms/ds/ISCCP.HGS.0.HIM8.2017.06.30.1800.JMA.10KM.CS1239012419.EQ1.00.nc noa/ds/ISCCP.HGS.0.NOAJ.2017.06.30.1800.NOA.10KM.CS1239012419.EQ1.00.nc goe/ds/ISCCP.HGS.0.GOED.2017.06.30.1800.AES.10KM.CS1239012419.EQ1.00.nc";
    Int32 isccp_month 6;
    Int32 isccp_number_of_satellites_contributing 7;
    Int32 isccp_percent_empty_cells 0;
    Int32 isccp_percent_full_cells 100;
    Int32 isccp_year 2017;
    String keywords "Earth Science > Atmosphere > Atmospheric Chemistry > Oxygen Compounds > Ozone, Earth Science > Atmosphere > Atmospheric Pressure > Surface Pressure, Earth Science > Atmosphere > Atmospheric Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Air Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Surface Temperature > Skin Temperature, Earth Science > Atmosphere > Atmospheric Temperature > Upper Air Temperature > Vertical Profiles, Earth Science > Atmosphere > Atmospheric Water Vapor, Earth Science > Atmosphere > Atmospheric Water Vapor > Humidity, Earth Science > Atmosphere > Atmospheric Water Vapor > Water Vapor Profiles, Earth Science > Atmosphere > Clouds, Earth Science > Atmosphere > Clouds > Cloud Microphysics > Cloud Liquid Water/Ice, Earth Science > Atmosphere > Clouds > Cloud Microphysics > Cloud Optical Depth/Thickness, Earth Science > Atmosphere > Clouds > Cloud Properties, Earth Science > Atmosphere > Clouds > Cloud Properties > Cloud Fraction, Earth Science > Atmosphere > Clouds > Cloud Properties > Cloud Top Pressure, Earth Science > Atmosphere > Clouds > Cloud Properties > Cloud Top Temperature, Earth Science > Atmosphere > Clouds > Cloud Properties > Cloud Vertical Distribution, Earth Science > Atmosphere > Clouds > Cloud Types, Earth Science > Cryosphere > Snow/Ice, Earth Science > Land Surface > Surface Radiative Properties, Earth Science > Land Surface > Surface Radiative Properties > Reflectance, Earth Science > Land Surface > Surface Thermal Properties, Earth Science > Land Surface > Surface Thermal Properties > Skin Temperature, Earth Science > Land Surface > Topography > Terrain Elevation, Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature, Earth Science > Spectral/Engineering > Infrared Wavelengths > Brightness Temperature, Earth Science > Spectral/Engineering > Infrared Wavelengths > Infrared Imagery, Earth Science > Spectral/Engineering > Platform Characteristics > Viewing Geometry, Earth Science > Spectral/Engineering > Sensor Characteristics > Viewing Geometry, Earth Science > Spectral/Engineering > Visible Wavelengths > Visible Imagery, Earth Science > Spectral/Engineering > Visible Wavelengths > Visible Radiance";
    String keywords_vocabulary "NASA Global Change Master Directory (GCMD) Science Keyword Version 8.1";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    String location "Proto fmrc:ISCCP-H_Aggregation_Basic_Gridded_Global_(HGG)";
    String metadata_link "gov.noaa.ncdc.C00956";
    String naming_authority "gov.noaa.ncdc";
    String NCO "netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco)";
    Float64 Northernmost_Northing 89.5;
    String platform "HIM-8, METEOSAT-10, GOES-15, GOES-13, METEOSAT-8, NOAA-19, METOP-A";
    String platform_vocabulary "NASA Global Change Master Directory (GCMD) Platforms Keyword Version 8.1";
    String processing_level "3";
    String product_version "v01r00 Basic";
    String program "NOAA Climate Data Record Program for satellites, FY 2016";
    String project "International Satellite Cloud Climatology Project (ISCCP)";
    String publisher_email "ncdc.isccp.team@noaa.gov";
    String publisher_institution "NOAA National Centers for Environmental Information (NCEI)";
    String publisher_name "NOAA National Centers for Environmental Information (NCEI)";
    String publisher_type "institution";
    String publisher_url "http://www.ncei.noaa.gov";
    String references "'Please include a citation for this paper in addition to the dataset citation when using the dataset: Rossow, W.B. and R.A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bulletin of the American Meteorological Society, 80, 2261-2287. doi: https://dx.doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2','ISCCP CDR Climate Algorithm Theoretical Basis Document (C-ATBD)'";
    String source "The source for the ISCCP Basic data files are the original ISCCP files. ISCCP Basic represents a subset of variables from ISCCP that have been remapped to equal-angle, do not use table to store data, etc. in order to make the files CF compliant";
    String sourceUrl "https://www.ncei.noaa.gov/thredds/dodsC/cdr/isccp_hgg_agg/ISCCP-H_Aggregation_Basic_Gridded_Global_(HGG)_best.ncd";
    Float64 Southernmost_Northing -89.5;
    String summary "ISCCP H Global Gridded (HGG) Dimensioned By time, latitude, longitude.";
    String time_coverage_duration "PT3H";
    String time_coverage_end "2017-06-30T22:00:00Z";
    String time_coverage_resolution "PT3H";
    String time_coverage_start "1983-07-01T01:00:00Z";
    String title "ISCCP H Global Gridded (HGG) By time, latitude, longitude";
    Float64 Westernmost_Easting 0.5;
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdBAssta5day.htmlTable?sst[(2007-10-21T00:00:00)][0][(-75):100:(75)][(180):100:(360)]
Thus, the query is often a data variable name (e.g., sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][altitude][latitude][longitude]).

For details, see the griddap Documentation.


 
ERDDAP, Version 2.23
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