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Dataset Title:  ISCCP H Gridded Monthly (HGM) By time, latitude, longitude   RSS
Institution:  International Cloud Climatology Project (ISCPP)   (Dataset ID: iscpp_hgm_by_time_lat_lon)
Information:  Summary ? | License ? | Metadata | Background | Make a graph
 
Dimensions ? Start ? Stride ? Stop ?  Size ?    Spacing ?
 time (UTC) ?      408    30 days 10h 29m 47s (uneven)
  < 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) 
 eqland (Equal-area cell land area, percent) ?
 eqheight (Equal-area cell mean topographic height, m) ?
 scene (Scene identification) ?
 n_obs (Number of observations, 1) ?
 n_day (Number of day-time observations, 1) ?
 n_orig (Number of original data cells, 1) ?
 n_toplev (Number of satellite hierarchy top-level data cells, 1) ?
 cldamt (Mean cloud amount, percent) ?
 pc (Mean cloud pressure, hPa) ?
 sigma_pc_time (cloud-top pressure (PC) mean standard deviation over time, hPa) ?
 tc (Mean cloud temperature, K) ?
 sigma_tc_time (cloud-top temperature (TC) mean standard deviation over time, K) ?
 tau (Mean cloud cloud optical depth (TAU), 1) ?
 sigma_tau_time (cloud optical depth (TAU) mean standard deviation over time, 1) ?
 wp (Mean cloud water path, cm) ?
 sigma_wp_time (cloud water path (WP) mean standard deviation over time, cm) ?
 cldamt_ir (Mean IR-cloud amount, percent) ?
 pc_ir (Mean IR-cloud pressure, hPa) ?
 sigma_pc_space (cloud-top pressure (PC) IR-cloud mean standard deviation over space, hPa) ?
 tc_ir (Mean IR-cloud temperature, K) ?
 sigma_tc_space (cloud-top temperature (TC) IR-cloud mean standard deviation over space, K) ?
 tau_ir (Mean IR-cloud cloud optical depth (TAU), 1) ?
 sigma_tau_space (cloud optical depth (TAU) IR-cloud mean standard deviation over space, 1) ?
 wp_ir (Mean IR-cloud water path, cm) ?
 sigma_wp_space (cloud water path (WP) IR-cloud mean standard deviation over space, cm) ?
 cldamt_irmarg (Cloud amount uncertainty (using IR data), percent) ?
 snoice (Mean snow/ice amount, percent) ?

File type: (more information)

(Documentation / Bypass this form) ?
 
(Please be patient. It may take a while to get the data.)


 

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 4.285332e+8, 1.4988564e+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;
  }
  eqland {
    Int32 _ChunkSizes 5, 180, 360;
    String coordinates "time_run time lat lon";
    String long_name "Equal-area cell land area";
    String units "percent";
  }
  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";
  }
  scene {
    Int32 _ChunkSizes 8, 180, 360;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String flag_meanings "water land coast no-data";
    Int16 flag_values 1, 2, 3, -1;
    String long_name "Scene identification";
  }
  n_obs {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Number of observations";
    String standard_name "number_of_observations";
    String units "1";
    Byte valid_max 8;
    Byte valid_min 0;
  }
  n_day {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Number of day-time observations";
    String units "1";
    Byte valid_max 8;
    Byte valid_min 0;
  }
  n_orig {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Number of original data cells";
    String units "1";
    Byte valid_max 8;
    Byte valid_min 0;
  }
  n_toplev {
    Int32 _ChunkSizes 8, 180, 360;
    Byte _FillValue -1;
    String _Unsigned "false";
    String coordinates "time_run time lat lon";
    String long_name "Number of satellite hierarchy top-level data cells";
    String units "1";
    Byte valid_max 8;
    Byte valid_min 0;
  }
  cldamt {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String long_name "Mean cloud amount";
    String standard_name "isccp_cloud_area_fraction";
    String units "percent";
  }
  pc {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String long_name "Mean cloud pressure";
    String standard_name "air_pressure_at_cloud_top";
    String units "hPa";
  }
  sigma_pc_time {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Standard deviation of time variations of grid cell average cloud top pressure";
    String long_name "cloud-top pressure (PC) mean standard deviation over time";
    String units "hPa";
  }
  tc {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String long_name "Mean cloud temperature";
    String standard_name "air_temperature_at_cloud_top";
    String units "K";
  }
  sigma_tc_time {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Standard deviation of time variations of grid cell average cloud top temperature";
    String long_name "cloud-top temperature (TC) mean standard deviation over time";
    String units "K";
  }
  tau {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Radiatively-weighted average cloud visible optical thickness, liquid and ice clouds combined";
    String long_name "Mean cloud cloud optical depth (TAU)";
    String standard_name "atmosphere_optical_thickness_due_to_cloud";
    String units "1";
  }
  sigma_tau_time {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Standard deviation of time variations of grid cell average cloud visible optical thickness, liquid and ice clouds combined";
    String long_name "cloud optical depth (TAU) mean standard deviation over time";
    String units "1";
  }
  wp {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Water path given by linear average of cloud optical thickness times particle radius, liquid and ice clouds combined";
    String long_name "Mean cloud water path";
    String units "cm";
  }
  sigma_wp_time {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: standard_deviation";
    String coordinates "time_run time lat lon";
    String description "Standard deviation of time variations of grid cell average cloud water path, liquid and ice clouds combined";
    String long_name "cloud water path (WP) mean standard deviation over time";
    String units "cm";
  }
  cldamt_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Average cloud amount detected by IR threshold regardless of VIS threshold";
    String long_name "Mean IR-cloud amount";
    String units "percent";
  }
  pc_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Average cloud top pressure for clouds detected by IR threshold regardless of VIS threshold";
    String long_name "Mean IR-cloud pressure";
    String standard_name "air_pressure_at_cloud_top";
    String units "hPa";
  }
  sigma_pc_space {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation time: mean";
    String coordinates "time_run time lat lon";
    String description "Time average of spatial standard deviations of cloud top pressure";
    String long_name "cloud-top pressure (PC) IR-cloud mean standard deviation over space";
    String units "hPa";
  }
  tc_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Average cloud top temperature for clouds detected by IR threshold regardless of VIS threshold";
    String long_name "Mean IR-cloud temperature";
    String standard_name "air_temperature_at_cloud_top";
    String units "K";
  }
  sigma_tc_space {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation time: mean";
    String coordinates "time_run time lat lon";
    String description "Time average of spatial standard deviations of cloud top temperature";
    String long_name "cloud-top temperature (TC) IR-cloud mean standard deviation over space";
    String units "K";
  }
  tau_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Average (radiatively-weighted) cloud visible optical thickness for clouds detected by IR threshold regardless of VIS threshold";
    String long_name "Mean IR-cloud cloud optical depth (TAU)";
    String standard_name "atmosphere_optical_thickness_due_to_cloud";
    String units "1";
  }
  sigma_tau_space {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation time: mean";
    String coordinates "time_run time lat lon";
    String description "Time average of spatial standard deviations of cloud visible optical thickness";
    String long_name "cloud optical depth (TAU) IR-cloud mean standard deviation over space";
    String units "1";
  }
  wp_ir {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Average cloud water path (linear average of optical thickness times particle radius) for clouds detected by IR threshold regardless of VIS threshold";
    String long_name "Mean IR-cloud water path";
    String units "cm";
  }
  sigma_wp_space {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: standard_deviation time: mean";
    String coordinates "time_run time lat lon";
    String description "Time average of spatial standard deviations of cloud water path";
    String long_name "cloud water path (WP) IR-cloud mean standard deviation over space";
    String units "cm";
  }
  cldamt_irmarg {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean time: mean within days time: mean over days";
    String coordinates "time_run time lat lon";
    String description "Average cloud amount for clouds marginally detected by IR threshold regardless of VIS threshold";
    String long_name "Cloud amount uncertainty (using IR data)";
    String note "This is the ISCCP variable: cldamt_irmarg. 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";
  }
  snoice {
    Int32 _ChunkSizes 5, 180, 360;
    String cell_methods "area: mean";
    String coordinates "time_run time lat lon";
    String description "Mean snow/ice cover for the cell";
    String long_name "Mean snow/ice amount";
    String units "percent";
  }
  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.";
    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 "2019-07-17T19:08:49Z";
    String date_issued "2019-07-17T19:08:49Z";
    String date_metadata_modified "2019-07-17T19:08:49Z";
    String date_modified "2019-07-17T19:08:49Z";
    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 
"Wed Jul 17 15:08:50 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,2019-07-17T19:08:49Z -a date_created,global,m,c,2019-07-17T19:08:49Z -a date_modified,global,m,c,2019-07-17T19:08:49Z -a date_metadata_modified,global,m,c,2019-07-17T19:08:49Z -a long_name,cldbin_bounds,c,c,Boundaries of the cloud fractional amounts -a description,cldbin_bounds,c,c,The frequency of occurrence of this amount of cloud cover is provided in cldamt_dist -a units,cldbin_bounds,c,c,percent -a cell_methods,eqheight,c,c,area: mean -a cell_methods,snoice,c,c,area: mean -a cell_methods,cldamt,c,c,area: mean time: mean within days time: mean over days -a cell_methods,cldamt_ir,c,c,area: mean time: mean within days time: mean over days -a long_name,cldamt_irmarg,m,c,Cloud amount uncertainty (using IR data) -a cell_methods,cldamt_irmarg,c,c,area: mean time: mean within days time: mean over days -a note,cldamt_irmarg,c,c,This is the ISCCP variable: cldamt_irmarg. 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 cell_methods,cldamt_types,c,c,area: mean time: mean within days time: mean over days -a cell_methods,_time$,c,c,area: mean time: standard_deviation -a cell_methods,_space$,c,c,area: standard_deviation time: mean -a cell_methods,^pc,c,c,area: mean time: mean within days time: mean over days -a cell_methods,^tc,c,c,area: mean time: mean within days time: mean over days -a cell_methods,^tau,c,c,area: mean time: mean within days time: mean over days -a cell_methods,^wp,c,c,area: mean time: mean within days time: mean over days /glfs2/isccp-p/basic/intermediate//temp_file2.nc -O /glfs2/isccp-p/basic/intermediate//temp_file3.nc
Wed Jul 17 15:08:48 2019: ncks --no-abc -4 -L 5 /glfs2/isccp-p/basic/intermediate//temp_file.nc -O /glfs2/isccp-p/basic/intermediate//temp_file2.nc
2019-05-14T20:52:46.000Z bhankins d2prodc /glfs2/isccp-p/prd/wrkdirs/2017_05 2017 05 ;
FMRC Best Dataset
2024-06-19T12:08:32Z https://www.ncei.noaa.gov/thredds/dodsC/cdr/isccp_hgm_agg/ISCCP-H_Aggregation_Basic_Gridded_Monthly_(HGM)_best.ncd
2024-06-19T12:08:32Z https://www.ncei.noaa.gov/erddap/griddap/iscpp_hgm_by_time_lat_lon.das";
    String id "ISCCP.HGM.0.GLOBAL.2017.05.99.9999.GPC.10KM.CS00.EQ1.00.nc";
    String infoUrl "https://www.ncei.noaa.gov/thredds/catalog/cdr/isccp_hgm_agg/catalog.html?dataset=cdr/isccp_hgm_agg/ISCCP-H_Aggregation_Basic_Gridded_Monthly_(HGM)_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_gmt 9999;
    String isccp_input_files "ISCCP.HGH.0.GLOBAL.2017.05.99.0000.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.0300.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.0600.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.0900.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.1200.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.1500.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGH.0.GLOBAL.2017.05.99.2100.GPC.10KM.CS00.EQ1.00.nc";
    Int32 isccp_month 5;
    Int32 isccp_number_of_satellites_contributing 7;
    Int32 isccp_percent_empty_cells 0;
    Int32 isccp_percent_full_cells 100;
    Int32 isccp_year 17;
    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 Frequency, 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";
    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_Monthly_(HGM)";
    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_hgm_agg/ISCCP-H_Aggregation_Basic_Gridded_Monthly_(HGM)_best.ncd";
    Float64 Southernmost_Northing -89.5;
    String summary "ISCCP H Gridded Monthly (HGM) Dimensioned By time, latitude, longitude.";
    String time_coverage_duration "P1M";
    String time_coverage_end "2017-06-30T21:00:00Z";
    String time_coverage_resolution "P1M";
    String time_coverage_start "1983-07-31T21:00:00Z";
    String title "ISCCP H Gridded Monthly (HGM) 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.


 
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