The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a monthly global merged land-ocean surface temperature analysis product that is derived from two independent analyses. The first is the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the second is a land surface air temperature (LSAT) analysis that uses the Global Historical Climatology Network - Monthly (GHCN-M) temperature database. The NOAAGlobalTemp data set contains global surface temperatures in gridded (5° × 5°) and monthly resolution time series (from 1850 to present time) data files. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. This version, v6.0, an updated version to the current operational release v5.1, is implemented by an Artificial Neural Network method to improve the surface temperature reconstruction over the land.

The Ocean Heat fluxes Climate Data Record (CDR) is one of three CDRs that make up the NOAA Ocean Surface Bundle (OSB). They can be used to describe essential aspects of the air-sea exchange. This CDR leverages the parameters of surface atmospheric properties and sea surface temperature to calculate the latent and sensible heat fluxes from a neural-network emulator of the TOGA-COARE Bulk Air-Sea Flux Algorithm. The final record is a 3-hourly 0.25-degree resolution grid over the global ice-free oceans from January 1988 to August 2021.

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24

ISCCP H Gridded Monthly (HGM) Dimensioned By satpos. _CoordSysBuilder=ucar.nc2.dataset.conv.CF1Convention acknowledgement=This project received funding support from NASA REASON PROGRAM, NASA MEASURES PROGRAM and NOAA CLIMATE DATA RECORD (CDR) PROGRAM cdm_data_type=Grid 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. contributor_name=William B. Rossow, Alison Walker, Violeta Golea, NOAA, EUMETSAT, ESA, JP/JMA, CHINA/CMA, BR/INPE, NASA contributor_role=principalInvestigator, processor, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider Conventions=CF-1.4, ACDD-1.3 date_metadata_modified=2019-07-17T19:08:49Z geospatial_bounds=POLYGON((-90.0 0.0, -90.0 360.0, 90.0 360.0, 90.0 0.0, -90.0 0.0)) geospatial_bounds_crs=EPSG:4326 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 id=ISCCP.HGM.0.GLOBAL.2017.05.99.9999.GPC.10KM.CS00.EQ1.00.nc 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 institution=International Cloud Climatology Project (ISCPP) instrument=Himawari-8 AHI, SEVIRI, GOES-15 Imager, GOES-13 Imager, SEVIRI,, AVHRR-3 instrument_vocabulary=NASA Global Change Master Directory (GCMD) Instruments Keywords Version 8.1 isccp_gmt=9999 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 isccp_month=5 isccp_number_of_satellites_contributing=7 isccp_percent_empty_cells=0 isccp_percent_full_cells=100 isccp_year=17 keywords_vocabulary=NASA Global Change Master Directory (GCMD) Science Keyword Version 8.1 location=Proto fmrc:ISCCP-H_Aggregation_Basic_Gridded_Monthly_(HGM) metadata_link=gov.noaa.ncdc.C00956 naming_authority=gov.noaa.ncdc NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) platform=HIM-8, METEOSAT-10, GOES-15, GOES-13, METEOSAT-8, NOAA-19, METOP-A platform_vocabulary=NASA Global Change Master Directory (GCMD) Platforms Keyword Version 8.1 processing_level=3 program=NOAA Climate Data Record Program for satellites, FY 2016 project=International Satellite Cloud Climatology Project (ISCCP) 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)0802261:AIUCFI2.0.CO;2','ISCCP CDR Climate Algorithm Theoretical Basis Document (C-ATBD)' 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 sourceUrl=https://www.ncei.noaa.gov/thredds/dodsC/cdr/isccp_hgm_agg/ISCCP-H_Aggregation_Basic_Gridded_Monthly_(HGM)_best.ncd time_coverage_duration=P1M time_coverage_resolution=P1M

Data in the Classroom is an online curriculum to foster data literacy. This Investigating Coral Bleaching Using Data in the Classroom module is geared towards grades 6 - 12. Visit Data in the Classroom for more information.This application is the Investigating Coral Bleaching module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

The mission of the Ocean Climate Stations (OCS) Project is to make meteorological and oceanic measurements from autonomous platforms. Calibrated, quality-controlled, and well-documented climatological measurements are available on the OCS webpage and the OceanSITES Global Data Assembly Centers (GDACs), with near-realtime data available prior to release of the complete, downloaded datasets.

OCS measurements served through the Big Data Program come from OCS high-latitude moored buoys located in the Kuroshio Extension (32°N 145°E) and the Gulf of Alaska (50°N 145°W). Initiated in 2004 and 2007, the respective moored buoys, KEO and Papa, measure a suite of surface and subsurface essential ocean variables. The surface suite includes air temperature, relative humidity, shortwave and longwave radiation, barometric pressure, winds, and rain, while subsurface instrumentation includes temperature, salinity, and ocean currents. Individual buoy deployments are stitched together into a continuous time-series, which is synced to the OceanSITES GDACs, and subsequently, to BDP.

ISCCP H Gridded By Hour (HGH) Dimensioned By time, latitude, longitude. _CoordSysBuilder=ucar.nc2.dataset.conv.CF1Convention acknowledgement=This project received funding support from NASA REASON PROGRAM, NASA MEASURES PROGRAM and NOAA CLIMATE DATA RECORD (CDR) PROGRAM cdm_data_type=Grid 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. contributor_name=William B. Rossow, Alison Walker, Violeta Golea, NOAA, EUMETSAT, ESA, JP/JMA, CHINA/CMA, BR/INPE, NASA contributor_role=principalInvestigator, processor, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider, resourceProvider Conventions=CF-1.4, ACDD-1.3 date_metadata_modified=2019-07-19T06:48:07Z Easternmost_Easting=359.5 geospatial_bounds=POLYGON((-90.0 0.0, -90.0 360.0, 90.0 360.0, 90.0 0.0, -90.0 0.0)) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=89.5 geospatial_lat_min=-89.5 geospatial_lat_resolution=1.0 geospatial_lat_units=degrees_north geospatial_lon_max=359.5 geospatial_lon_min=0.5 geospatial_lon_resolution=1.0 geospatial_lon_units=degrees_east history=Fri Jul 19 06:48:07 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,2019-07-19T06:48:07Z -a date_created,global,m,c,2019-07-19T06:48:07Z -a date_modified,global,m,c,2019-07-19T06:48:07Z -a date_metadata_modified,global,m,c,2019-07-19T06:48:07Z -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 -a cell_methods,^pc,c,c,area: mean time: mean within days -a cell_methods,^tc,c,c,area: mean time: mean within days -a cell_methods,^tau,c,c,area: mean time: mean within days -a cell_methods,^wp,c,c,area: mean time: mean within 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,cldamt_ir,c,c,area: mean time: mean within 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 -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_irtypes,c,c,area: mean time: mean within days -a cell_methods,cldamt_types,c,c,area: mean time: mean within days -a cell_methods,snoice,c,c,area: mean time: mean within days /glfs2/isccp-p/basic/intermediate//temp_file2.nc -O /glfs2/isccp-p/basic/intermediate//temp_file3.nc Fri Jul 19 06:48:06 2019: ncks --no-abc -4 -L 5 /glfs2/isccp-p/basic/intermediate//temp_file.nc -O /glfs2/isccp-p/basic/intermediate//temp_file2.nc 2019-05-14T19:54:07.000Z bhankins d2proda /glfs2/isccp-p/prd/wrkdirs/2017_06 2017 06 ; FMRC Best Dataset id=ISCCP.HGH.0.GLOBAL.2017.06.99.1800.GPC.10KM.CS00.EQ1.00.nc infoUrl=https://www.ncei.noaa.gov/thredds/catalog/cdr/isccp_hgh_agg/catalog.html?dataset=cdr/isccp_hgh_agg/ISCCP-H_Aggregation_Basic_Gridded_By_Hour_(HGH)_best.ncd institution=International Cloud Climatology Project (ISCPP) instrument=Himawari-8 AHI, SEVIRI, GOES-15 Imager, GOES-13 Imager, SEVIRI,, AVHRR-3 instrument_vocabulary=NASA Global Change Master Directory (GCMD) Instruments Keywords Version 8.1 isccp_gmt=18 isccp_input_files=ISCCP.HGG.0.GLOBAL.2017.06.01.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.02.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.03.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.04.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.05.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.06.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.07.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.08.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.09.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.10.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.11.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.12.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.13.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.14.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.15.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.16.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.17.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.18.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.19.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.20.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.21.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.22.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.23.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.24.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.25.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.26.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.27.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.28.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.29.1800.GPC.10KM.CS00.EQ1.00.nc ISCCP.HGG.0.GLOBAL.2017.06.30.1800.GPC.10KM.CS00.EQ1.00.nc isccp_month=6 isccp_number_of_satellites_contributing=7 isccp_percent_empty_cells=0 isccp_percent_full_cells=100 isccp_year=17 keywords_vocabulary=NASA Global Change Master Directory (GCMD) Science Keyword Version 8.1 location=Proto fmrc:ISCCP-H_Aggregation_Basic_Gridded_By_Hour_(HGH) metadata_link=gov.noaa.ncdc.C00956 naming_authority=gov.noaa.ncdc NCO=netCDF Operators version 4.7.5 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) Northernmost_Northing=89.5 platform=HIM-8, METEOSAT-10, GOES-15, GOES-13, METEOSAT-8, NOAA-19, METOP-A platform_vocabulary=NASA Global Change Master Directory (GCMD) Platforms Keyword Version 8.1 processing_level=3 program=NOAA Climate Data Record Program for satellites, FY 2016 project=International Satellite Cloud Climatology Project (ISCCP) 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)0802261:AIUCFI2.0.CO;2','ISCCP CDR Climate Algorithm Theoretical Basis Document (C-ATBD)' 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 sourceUrl=https://www.ncei.noaa.gov/thredds/dodsC/cdr/isccp_hgh_agg/ISCCP-H_Aggregation_Basic_Gridded_By_Hour_(HGH)_best.ncd Southernmost_Northing=-89.5 time_coverage_duration=P1M time_coverage_end=2017-06-30T21:00:00Z time_coverage_resolution=PT3H time_coverage_start=1983-07-31T00:00:00Z Westernmost_Easting=0.5

ANNOUNCEMENTS: [NOS OFS Version Updates and Implementation of Upgraded Oceanographic Forecast Modeling Systems for Lakes Superior and Ontario; Effective October 25, 2022}(https://www.weather.gov/media/notification/pdf2/scn22-91_nos_loofs_lsofs_v3.pdf)

For decades, mariners in the United States have depended on NOAA's Tide Tables for the best estimate of expected water levels. These tables provide accurate predictions of the astronomical tide (i.e., the change in water level due to the gravitational effects of the moon and sun and the rotation of the Earth); however, they cannot predict water-level changes due to wind, atmospheric pressure, and river flow, which are often significant.

The National Ocean Service (NOS) has the mission and mandate to provide guidance and information to support navigation and coastal needs. To support this mission, NOS has been developing and implementing hydrodynamic model-based Operational Forecast Systems.

This forecast guidance provides oceanographic information that helps mariners safely navigate their local waters. This national network of hydrodynamic models provides users with operational nowcast and forecast guidance (out to 48 – 120 hours) on parameters such as water levels, water temperature, salinity, and currents. These forecast systems are implemented in critical ports, harbors, estuaries, Great Lakes and coastal waters of the United States, and form a national backbone of real-time data, tidal predictions, data management and operational modeling.

Nowcasts and forecasts are scientific predictions about the present and future states of water levels (and possibly currents and other relevant oceanographic variables, such as salinity and temperature) in a coastal area. These predictions rely on either observed data or forecasts from a numerical model. A nowcast incorporates recent (and often near real-time) observed meteorological, oceanographic, and/or river flow rate data. A nowcast covers the period from the recent past (e.g., the past few days) to the present, and it can make predictions for locations where observational data are not available. A forecast incorporates meteorological, oceanographic, and/or river flow rate forecasts and makes predictions for times where observational data will not be available. A forecast is usually initiated by the results of a nowcast.

OFS generally runs four times per day (every 6 hours) on NOAA's Weather and Climate Operational Supercomputing Systems (WCOSS) in a standard Coastal Ocean Modeling Framework (COMF) developed by the Center for Operational Oceanographic Products and Services (CO-OPS). COMF is a set of standards and tools for developing and maintaining NOS’s hydrodynamic model–based operational forecast systems. The goal of COMF is to provide a standard and comprehensive software infrastructure to enhance ease of use, performance, portability, and interoperability of NOS’s operational forecast systems.

These quality monitoring data for Pathfinder Version 5.2 (PFV5.2) Sea Surface Temperature (SST) are based on the concept of a Rich Inventory developed by the Enterprise Data Systems Group at the National Geophysical Data Center (NGDC). The principle concept of a Rich Inventory is to calculate statistics for selected parameters in each data file, store them in a database, and make them available to users and managers of the archive. In PFV5.2, one data file is generated every 12 hours, i.e., daily and nightly. The following statistics are calculated in this quality monitoring accession: valid observation number, number of the observations with value over 3 times the standard deviation, the observed minimum, maximum, mean and median of each image. The above statistics are calculated for several different domains, e. g., global, tropics, middle and latitudes, and also for 22.5x22.5 (lon/lat) degree "chunks". Visualizations of the quality monitoring data are accessible in NODC's Live Access Sever (LAS) at: https://data.nodc.noaa.gov/las. Select Datasets: Pathfinder 5.2 RI statistics.

Last Revised: February 2016 Map InformationThis nowCOAST™ time-enabled map service provides maps depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is a method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in; however, all cloud cover values are presently displayed using the "Missing" symbol due to a problem with the source data. Present weather information is also not available for display at this time. Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs, which indicate wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds.Due to software limitations, the observations included in this map service are organized into three separate group layers: 1) Wind velocity (wind barb) observations, 2) Cloud Cover observations, and 3) All other observations, which are displayed as numerical values (e.g. Air Temperature, Wind Gust, Visibility, Sea Surface Temperature, etc.).Additionally, due to the density of weather/ocean observations in this map service, each of these group data layers has been split into ten individual "Scale Band" layers, with each one visible for a certain range of map scales. Thus, to ensure observations are displayed at any scale, users should make sure to always specify all ten corresponding scale band layers in every map request. This will result in the scale band most appropriate for your present zoom level being shown, resulting in a clean, uncluttered display. As you zoom in, additional observations will appear.The observations in this nowCOAST™ map service are updated approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observations for a particular station may update only once per hour. For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.Background InformationThe maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing stations from the U.S.A. and other countries. For terrestrial networks, the platforms include but are not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Real-Time System (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until approximately 23 minutes past top of the hour for land-based stations and 33 minutes past the top of the hour for maritime stations.Time InformationThis map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:Issue a returnUpdates=true request (ArcGIS REST protocol only) for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of the REST Service page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes referred to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST™ LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST™ LayerInfo Help DocumentationReferencesNWS, 2013: Sample Station Plot, NWS/NCEP/WPC, College Park, MD (Available at http://www.wpc.ncep.noaa.gov/html/stationplot.shtml).NWS, 2013: Terminology and Weather Symbols, NWS/NCEP/OPC, College Park, MD (Available at http://www.opc.ncep.noaa.gov/product_description/keyterm.shtml).NWS, 2013: How to read Surface weather maps, JetStream an Online School for Weather (Available at http://www.srh.noaa.gov/jetstream/synoptic/wxmaps.htm).

The Global Precipitation Measurement (GPM) satellite was launched on February 27th, 2014 with the GPM Microwave Imager (GMI) instrument on board. The GPM mission is a joint effort between NASA, the Japan Aerospace Exploration Agency (JAXA) and other international partners. In march 2005, NASA has chosen the Ball Aerospace and Technologies Corp., Boulder, Colorado to build the GMI instrument on the continued success of the Tropical Rainfall Measuring Mission (TRMM) satellite by expanding current coverage of precipitation from the tropics to the entire world. GMI is a dual-polarization, multi-channel, conical-scanning, passive microwave radiometer with frequent revisit times. One of the primary differences between GPM and other satellites with microwave radiometers is the orbit, which is inclined 65 degrees, allowing a full sampling of all local Earth times repeated approximately every 2 weeks. The GPM platform undergoes yaw maneuvers approximately every 40 days to compensate for the sun's changing position and prevent the side of the spacecraft facing the sun from overheating. Today, the GMI instrument plays an essential role in the worldwide measurement of precipitation and environmental forecasting. Sea Surface Temperature (SST) is one of its major products. The GMI data from the Remote Sensing System (REMSS) have been produced using an updated RTM, Version-8. The V8 brightness temperatures from GMI are slightly different from the V7 brightness temperatures; The SST datasets are available in near-real time (NRT) as they arrive, with a delay of about 3 to 6 hours, including the Daily, 3-Day, Weekly, and Monthly time series products.

The Airborne eXpendable BathyThermograph (AXBT) measures ocean temperature as a function of depth. As part of the Hurricane Research Division's hurricane field program, AXBT data is collected to 1) assess the upper ocean's heat content available to approaching hurricanes, and 2) make quantitative estimates of upper ocean rates of mixing and cooling, and heat fluxes in the storm environment. During RAINEX, 18 AXBTs were dropped in post-Katrina warm eddy on 9/15 from the NOAA N43RF P-3. AXBT's were also dropped on 9/22 and 9/23, 2005.

This record contains the MOODS/MBT/Navoceano data. In 1987 the NAVY MOODS archive was being prepared for transfer to NAVOCEANO. Roger Bauer of Compass Systems and designer of the MOODS system turned over to NODC 2 digital files and approximately 4 boxes of computer listings and plots that identify MBT stations that should be deleted from our archives and stations that need TSP and DSP adjustments. The digital files reside on optical as Accession 8700387. The deletions (8700387.001) were performed. However the adjustments were never completed.

After numerous conversations with Roger Bauer regarding the erroneous MBT calibration adjustments in our MBT archive, I requested from Navoceno a copy of all of their MBTs to replace the NODC's MBT archive. This request was made in Aug. 1992 and fulfilled with the shipment of 5 magnetic tapes. At the time Navoceano took over the MOODS data sets and the Navy archiving. The MOODS MBT archive had all of the corrections applied. Again nothing was done with these replacement tapes. Primarily because they lacked NODC identifying information that would enable an easy match with the observations in our archive. However, in 1994? NODC's decision to build a profile database (NODB) and the need for profile data by the Ocean Climate Lab (OCL) seemed to have solved the problem. All profile data went through the OCL quality control activities before being loaded into the NODB. These quality control functions included adjustments to the MBT profile for calibration depths and temps. Thus the MBT profiles in the NODB are NOT the same as those from the DAMUS archive residing on optical disk. Status of materials: The boxes of computer listings and plots are no longer at NODC. The five tapes from NAVOCEANO were trashed before the move to SSMC3.

It should be borne in mind tha author of this note is unknown as of April 10, 2003. Format of this note was edited by DWC on April 10, 2003.

The International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Near-Real-Time (NRT) product is an extension of the official ICOADS data set based on preliminary Global Telecommunication System (GTS) data. The data are considered 'preliminary' until fully incorporated into a full major ICOADS data set release. The NRT data set is a merged product of various marine GTS data sources providing surface marine in situ observations from different observing platforms. Observations in the NRT product are primarily from ships (merchant, research, fishing, navy, etc.), moored and drifting buoys, and coastal stations. Each report contains individual observations of meteorological and oceanographic variables, such as sea surface and air temperatures, winds, pressure, humidity, wet bulb, dew point, ocean waves and cloudiness. The data are provided in the ICOADS common International Maritime Meteorological Archive (IMMA) data format and NetCDF. Useful metadata have been added in the global and variable attributes of each file to make the netCDF self-contained. R3.0 includes changes designed to enable more effective exchange of information describing data quality between ICOADS, reanalysis centers, data set developers, scientists and the public. These user-driven innovations include the assignment of a unique identifier (UID) to each marine report, to enable tracing of observations, linking with reports and improved data sharing. Many new input data and metadata sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. For NRT creation the data are first quality controlled and blended together, with duplicates (Intermediate product) retained. The duplicates are then compared and removed for the Final product and a statistical output summary file providing justifications for duplicate deletions that occurred during the blending process. User groups include sea surface temperature analyses, global surface temperature analyses, model/reanalysis input, and general ocean research. The data set is produced and archived monthly. Spatial coverage is global and the current period of record for these files begins in January 2008 to the most current complete month.

This data set contains observations from the NOAA Air Resources Laboratory, Atmospheric Turbulence and Diffusion Division (NOAA/ARL/ATDD) DJI S-1000 small Unmanned Aerial System (sUAS) and the Microdrone MD4-1000 sUAS flights conducted around Cullman, Alabama during the VORTEX-SE 2017 field campaign. Both aircraft were instrumented to make measurements of air temperature, relative humidity, and atmospheric pressure while in flight. The DJI S-1000 was also equipped to measure the Earth's surface temperature while in flight. The DJI S-1000 conducted flights on 25 and 27 March and 5 and 28 April 2017. The MD4-1000 flights were all conducted on 28 April 2017.

NODC Accession 0104427 includes underway - surface, meteorological, physical, chemical and optical data collected aboard the ENDEAVOR and OCEANUS during cruises EN319, EN320, EN321, EN322, EN323, EN324, EN325, EN330, EN331, OC336, OC338, OC339, OC340, OC341, OC342, OC343, OC344, OC345, OC346 and OC347 in the North Atlantic Ocean from 1999-02-11 to 1999-12-14. These data include CONDUCTIVITY, WIND DIRECTION, SHORTWAVE IRRADIANCE, AIR TEMPERATURE, WIND SPEED, LONGWAVE IRRADIANCE, RELATIVE HUMIDITY, SALINITY - SURFACE WATER, BAROMETRIC PRESSURE, PRECIPITATION and SEA SURFACE TEMPERATURE. The instruments used to collect these data include thermosalinographs. These data were collected by Richard Payne of Woods Hole Oceanographic Institution as part of GB. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) submitted these data to NODC on 2013-04-13.

The following is the text of the abstract provided by BCO-DMO:

Continuous along track meteorology and sea surface data, 15 minute averaged values, 1999

Processed by: Richard Payne Woods Hole Oceanographic Institution Woods Hole, MA 20543 rpayne@whoi.edu (mailto:rpayne@whoi.edu)

Additional data processing notes (http://globec.whoi.edu/globec-dir/data_doc/additional_endeavor_processing_details.html) are available.

The sea surface temperature as measured by the hull sensor is not shown since the sea surface temperature as measured via the engine inlet (field name is temp_ss1) is more accurate. Processing Notes

Concatenate daily 1 minute files into one file for whole cruise Edit file for obvious bad data, i.e., missing data, garbage characters, etc. Run program which reformats data. Output parameters: Year day, lat, long, Speed made good, course made good, gyro 1 & 2, Edo speed, Edo indicator, port wind speed, starboard ws, port wind azimuth, starboard waz, air temp, relative humidity, barometric pressure, sea surface temp @5m & 1m depth, Edo depth, Chirp sonar depth. Put plots of all parameters on screen and look for obvious single bad points. Edit in basic concatenated file. Except I have not edited depths. Iterate steps 2-4 until no more obvious bad points. Run second program which computes true wind speed and direction from speed and course made good, gyros, larger of port or starboard ws and accompanying wind azimuth. Outputs are year day, lat lon, speed and course made good, gyro, relative ws and direction, true ws and direction, air temp, relative humidity, barometric pressure, short- and long-wave radiation,5m and 1m sea surface temps, Edo depth, Chirp sonar depth, Edo speed, Edo indicator. Check plots of true wind speed and direction to make sure they look ok. Run vector averaging program which produces 60 minute series. The program uses 60 consecutive records and does not check for missing records. I have not carried depths since hourly averages do not seem useful nor Edo speeds since they seem pretty generally useless. Output parameters are: Year day, lat, long, true wind speed and direction, air temp, relative humidity, barometric pressure, short- and long-wave radiation, sea surface temp @ 5m & 1m.

From: Richard E. Payne, May 28, 1999 Updated: April 29, 2004; G.Heimerdinger

The NASA/GEWEX Surface Radiation Budget (SRB) Release-3.0 data sets contains global 3-hourly, daily, monthly/3-hourly, and monthly averages of surface and top-of atmosphere (TOA) longwave and shortwave radiative parameters on a 1?x1? grid. Model inputs of cloud amounts and other atmospheric state parameters are also available in some of the data sets. Primary inputs to the models include: visible and infrared radiances from International Satellite Cloud Climatology Project (ISCCP) pixel-level (DX) data, cloud and surface properties derived from those data, temperature and moisture profiles from GEOS-4 reanalysis product obtained from the NASA Global Modeling and Assimilation Office (GMAO), and column ozone amounts constituted from Total Ozone Mapping Spectrometer (TOMS), TIROS Operational Vertical Sounder (TOVS) archives, and Stratospheric Monitoring-group's Ozone Blended Analysis (SMOBA), an assimilation product from NOAA's Climate Prediction Center. SRB products are reformatted for the use of renewable energy and agricultural communities and made available through the Surface meteorology and Solar Energy (SSE) website. SRB products now overlap a portion of surface and TOA flux data sets that are available from Clouds and the Earth's Radiant Energy System (CERES) project. These CERES data products and those from the CERES Fast Longwave and SHortwave Radiative Fluxes (FLASHFlux) project extend past the SRB time frame. The latter project provides radiative fluxes on a near real-time basis. The CERES and CERES/FLASHFlux data sets also make use of global observations from Moderate-resolution Imaging SpectroRadiometer (MODIS) instruments. Release-3.0 products differ substantially from earlier SRB Releases (2.0 and 2.5) arising from numerous improvements of the algorithms and input data sets. Temporal coverage of Release-3.0 is extended to December 2007; Release-2.5 ended in June 2005. A modified version of the GEWEX Longwave data set, denoted as version 3.1, corrects for a numerical instability issue that was found to affect a small number of 3 hourly grid box TOA outgoing and surface downward fluxes in the release 3.0 longwave products .On-line documentation provides information on all changes applicable to Release-3.0. Users are encouraged to consult on-line documentation prior to using these data sets. In addition to the big-endian binary formatted files of previous releases, Release-3.0 SW/3.1 LW are now available in netCDF format.

This dataset, the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), is the most widely-used freely available collection of surface marine observations, with over 455 million individual marine reports spanning 1662-2014-each containing the observations and metadata reported from a given ship, buoy, coastal platform, or oceanographic instrument, providing data for the construction of gridded analyses of sea surface temperature, estimates of air-sea interaction and other meteorological variables. ICOADS observations are assimilated into all major atmospheric, oceanic and coupled reanalyses, further widening its impact. R3, therefore includes changes designed to enable the effective exchange of information describing data quality between ICOADS, reanalysis centres, data set developers, scientists, and the public. These user-driven innovations include the assignment of a unique identifier (UID) to each marine report to enable tracing of observations, linking with reports and improved data sharing. Other revisions and extensions of the ICOADS' International Maritime Meteorological Archive (IMMA) common data format incorporate new near-surface oceanographic data elements and cloud parameters. Many new input data sources have been assembled, and updates and improvements to existing data sources, or removal of erroneous data, made. Additionally, these data are offered in NetCDF with useful metadata added in global and variable attributes of each file to make the NetCDF self-contained.

This dataset includes 2 versions of the official ICOADS Release 3 dataset: 1) the 'Total' product (denoted by 'T' in the filename) which contains all duplicates and is used for verification and research purposes; and 2) 'Final' R3, with duplicates removed, where all reports have been compared for matching dates, id's and elements observed and the best duplicate retained as the final report.

Data in the Classroom is an online curriculum to foster data literacy. This Investigating Coral Bleaching Using Data in the Classroom module is geared towards grades 6 - 12. Visit Data in the Classroom for more information.This application is the Investigating Coral Bleaching module.This module was developed to engage students in increasingly sophisticated modes of understanding and manipulation of data. It was completed prior to the release of the Next Generation Science Standards (NGSS)* and has recently been adapted to incorporate some of the innovations described in the NGSS.Each level of the module provides learning experiences that engage students in the three dimensions of the NGSS Framework while building towards competency in targeted performance expectations. Note: this document identifies the specific practice, core idea and concept directly associated with a performance expectation (shown in parentheses in the tables) but also includes additional practices and concepts that can help students build toward a standard.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

The QuOTA project involved NOAA-IPRC and CMAR jointly undertaking to build a very high quality ocean thermal data archive by applying methods and expertise developed through the NOAA-IPRC/CMAR IOTA …Show full descriptionThe QuOTA project involved NOAA-IPRC and CMAR jointly undertaking to build a very high quality ocean thermal data archive by applying methods and expertise developed through the NOAA-IPRC/CMAR IOTA (Indian Ocean Thermal Archive) collaboration which was established in 1998. The Quota Project resulted in building a high quality upper ocean temperature dataset for the Indian Ocean and the South-western Pacific (east of the dateline). QuOTA contains ocean temperature data collected since 1778 and includes XBT, CT, CU, CTD, XCDT, MBT, BT, BA, DT, SST, TE, UO, bottle, drifting and moored bouy data. Quality control of the data is done by automated processes, followed by 'hand-QC' of data that fails the automated test. This results in a data set containing very little 'bad' data and any that remains is usually subtly faulty, having little impact on most analyses.