dpt.2m.mon.ltm (Monthly NARR) [time][y][x], 0001

Monthly NARR (dpt.2m.mon.ltm). National Centers for Environmental Prediction data from https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Derived/monolevel/dpt.2m.mon.ltm.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
dpt (Long Term Monthly Mean Dew Point Temperature at 2m, K)


Monthly NARR (dpt.2m.mon.ltm). National Centers for Environmental Prediction data from https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Derived/monolevel/dpt.2m.mon.ltm.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [y][x]):
lat (latitude coordinate, degrees_north)
lon (longitude coordinate, degrees_east)


dswrf.mon.ltm (Monthly NARR) [time][y][x], 0001

Monthly NARR (dswrf.mon.ltm). National Centers for Environmental Prediction data from https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Derived/monolevel/dswrf.mon.ltm.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
dswrf (Long Term Monthly Mean Downward Shortwave Radiation Flux at Surface, W/m^2)


Monthly NARR (dswrf.mon.ltm). National Centers for Environmental Prediction data from https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Derived/monolevel/dswrf.mon.ltm.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [y][x]):
lat (latitude coordinate, degrees_north)
lon (longitude coordinate, degrees_east)


dswrf.sfc.mon.1981-2010.ltm (NCEP-NCAR Reanalysis 1), 0001

National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) Reanalysis 1. Data is from NCEP initialized reanalysis
(4x/day).  It consists of T42  variables interpolated to
pressure surfaces from model (sigma) surfaces.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
dswrf (Monthly Long Term Mean Downward Solar Radiation Flux at surface, W/m^2)
valid_yr_count (count of non-missing values used in mean)


Dutch Harbor, Alaska Coastal Digital Elevation Model (dutch harbor 1 mhw 2006), 2.7777778E-4°

Dutch Harbor, Alaska Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/294; Dutch Harbor, AK 1 arc-second Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


easter island 3 isl 2016 (GDAL Band Number 1), 8.3333335E-4°

GDAL Band Number 1. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1)


eastern canada 3 isl 2015 (eastern_canada_3as_msl), 8.3333335E-4°

eastern_canada_3as_msl. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
z (meters)


Elfin Cove Alaska Coastal Digital Elevation Model (elfin cove 13 mhhw 2012), 9.259E-5°

Elfin Cove Alaska Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator


Elfin Cove Alaska Coastal Digital Elevation Model (elfin cove 13 mhw 2011), 9.259259E-5°


Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model (ElfinCoveAK A)

Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model. Pacific Marine Environmental Laboratory (PMEL) Forecast Model Grids provide bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite of numerical simulation codes capable of simulating three processes of tsunami evolution: generation, transoceanic propagation, and inundation of dry land. Tsunami waves are computationally propagated across a set of three nested grids (A, B, and C), each of which is successively finer in resolution, moving from offshore to onshore. Nearshore details are resolved to the point that model output can be directly compared with tide gauge observations and can provide estimates of wave arrival time, wave amplitude and simulation of wave inundation onto dry land.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
bathy (Grid Bathymetry, meters)

NOAA OAR PMEL Center for Tsunami Research


Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model (ElfinCoveAK B)


Elfin Cove, Alaska Tsunami Forecast Grids for MOST Model (ElfinCoveAK C)


ESA CCI Ocean Colour Product (CCI ALL-v5.0-5DAY), 0.04166666°, 1997-present

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are 5 day composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products.  MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007.  VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30.  VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer.  The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP.  Derived products were generally computed with the standard algorithmsthrough SeaDAS.  QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting.  The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships.  Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Rrs_412 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_443 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
Rrs_490 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
Rrs_510 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 510 nm., sr-1)
Rrs_560 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 560 nm., sr-1)
Rrs_665 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 665 nm., sr-1)
water_class1 (Mean of normalised water class 1 membership over the compositing period)
water_class2 (Mean of normalised water class 2 membership over the compositing period)
water_class3 (Mean of normalised water class 3 membership over the compositing period)
... (83 more variables)


ESA CCI Ocean Colour Product (CCI ALL-v5.0-5DAY), 0.04166666°, 1997-present, Lon0360


ESA CCI Ocean Colour Product (CCI ALL-v5.0-8DAY), 0.04166666°, 1997-present

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are 8 day composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products.  MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007.  VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30.  VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer.  The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP.  Derived products were generally computed with the standard algorithmsthrough SeaDAS.  QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting.  The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships.  Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Rrs_412 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_443 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
Rrs_490 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
Rrs_510 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 510 nm., sr-1)
Rrs_560 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 560 nm., sr-1)
Rrs_665 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 665 nm., sr-1)
water_class1 (Mean of normalised water class 1 membership over the compositing period)
water_class2 (Mean of normalised water class 2 membership over the compositing period)
water_class3 (Mean of normalised water class 3 membership over the compositing period)
... (83 more variables)


ESA CCI Ocean Colour Product (CCI ALL-v5.0-8DAY), 0.04166666°, 1997-present, Lon0360


ESA CCI Ocean Colour Product (CCI ALL-v5.0-DAILY), 0.04166666°, 1997-present

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are daily composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products.  MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007.  VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30.  VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer.  The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP.  Derived products were generally computed with the standard algorithmsthrough SeaDAS.  QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting.  The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships.  Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
MODISA_nobs (Count of the number of observations from the MODIS (Aqua) sensor contributing to this bin cell)
VIIRS_nobs (Count of the number of observations from the VIIRS sensor contributing to this bin cell)
OLCI_nobs (Count of the number of observations from the OLCI sensor contributing to this bin cell)
MERIS_nobs (Count of the number of observations from the MERIS sensor contributing to this bin cell)
SeaWiFS_nobs (Count of the number of observations from the SeaWiFS (GAC and LAC) sensor contributing to this bin cell)
total_nobs (Count of the total number of observations contributing to this bin cell)
Rrs_412 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_443 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
Rrs_490 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
... (83 more variables)


ESA CCI Ocean Colour Product (CCI ALL-v5.0-DAILY), 0.04166666°, 1997-present, Lon0360


ESA CCI Ocean Colour Product (CCI ALL-v5.0-MONTHLY), 0.04166666°, 1997-present

Data products generated by the Ocean Colour component of the European Space Agency Climate Change Initiative project. These files are monthly composites of merged sensor (MERIS, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Sea-Wide Field-of-View Sensor (SeaWiFS) Local Area Coverage (LAC) & Global Area Coverage (GAC), Visible and Infrared Imager/Radiometer Suite (VIIRS), OLCI) products.  MODIS Aqua and SeaWiFS were band-shifted and bias-corrected to MERIS bands and values using a temporally and spatially varying scheme based on the overlap years of 2003-2007.  VIIRS was band-shifted and bias-corrected in a second stage against the MODIS Rrs that had already been corrected to MERIS levels, for the overlap period 2012-2013; and at the third stage OLCI was bias corrected against already corrected MODIS, for overlap period 2016-07-01 to 2019-06-30.  VIIRS, MODIS, SeaWiFS and MERIS Rrs were derived from a combination of NASA's l2gen (for basic sensor geometry corrections, etc) and HYGEOS Polymer v4.12 (for atmospheric correction). OLCI Rrs were sourced at L1b (already geometrically corrected) and processed with polymer.  The Rrs were binned to a sinusoidal 4km level-3 grid, and later to 4km geographic projection, by Brockmann Consult's SNAP.  Derived products were generally computed with the standard algorithmsthrough SeaDAS.  QAA IOPs were derived using the standard SeaDAS algorithm but with a modified backscattering table to match that used in the bandshifting.  The final chlorophyll is a combination of OCI, OCI2, OC2 and OCx, depending on the water class memberships.  Uncertainty estimates were added using the fuzzy water classifier and uncertainty estimation algorithm of Tim Moore as documented in Jackson et al (2017). and updated accorsing to Jackson et al. (in prep).

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Rrs_412 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_443 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
Rrs_490 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
Rrs_510 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 510 nm., sr-1)
Rrs_560 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 560 nm., sr-1)
Rrs_665 (Sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 665 nm., sr-1)
water_class1 (Mean of normalised water class 1 membership over the compositing period)
water_class2 (Mean of normalised water class 2 membership over the compositing period)
water_class3 (Mean of normalised water class 3 membership over the compositing period)
... (83 more variables)


ESA CCI Ocean Colour Product (CCI ALL-v5.0-MONTHLY), 0.04166666°, 1997-present, Lon0360


Estimated Seafloor Depth Gradients: ETOPO (US West Coast)

Estimated sea_floor depth  x-gradient, y-gradient and gradient magnitude using the Python scikit-image canny algorithm with sigma = 7.5, x-gradient, y-gradient and gradient magnitude. For the code used to create this dataset (and a description), see https://rmendels.github.io/seafloor_gradient_doc.html .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
magnitude_gradient (Magnitude of Sea Floor Depth Gradient, m)
x_gradient (East-West Gradient of Sea Floor Depth, m)
y_gradient (North-South Gradient of Sea Floor Depth, m)
sea_floor_depth (m)


Estimated Seafloor Depth Gradients: srtm30plus (US West Coast)

Estimated sea_floor depth  x-gradient, y-gradient and gradient magnitude using the Python scikit-image canny algorithm with sigma = 12.5, x-gradient, y-gradient and gradient magnitude. For the code used to create this dataset (and a description), see https://rmendels.github.io/seafloor_gradient_doc.html .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
magnitude_gradient (Magnitude of Sea Floor Depth Gradient, m)
x_gradient (East-West Gradient of Sea Floor Depth, m)
y_gradient (North-South Gradient of Sea Floor Depth, m)
sea_floor_depth (m)


ETOPO Global Topography and Bathymetry. ETOPO1 Bed g gmt4 (ETOPO1_Bed_g.int), 0.016666668°

ETOPO1_Bed_g.int. ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. It was built from numerous global and regional data sets, and is available in "Ice Surface" (top of Antarctic and Greenland ice sheets) and "Bedrock" (base of the ice sheets) versions.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
z (meters)


ETOPO Global Topography and Bathymetry. ETOPO1 Ice g gmt4 (ETOPO1_Ice_g.int), 0.016666668°

ETOPO1_Ice_g.int. ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. It was built from numerous global and regional data sets, and is available in "Ice Surface" (top of Antarctic and Greenland ice sheets) and "Bedrock" (base of the ice sheets) versions.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
z (meters)


ETOPO1 is a 1 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. It was built from numerous global and regional data sets, and is available in "Ice Surface" (top of Antarctic and Greenland ice sheets) and "Bedrock" (base of the ice sheets) versions.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
z (meters)

NOAA National Geophysical Data Center (NGDC) Marine Geology and Geophysics (MGG) Division


ETOPO1 Global Surface Relief: Bedrock, Lon0360


ETOPO1 Global Surface Relief: Ice Surface


ETOPO1 Global Surface Relief: Ice Surface, Lon0360


ETOPO5 is a 5 arc-minute global relief model of Earth's surface that integrates land topography and ocean bathymetry. It was built from numerous global and regional data sets. This historical dataset has been deprecated in favor of the newer and higher-resolution ETOPO1 1 arc-minute global relief model.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
ROSE (Relief Of the Surface of the Earth, meters)

NOAA Marine Geology and Geophysics (MGG)


Eureka, California Coastal Digital Elevation Model (eureka 13 mhw 2009), 9.259259E-5°

Eureka, California Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/261; Eureka, CA 1/3 arc-second MHW Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Eureka, California Coastal Digital Elevation Model (eureka 13 navd88 2009), 9.259259E-5°

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/667; Eureka, CA 1/3 arc-second NAVD 88 Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Eureka, California Tsunami Forecast Grids for MOST Model (EurekaCA A), 0.02°

Eureka, California Tsunami Forecast Grids for MOST Model. Pacific Marine Environmental Laboratory (PMEL) Forecast Model Grids provide bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite of numerical simulation codes capable of simulating three processes of tsunami evolution: generation, transoceanic propagation, and inundation of dry land. Tsunami waves are computationally propagated across a set of three nested grids (A, B, and C), each of which is successively finer in resolution, moving from offshore to onshore. Nearshore details are resolved to the point that model output can be directly compared with tide gauge observations and can provide estimates of wave arrival time, wave amplitude and simulation of wave inundation onto dry land.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
bathy (Grid Bathymetry, meters)

NOAA OAR PMEL Center for Tsunami Research


Eureka, California Tsunami Forecast Grids for MOST Model (EurekaCA B), 0.005°


Eureka, California Tsunami Forecast Grids for MOST Model (EurekaCA C)


Experimental NRT Global composite from NPP VIIRS created from ACSPO_VIIRS_sst_sub1. Data provided by NOAA/NESDIS Center for Satellite Applications and Research), 0.0375°, 2020-present

Global Daily Sea Surface Temperature (SST). United States Department of Commerce (USDOC)/NOAA/OAR/AOML/Physical Oceanography Division (PHOD) data from https://cwcgom.aoml.noaa.gov/thredds/dodsC/VIIRS_SST/SST.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sst (NRT ACSPO VIIRS SST. Interim version generated by AOML, degree_C)


Experimental NRT Global Monthly Cumulative field from NPP VIIRS created from Daily NPP VIIRS. Data provided by NOAA/NESDIS Center for Satellite Applications and Research), 0.0375°, 2020-present

Global Monthly Cumulative chlor_a from Visible and Infrared Imager/Radiometer Suite (VIIRS)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
chlor_a (Chlorophyll Concentration, OC3 Algorithm, mg m^-3)


Experimental NRT Global Weekly Cumulative field from NPP VIIRS created from Daily NPP VIIRS. Data provided by NOAA/NESDIS Center for Satellite Applications and Research), 0.0375°, 2020-present

Global Weekly Cumulative chlor_a from Visible and Infrared Imager/Radiometer Suite (VIIRS)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
chlor_a (Chlorophyll Concentration, OC3 Algorithm, mg m^-3)


F15 SSM/I daily products (RSS SSMI F15), 0.25°, 2019

F15 SSM/I daily products. These Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products are produced as part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. Remote Sensing Systems generates SSM/I and SSMIS data products using a unified, physically based algorithm to simultaneously retrieve the products. This algorithm is a product of 20 years of refinements, improvements, and verifications.nThe daily files consist of over-ocean measurements (geophysical retrievals or parameters) mapped to a regular 0.25-degree grid complete with data gaps between orbits. Two maps exist for each parameter, one of ascending orbit segments and the other of descending orbit segments. Data on each of the segment maps are averaged where successive orbits cross and are overwritten at the seam or region where the last orbit of the day overlaps the first orbit of the day. Daily data files contain time maps consisting of the UTC observation time for each set of passes (ascending and descending).n Gridded data are organized according to observation date. All dates and times are Coordinated Universal Time (UTC), also known as Greenwich Mean Time (GMT), Zulu Time (Z), Universal Time (UT), and World Time.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Time_Ascending (Decimal Hour of the Day, Ascending Passes, hour)
WSPD_MF_Ascending (Surface Wind Speed, Medium Frequency, Ascending Passes, m s-1)
Water_Vapor_Ascending (Atmospheric Water Vapor, Ascending Passes, mm)
Cloud_LW_Ascending (Cloud Liquid Water, Ascending Passes, mm)
Rain_Rate_Ascending (Rain Rate, Ascending Passes, MM' 'Hr-1)
Time_Descending (Decimal Hour of the Day, Descending Passes, hour)
WSPD_MF_Descending (Surface Wind Speed, Medium Frequency, Descending Passes, m s-1)
Water_Vapor_Descending (Atmospheric Water Vapor, Descending Passes, mm)
Cloud_LW_Descending (Cloud Liquid Water, Descending Passes, mm)
Rain_Rate_Descending (Rain Rate, Descending Passes, MM' 'Hr-1)


F16 SSMIS daily products (RSS SSMIS F16), 0.25°, 2019-present

F16 SSMIS daily products. These Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products are produced as part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. Remote Sensing Systems generates SSM/I and SSMIS data products using a unified, physically based algorithm to simultaneously retrieve the products. This algorithm is a product of 20 years of refinements, improvements, and verifications.nThe daily files consist of over-ocean measurements (geophysical retrievals or parameters) mapped to a regular 0.25-degree grid complete with data gaps between orbits. Two maps exist for each parameter, one of ascending orbit segments and the other of descending orbit segments. Data on each of the segment maps are averaged where successive orbits cross and are overwritten at the seam or region where the last orbit of the day overlaps the first orbit of the day. Daily data files contain time maps consisting of the UTC observation time for each set of passes (ascending and descending).n Gridded data are organized according to observation date. All dates and times are Coordinated Universal Time (UTC), also known as Greenwich Mean Time (GMT), Zulu Time (Z), Universal Time (UT), and World Time.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Time_Ascending (Decimal Hour of the Day, Ascending Passes, hour)
WSPD_MF_Ascending (Surface Wind Speed, Medium Frequency, Ascending Passes, m s-1)
Water_Vapor_Ascending (Atmospheric Water Vapor, Ascending Passes, mm)
Cloud_LW_Ascending (Cloud Liquid Water, Ascending Passes, mm)
Rain_Rate_Ascending (Rain Rate, Ascending Passes, MM' 'Hr-1)
Time_Descending (Decimal Hour of the Day, Descending Passes, hour)
WSPD_MF_Descending (Surface Wind Speed, Medium Frequency, Descending Passes, m s-1)
Water_Vapor_Descending (Atmospheric Water Vapor, Descending Passes, mm)
Cloud_LW_Descending (Cloud Liquid Water, Descending Passes, mm)
Rain_Rate_Descending (Rain Rate, Descending Passes, MM' 'Hr-1)


F17 SSMIS daily products (RSS SSMIS F17), 0.25°, 2019-present

F17 SSMIS daily products. These Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products are produced as part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. Remote Sensing Systems generates SSM/I and SSMIS data products using a unified, physically based algorithm to simultaneously retrieve the products. This algorithm is a product of 20 years of refinements, improvements, and verifications.nThe daily files consist of over-ocean measurements (geophysical retrievals or parameters) mapped to a regular 0.25-degree grid complete with data gaps between orbits. Two maps exist for each parameter, one of ascending orbit segments and the other of descending orbit segments. Data on each of the segment maps are averaged where successive orbits cross and are overwritten at the seam or region where the last orbit of the day overlaps the first orbit of the day. Daily data files contain time maps consisting of the UTC observation time for each set of passes (ascending and descending).n Gridded data are organized according to observation date. All dates and times are Coordinated Universal Time (UTC), also known as Greenwich Mean Time (GMT), Zulu Time (Z), Universal Time (UT), and World Time.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Time_Ascending (Decimal Hour of the Day, Ascending Passes, hour)
WSPD_MF_Ascending (Surface Wind Speed, Medium Frequency, Ascending Passes, m s-1)
Water_Vapor_Ascending (Atmospheric Water Vapor, Ascending Passes, mm)
Cloud_LW_Ascending (Cloud Liquid Water, Ascending Passes, mm)
Rain_Rate_Ascending (Rain Rate, Ascending Passes, MM' 'Hr-1)
Time_Descending (Decimal Hour of the Day, Descending Passes, hour)
WSPD_MF_Descending (Surface Wind Speed, Medium Frequency, Descending Passes, m s-1)
Water_Vapor_Descending (Atmospheric Water Vapor, Descending Passes, mm)
Cloud_LW_Descending (Cloud Liquid Water, Descending Passes, mm)
Rain_Rate_Descending (Rain Rate, Descending Passes, MM' 'Hr-1)


F18 SSMIS daily products (RSS SSMIS F18), 0.25°, 2019-present

F18 SSMIS daily products. These Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) data products are produced as part of the NASA's Making Earth System Data Records for Use in Research Environments (MEaSUREs) Program. Remote Sensing Systems generates SSM/I and SSMIS data products using a unified, physically based algorithm to simultaneously retrieve the products. This algorithm is a product of 20 years of refinements, improvements, and verifications.nThe daily files consist of over-ocean measurements (geophysical retrievals or parameters) mapped to a regular 0.25-degree grid complete with data gaps between orbits. Two maps exist for each parameter, one of ascending orbit segments and the other of descending orbit segments. Data on each of the segment maps are averaged where successive orbits cross and are overwritten at the seam or region where the last orbit of the day overlaps the first orbit of the day. Daily data files contain time maps consisting of the UTC observation time for each set of passes (ascending and descending).n Gridded data are organized according to observation date. All dates and times are Coordinated Universal Time (UTC), also known as Greenwich Mean Time (GMT), Zulu Time (Z), Universal Time (UT), and World Time.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Time_Ascending (Decimal Hour of the Day, Ascending Passes, hour)
WSPD_MF_Ascending (Surface Wind Speed, Medium Frequency, Ascending Passes, m s-1)
Water_Vapor_Ascending (Atmospheric Water Vapor, Ascending Passes, mm)
Cloud_LW_Ascending (Cloud Liquid Water, Ascending Passes, mm)
Rain_Rate_Ascending (Rain Rate, Ascending Passes, MM' 'Hr-1)
Time_Descending (Decimal Hour of the Day, Descending Passes, hour)
WSPD_MF_Descending (Surface Wind Speed, Medium Frequency, Descending Passes, m s-1)
Water_Vapor_Descending (Atmospheric Water Vapor, Descending Passes, mm)
Cloud_LW_Descending (Cloud Liquid Water, Descending Passes, mm)
Rain_Rate_Descending (Rain Rate, Descending Passes, MM' 'Hr-1)


Fajardo, Puerto Rico Coastal Digital Elevation Model (fajardo 13 mhw 2007), 9.259259E-5°

Fajardo, Puerto Rico Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/689; Fajardo, PR 1/3 arc-second Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model (FajardoPR A)

Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model. Pacific Marine Environmental Laboratory (PMEL) Forecast Model Grids provide bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite of numerical simulation codes capable of simulating three processes of tsunami evolution: generation, transoceanic propagation, and inundation of dry land. Tsunami waves are computationally propagated across a set of three nested grids (A, B, and C), each of which is successively finer in resolution, moving from offshore to onshore. Nearshore details are resolved to the point that model output can be directly compared with tide gauge observations and can provide estimates of wave arrival time, wave amplitude and simulation of wave inundation onto dry land.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
bathy (Grid Bathymetry, meters)

NOAA OAR PMEL Center for Tsunami Research


Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model (FajardoPR B)


Fajardo, Puerto Rico Tsunami Forecast Grids for MOST Model (FajardoPR C), 8.333333E-4°


false pass 815 mhhw 2016 (GDAL Band Number 1), 1.4814814E-4°

GDAL Band Number 1. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1)


Florence, Oregon Tsunami Forecast Grids for MOST Model (FlorenceOR A), 0.02°

Florence, Oregon Tsunami Forecast Grids for MOST Model. Pacific Marine Environmental Laboratory (PMEL) Forecast Model Grids provide bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite of numerical simulation codes capable of simulating three processes of tsunami evolution: generation, transoceanic propagation, and inundation of dry land. Tsunami waves are computationally propagated across a set of three nested grids (A, B, and C), each of which is successively finer in resolution, moving from offshore to onshore. Nearshore details are resolved to the point that model output can be directly compared with tide gauge observations and can provide estimates of wave arrival time, wave amplitude and simulation of wave inundation onto dry land.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
bathy (Grid Bathymetry, meters)


Florence, Oregon Tsunami Forecast Grids for MOST Model (FlorenceOR B)


Florence, Oregon Tsunami Forecast Grids for MOST Model (FlorenceOR C)


Fluorescence Line Height, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (1 Day Composite)

This dataset has monthly composites of Level 3, Standard Mapped Image, 4km, chlorophyll fluorescence data from NASA's Aqua Spacecraft, which gives insight into the physiology of phytoplankton in the ocean. When phytoplankton are under stress, the rate at which they fluoresce can be decoupled from the rate of photosynthetic productivity. Measurements are gathered by the Moderate Resolution Imaging Spectroradiometer (MODIS) carried aboard the spacecraft. This is Science Quality data.  This is the August 2015 version of this dataset.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
cflh (Normalized Fluorescence Line Height, W m-2 um-1 sr-1)


Fluorescence Line Height, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (1 Day Composite), Lon0360


Fluorescence Line Height, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (8 Day Composite)


Fluorescence Line Height, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (8 Day Composite), Lon0360


Fluorescence Line Height, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (Monthly Composite)


Fluorescence Line Height, Aqua MODIS, NPP, L3SMI, Global, 4km, Science Quality, 2003-present (Monthly Composite), Lon0360


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (1 Day Composite)

MODIS measures chlorophyll fluorescence, which gives insight into the physiology of phytoplankton in the ocean. When phytoplankton are under stress, the rate at which the fluoresce can be decoupled from the rate of photosynthetic productivity.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][altitude][latitude][longitude]):
fluorescence (microwatt cm-2 s-2 nm-1 sr-1)


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (1 Day Composite), Lon+/-180


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (14 Day Composite)


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (14 Day Composite), Lon+/-180


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (3 Day Composite)


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (3 Day Composite), Lon+/-180


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (8 Day Composite)


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (8 Day Composite), Lon+/-180


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (Monthly Composite)


Fluorescence, Aqua MODIS, NPP, 0.0125°, West US, 2002-present (Monthly Composite), Lon+/-180


FNMOC 10m Surface Winds, 360x181, Monthly

Global monthly derived winds can be obtained for any point on the ocean
(Northern Hemisphere: 1967 - present, Southern Hemisphere: 1981 - present).

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
u_mean (East-West Component of 10m Surface Wind, m s-1)
v_mean (North-South Component of 10m Surface Wind, m s-1)
uv_mag_mean (Magnitude of 10m Surface Wind, m s-1)
taux_mean (East-West Component of 10m Wind Stress, N m-2)
tauy_mean (North-South Component of 10m Wind Stress, N m-2)
curl (Curl of Surface 10m Wind Stress, MPa m-1)


FNMOC 10m Surface Winds, 360x181, Monthly, Lon+/-180


FNMOC 20m Surface Winds, 360x181, Monthly

Global monthly derived winds can be obtained for any point on the ocean
(Northern Hemisphere: 1967 - present, Southern Hemisphere: 1981 - present).

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
u_mean (East-West Component of 20m Surface Wind, m s-1)
v_mean (North-South Component of 20m Surface Wind, m s-1)
uv_mag_mean (Magnitude of 20m Surface Wind, m s-1)
taux_mean (East-West Component of 20m Wind Stress, N m-2)
tauy_mean (North-South Component of 20m Wind Stress, N m-2)
curl (Curl of Surface 20m Wind Stress, MPa m-1)


FNMOC 20m Surface Winds, 360x181, Monthly, Lon+/-180


FNMOC 500 mb Geopotential Height, 360x181, Monthly

Monthly means of the FNMOC (Fleet Numerical Meteorology and Oceanography Center) 6-hourly analyzed geopotential height field on a 360 by 181 global spherical grid (1 degree). For more information on the models used to compute these data please see:

Rosmond, T. E., 1992: A prototype fully coupled Ocean-Atmosphere Prediction System, Oceanography Vol. 5, No. 1, 25-30.

Clancy, R. M., 1992: Operational Modeling: Ocean Modeling at the Fleet Numerical Oceanography Center, Oceanography Vol. 5, No. 1, 31-35.

ERD was established at the U.S. Navy Fleet Numerical Meteorology and Oceanography Center (FNMOC) to take advantage of the Navy's global oceanographic and meteorological databases. FNMOC produces operational forecasts of the state of the atmosphere and the ocean several times daily and maintains archives of several important parameters, such as sea level pressure and temperature. ERD derives a number of well-known environmental data and index products which are routinely distributed to researchers at many state and federal laboratories, as well as to academic and international researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
z_mean (Geopotential height at 500mb, gp)


FNMOC 500 mb Geopotential Height, 360x181, Monthly, Lon+/-180


FNMOC Ekman Transports, 360x180, 6-hourly

Ekman transports and wind stress curl calculated from geostrophic winds based
on FNMOC 360x180 (1-degree) analyzed fields of sea level pressure. This
dataset has data prior to the current month.

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
curl (Curl of Surface Wind Stress, MPa m-1)
ektrx (East-West Component of Ekman Transport, kg m-1 s-1)
ektry (North-South Component of Ekman Transport, kg m-1 s-1)


FNMOC Ekman Transports, 360x180, 6-hourly, Lon+/-180


FNMOC Sea Level Pressure for input to OSCURS Model, Interpolated from 6-hourly

The OSCURS numerical model is a research tool that allows oceanographers and
fisheries scientists to perform retrospective analyses of daily ocean surface
currents anywhere in a 90-km ocean-side grid from Baja California to the
Bering Strait.

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
pmsl (Pressure Reduced To Mean Sea Level, hPa)


FNMOC Sea Level Pressure for input to OSCURS Model, Longitude and Latitude

This dataset is used to convert x and y from the OSCURS model into longitude
and latitude.  The OSCURS numerical model is a research tool that allows
oceanographers and fisheries scientists to perform retrospective analyses of
daily ocean surface currents anywhere in a 90-km ocean-side grid from Baja
California to the Bering Strait.

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [y][x]):
latitude (degrees_north)
longitude (degrees_east)


FNMOC Sea Level Pressure, 360x180, 6-hourly

Sea level pressure based on the FNMOC 360x180 (1-degree) analyzed fields.

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
p_msl (Pressure Reduced To Mean Sea Level, hPa)


FNMOC Sea Level Pressure, 360x180, 6-hourly, Lon+/-180


FNMOC Wind and Ekman Transport Data, 360x180, Monthly, from 6-hr Pressure

Global monthly derived winds and Ekman transports can be obtained for any
point on the ocean (Northern Hemisphere: 1967 - present, Southern Hemisphere:
1981 - present).

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
pmsl (Pressure Reduced To MSL, Mb)
u_mean (East-West Component of Surface Wind, m s-1)
v_mean (North-South Component of Surface Wind, m s-1)
uv_mag_mean (Magnitude of Surface Wind, m s-1)
taux_mean (East-West Component of Wind Stress, N m-2)
tauy_mean (North-South Component of Wind Stress, N m-2)
curl (Curl of Surface Wind Stress, MPa m-1)
ektrx (East-West Component of Ekman Transport, kg m-1 s-1)
ektry (North-South Component of Ekman Transport, kg m-1 s-1)


FNMOC Wind and Ekman Transport Data, 360x180, Monthly, from 6-hr Pressure, Lon+/-180


FNMOC Wind and Ekman Transport Data, 360x181, 6-hourly for This Month

Global monthly derived winds and transports can be obtained for any point on
the ocean (Northern Hemisphere: 1967 - present, Southern Hemisphere:
1981 - present).

ERD was established at the U.S. Navy Fleet Numerical Meteorology and
Oceanography Center (FNMOC) to take advantage of the Navy's global
oceanographic and meteorological databases. FNMOC produces operational
forecasts of the state of the atmosphere and the ocean several times daily
and maintains archives of several important parameters, such as sea level
pressure and temperature. ERD derives a number of well-known environmental
data and index products which are routinely distributed to researchers at
many state and federal laboratories, as well as to academic and international
researchers.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
P_msl (Pressure Reduced To Mean Sea Level, hPa)
u (East-West Component of Surface Wind, m s-1)
v (North-South Component of Surface Wind, m s-1)
uv_mag (Magnitude of Surface Wind, m s-1)
taux (East-West Component of Wind Stress, N m-2)
tauy (North-South Component of Wind Stress, N m-2)
curl (Curl of Surface Wind Stress, MPa m-1)
ektrx (East-West Component of Ekman Transport, kg m-1 s-1)
ektry (North-South Component of Ekman Transport, kg m-1 s-1)


FNMOC Wind and Ekman Transport Data, 360x181, 6-hourly for This Month, Lon+/-180


Fort Bragg, California Coastal Digital Elevation Model (fort bragg 13 mhw 2012), 9.259259E-5°

Fort Bragg, California Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/3547; Fort Bragg, CA 1/3 arc-second Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Fort Bragg, California Coastal Digital Elevation Model (fort bragg 13 navd88 2012), 9.259259E-5°

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/3546; Fort Bragg, CA 1/3 arc-second Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


galapagos 1 isl 2016 (GDAL Band Number 1), 2.7777778E-4°

GDAL Band Number 1. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1)


galapagos 3 isl 2016 (GDAL Band Number 1), 8.3333335E-4°


Galveston Bay (G260) Bathymetric Digital Elevation Model (galveston bay G260 2018), 9.259259E-5°

Galveston Bay (G260) Bathymetric Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)


Galveston, Texas Coastal Digital Elevation Model (galveston 13 mhw 2007), 9.259259E-5°

Galveston, Texas Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/403; Galveston, TX 1/3 arc-second Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [y][x]):
Band1 (GDAL Band Number 1)


Garibaldi, Oregon Coastal Digital Elevation Model (garibaldi 13 mhw 2013), 9.259259E-5°

Garibaldi, Oregon Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/4752; Garibaldi, OR 1/3 arc-second NAVD 88 Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Garibaldi, Oregon Coastal Digital Elevation Model (garibaldi 13 navd88 2013), 9.259259E-5°

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/4750; Garibaldi, OR 1/3 arc-second NAVD 88 Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Garibaldi, Oregon Tsunami Forecast Grids for MOST Model (GaribaldiOR A), 0.02°

Garibaldi, Oregon Tsunami Forecast Grids for MOST Model. Pacific Marine Environmental Laboratory (PMEL) Forecast Model Grids provide bathymetric data strictly for tsunami inundation modeling with the Method of Splitting Tsunami (MOST) model. MOST is a suite of numerical simulation codes capable of simulating three processes of tsunami evolution: generation, transoceanic propagation, and inundation of dry land. Tsunami waves are computationally propagated across a set of three nested grids (A, B, and C), each of which is successively finer in resolution, moving from offshore to onshore. Nearshore details are resolved to the point that model output can be directly compared with tide gauge observations and can provide estimates of wave arrival time, wave amplitude and simulation of wave inundation onto dry land.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
bathy (Grid Bathymetry, meters)

NOAA OAR PMEL Center for Tsunami Research


Garibaldi, Oregon Tsunami Forecast Grids for MOST Model (GaribaldiOR B)


Garibaldi, Oregon Tsunami Forecast Grids for MOST Model (GaribaldiOR C)


GDAL Band Number 1 (adak 815 mhhw 2017), 1.4814814E-4°

GDAL Band Number 1. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1)


GDAL Band Number 1 (atka 815 mhhw 2017), 1.4814814E-4°


GDAL Band Number 1 (grenada 1 isl 2017), 2.7777778E-4°


GDAL Band Number 1 (ponce 13 mhw 2007), 9.259259E-5°

GDAL Band Number 1. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
z (GDAL Band Number 1)


gflux.mon.ltm (Monthly NARR) [time][y][x], 0001

Monthly NARR (gflux.mon.ltm). National Centers for Environmental Prediction data from https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Derived/monolevel/gflux.mon.ltm.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][y][x]):
gflux (Long Term Monthly Mean Ground Heat Flux at Surface, W/m^2)


Monthly NARR (gflux.mon.ltm). National Centers for Environmental Prediction data from https://psl.noaa.gov/thredds/dodsC/Datasets/NARR/Derived/monolevel/gflux.mon.ltm.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [y][x]):
lat (latitude coordinate, degrees_north)
lon (longitude coordinate, degrees_east)


gflux.sfc.mon.1981-2010.ltm (4x daily NMC reanalysis), 0001

4x daily National Meteorological Center (NMC) reanalysis. Data is from NMC initialized reanalysis
(4x/day).  It consists of T42  variables interpolated to
pressure surfaces from model (sigma) surfaces.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
gflux (Monthly Long Term Mean of Ground Heat Flux, W/m^2)
valid_yr_count (count of non-missing values used in mean)


GISTEMP: 1200km smoothing combined land/ocean (air.2x2.1200.mon.1991-2020.ltm.comb), 2.0°, 0001

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 1200km smoothing combined land/ocean. This is the 1200km smoothed combined land,ocean version of the dataset on a 2x2 grid. Sea Surface Temperature (SST) dataset used at NASA is now ERSST

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Long Term Mean Monthly Average Temperature Anomalies, degree_C)
valid_yr_count (count of non-missing values used in mean)


GISTEMP: 1200km smoothing combined land/ocean (air.2x2.1200.mon.anom.comb), 2.0°, 1880-present

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 1200km smoothing combined land/ocean. This is the 1200km smoothed combined land,ocean version of the dataset on a 2x2 grid. Sea Surface Temperature (SST) dataset used at NASA is now ERSST

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Monthly Average Temperature Anomalies, degree_C)


GISTEMP: 1200km smoothing combined land/ocean (air.2x2.1200.mon.ltm.comb), 2.0°, 0001


GISTEMP: 1200km smoothing land only (air.2x2.1200.mon.1991-2020.ltm.land), 2.0°, 0001

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 1200km smoothing land only

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Long Term Mean Monthly Average Temperature Anomalies, degree_C)
valid_yr_count (count of non-missing values used in mean)


GISTEMP: 1200km smoothing land only (air.2x2.1200.mon.anom.land), 2.0°, 1880-present

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 1200km smoothing land only

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Monthly Average Temperature Anomalies, degree_C)


GISTEMP: 250km smoothing combined land/ocean (air.2x2.250.mon.1991-2020.ltm.comb), 2.0°, 0001

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 250km smoothing combined land/ocean. This is the 1200km smoothed combined land,ocean version of the dataset on a 2x2 grid. Sea Surface Temperature (SST) dataset used at NASA is now ERSST V5

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Long Term Mean Monthly Average Temperature Anomalies, degree_C)
valid_yr_count (count of non-missing values used in mean)


GISTEMP: 250km smoothing combined land/ocean (air.2x2.250.mon.anom.comb), 2.0°, 1880-present

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 250km smoothing combined land/ocean. This is the 1200km smoothed combined land,ocean version of the dataset on a 2x2 grid. Sea Surface Temperature (SST) dataset used at NASA is now ERSST V5

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Monthly Average Temperature Anomalies, degree_C)


GISTEMP: 250km smoothing lan only (air.2x2.250.mon.1991-2020.ltm.land), 2.0°, 0001

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 250km smoothing lan only. GISS Surface Temperature Analysis (GISTEMP): 250km smoothing land only

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Long Term Mean Monthly Average Temperature Anomalies, degree_C)
valid_yr_count (count of non-missing values used in mean)


GISTEMP: 250km smoothing lan only (air.2x2.250.mon.anom.land), 2.0°, 1880-present

Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP): 250km smoothing lan only. GISS Surface Temperature Analysis (GISTEMP): 250km smoothing land only

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
air (Monthly Average Temperature Anomalies, degree_C)


GLIDERS: Last 10-day locations (INSITU GLIDER) (glider last15d) [profile]

GLIDERS: Last 10-day locations. Array for Real-time Geostrophic Oceanography (ARGO) profile location information provided by Physical Oceanography Division (PhOD)/Atlantic Oceanographic & Meteorological Laboratory (AOML). Only location and time are provided.

cdm_data_type = Point
VARIABLES (all of which use the dimensions [profile]):
profile_time (Time, seconds since 1970-01-01T00:00:00Z)
lat (Latitude, degrees_north)
lon (Longitude, degrees_east)


GLIDERS: Last 10-day locations (INSITU GLIDER) (glider last15d) [profile][dpth]

GLIDERS: Last 10-day locations. Array for Real-time Geostrophic Oceanography (ARGO) profile location information provided by Physical Oceanography Division (PhOD)/Atlantic Oceanographic & Meteorological Laboratory (AOML). Only location and time are provided.

cdm_data_type = Point
VARIABLES (all of which use the dimensions [profile][dpth]):
depth (m)
temperature (Sea Water Temperature, degree_Celsius)
salinity (Sea Water Practical Salinity, PSU)


GLOBAL MONTHLY CARBON FLUXES. NOAA National Environmental Satellite, Data, and Information Service (NESDIS) CoastWatch Caribbean and Gulf of Mexico (CWCGOM), NOAA Atlantic Oceanographic & Meteorological Laboratory (AOML) data from https://cwcgom.aoml.noaa.gov/thredds/dodsC/GLOBAL_MONTHLY_CARBON_FLUXES/FLUXES.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
flux (Sea-Air Carbon Flux, molm-2Yr-1)
flux_anom (Sea-Air Carbon Flux Anomaly, molm-2Yr-1)
difco2 (Surface Water-Atmospheric Partial Pressure Difference, uatm)
difco2_anom (Surface Water-Atmospheric Part. Press Diff Anom, uatm)


Global Monthly Dai Palmer Drought Severity Index (pdsi.mon.mean), 2.5°, 1870-2005

Global Monthly Dai Palmer Drought Severity Index. This version of the dataset (1870-2005) supersedes the 1870-2003 version art Physical Sciences Division (PSD) before Feb 2007

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
pdsi (Monthly Global Palmer Drought Severity Index: v2, Standardized Units of Relative Dry and Wet)


Global Monthly Dai Palmer Drought Severity Index (pdsi.mon.mean.selfcalibrated), 2.5°, 1850-2014

Global Monthly Dai Palmer Drought Severity Index. Monthly Self-calibrated Palmer Drought Severity Index (scPDSI)
calculated using observed surface air temperature (HadCRUT4 from
https://www.cru.uea.ac.uk/cru/data/temperature/ ) and
precipitation (from Dai et al. (1997, J.Clim: for 1870-1947) +
Chen et al. (2002, J. Hydromet.: for 1948-1978 + Global Precipitation Climatology Project (GPCP) v2.2 for
1979-present. The Dai and Chen P data were adjusted to have the
same mean as the GPCP data over the 1979-1996 period).
Calibration (or reference) period is 1950-1979.
Wind speed and air pressure data were from 20CR v2. Other data
were based on Climatic Research Unit (CRU) TS3.22. Documention:
Dai, A., 2011a: Characteristics and trends in various forms of the
Palmer Drought Severity Index (PDSI) during 1900-2008. J. Geophys.
Res., 116, D12115, doi:10.1029/2010JD015541.
Dai, A., K. E. Trenberth, and T. Qian, 2004: A global data set of
Palmer Drought Severity Index for 1870-2002: Relationship with soil
moisture and effects of surface warming. J. Hydrometeorology, 5,
1117-1130. Data source: Dr. A. Dai/National Center for Atmospheric Research (NCAR) (adai@ucar.edu). See
https://www.cgd.ucar.edu/cas/catalog/climind/pdsi.html for updates.
WARNING: PDSI and scPDSI over the higher latitudes (e.g., >50deg.)
may not be a good proxy of soil moisutre content. Also, PDSI and
scPDSI are highly autocorrelated indices not good at resolving
sub-seasonal variations.  Please use with caution!
This is a normalized version so that every grid box has the same
s.d. as that in the central U.S. during the calibr. period (1950-79)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
pdsi (Monthly Self-calibrated Palmer Drought Severity Index using Penman-Monteith PE, Standardized)


Global Seascapes 8DAY: Prototype (SEASCAPE 8DAY 360), 0.05°, 2002-present

Global Seascapes 8DAY: Prototype. Biogeographic framework. Space and time classified simultaneously from synoptic time series using hierarchical and topology preserving machine learning

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
CLASS
P (Prob, Punits)

NOAA CoastWatch, OSU, USF, NASA, UAF, IOOS, NMS


Global Seascapes 8DAY: Prototype (SEASCAPE 8DAY), 0.05°, 2002-present


Global Seascapes: Prototype (SEASCAPE MONTH 360), 0.05°, 2003-present

Global Seascapes: Prototype. Biogeographic framework. Space and time classified simultaneously from synoptic time series using hierarchical and topology preserving machine learning

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
CLASS
P (Prob, Punits)


Global Seascapes: Prototype (SEASCAPE MONTH), 0.05°, 2003-present


GLOBAL SEASON CARBON FLUXES. NOAA National Environmental Satellite, Data, and Information Service (NESDIS) CoastWatch Caribbean and Gulf of Mexico (CWCGOM), NOAA Atlantic Oceanographic & Meteorological Laboratory (AOML) data from https://cwcgom.aoml.noaa.gov/thredds/dodsC/GLOBAL_SEASON_CARBON_FLUXES/FLUXES.nc.das .

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
flux (Seasonal Sea-Air Carbon Flux, molm-2Season-1)
flux_anom (Seasonal Sea-Air Carbon Flux Anom, molm-2Season-1)
difco2 (Surface Water-Atmospheric Part Press Diff, uatm)
difco2_anom (Surface Water-Atmospheric Part Press Diff Anom, uatm)


GMI (GPM Microwave Imager) daily products (RSS GMI), 0.25°, 2017-present

GMI (GPM Microwave Imager) daily products. The daily files consist of over-ocean measurements (geophysical retrievals or parameters) mapped to a regular 0.25-degree grid complete with data gaps between orbits. Two maps exist for each parameter, one of ascending orbit segments and the other of descending orbit segments. Data on each of the segment maps are averaged where successive orbits cross and are overwritten at the seam or region where the last orbit of the day overlaps the first orbit of the day. Daily data files contain time maps consisting of the UTC observation time for each set of passes (ascending and descending).n Gridded data are organized according to observation date. All dates and times are Coordinated Universal Time (UTC), also known as Greenwich Mean Time (GMT), Zulu Time (Z), Universal Time (UT), and World Time.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
Time_Ascending (Decimal Hour of the Day, Ascending Passes, hour)
GMI_SST_Ascending (GMI Sea Surface Temperature, Ascending Passes, degree_C)
WSPD_LF_Ascending (Surface Wind Speed, Low Frequency, Ascending Passes, m s-1)
WSPD_MF_Ascending (Surface Wind Speed, Medium Frequency, Ascending Passes, m s-1)
Water_Vapor_Ascending (Atmospheric Water Vapor, Ascending Passes, mm)
Cloud_LW_Ascending (Cloud Liquid Water, Ascending Passes, mm)
Rain_Rate_Ascending (Rain Rate, Ascending Passes, MM' 'Hr-1)
Time_Descending (Decimal Hour of the Day, Descending Passes, hour)
GMI_SST_Descending (GMI Sea Surface Temperature, Descending Passes, degree_C)
WSPD_LF_Descending (Surface Wind Speed, Low Frequency, Descending Passes, m s-1)
WSPD_MF_Descending (Surface Wind Speed, Medium Frequency, Descending Passes, m s-1)
Water_Vapor_Descending (Atmospheric Water Vapor, Descending Passes, mm)
Cloud_LW_Descending (Cloud Liquid Water, Descending Passes, mm)
Rain_Rate_Descending (Rain Rate, Descending Passes, MM' 'Hr-1)


GOES-MSG Daily SST (SST GOES MSG), 0.05°, 2020-present

Geostationary Operational Environmental Satellite (GOES)-Meteosat Second Generation (MSG) Daily Sea Surface Temperature (SST GOES MSG)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sst (sea surface temperature, degree_C)


GPCC 1x1 Precipitation First Guess (precip.first.mon.total.1x1), 1.0°, 2012-present

Global Precipitation Climatology Centre (GPCC) 1x1 Precipitation First Guess (precip.first.mon.total.1x1)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly Total of Precipitation: 1degree Version: First Guess, mm)


GPCC Full Data Reanalysis Version 2018 0.5x0.5 Monthly Total (precip.mon.ltm.v2018), 0001

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 0.5x0.5 Monthly Total (precip.mon.ltm.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean GPCC Monthly total of precipitation, mm)
valid_yr_count (count of non-missing values used in mean)


GPCC Full Data Reanalysis Version 2018 0.5x0.5 Monthly Total (precip.mon.nobs.v2018), 1891-2016

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 0.5x0.5 Monthly Total (precip.mon.nobs.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation: nobs, observations)


GPCC Full Data Reanalysis Version 2018 0.5x0.5 Monthly Total (precip.mon.total.v2018), 1891-2016

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 0.5x0.5 Monthly Total (precip.mon.total.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation, mm)


GPCC Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.mon.ltm.1x1.v2018), 0001

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.mon.ltm.1x1.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean GPCC Monthly total of precipitation, mm)
valid_yr_count (count of non-missing values used in mean)


GPCC Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.mon.nobs.1x1.v2018), 1891-2016

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.mon.nobs.1x1.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly Total Precipitation: nobs, observations)


GPCC Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.mon.total.1x1.v2018), 1891-2016

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.mon.total.1x1.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation, mm)


GPCC Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total, 1891-2019

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 1.0x1.0 Monthly Total (precip.comb.v2018to2016-v6monitorafter.total)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation, mm)


GPCC Full Data Reanalysis Version 2018 2.5x2.5 Monthly Total (precip.mon.ltm.2.5x2.5.v2018), 0001

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 2.5x2.5 Monthly Total (precip.mon.ltm.2.5x2.5.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean GPCC Monthly Total of precipitation, mm)
valid_yr_count (count of non-missing values used in mean)


GPCC Full Data Reanalysis Version 2018 2.5x2.5 Monthly Total (precip.mon.nobs.2.5x2.5.v2018), 1891-2016

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 2.5x2.5 Monthly Total (precip.mon.nobs.2.5x2.5.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation: nobs, observations)


GPCC Full Data Reanalysis Version 2018 2.5x2.5 Monthly Total (precip.mon.total.2.5x2.5.v2018), 1891-2016

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 2018 2.5x2.5 Monthly Total (precip.mon.total.2.5x2.5.v2018)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly Total of precipitation, mm)


GPCC Full Data Reanalysis Version 7 0.5x0.5 Monthly Nobs (precip.mon.nobs.v7), 1901-2013

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7 0.5x0.5 Monthly Nobs (precip.mon.nobs.v7)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation: nobs, observations)


GPCC Full Data Reanalysis Version 7 0.5x0.5 Monthly Total (precip.mon.ltm.v7.1981-2010), 0001

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7 0.5x0.5 Monthly Total (precip.mon.ltm.v7.1981-2010)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean GPCC Monthly total of precipitation, mm)
valid_yr_count (count of non-missing values used in mean)


GPCC Full Data Reanalysis Version 7 0.5x0.5 Monthly Total (precip.mon.total.v7), 1901-2013

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7 0.5x0.5 Monthly Total (precip.mon.total.v7)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation, mm)


GPCC Full Data Reanalysis Version 7 1.0x1.0 Monthly Totals (precip.mon.ltm.1x1.v7.1981-2010), 0001

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7 1.0x1.0 Monthly Totals (precip.mon.ltm.1x1.v7.1981-2010)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean GPCC Monthly total of precipitation, mm)
valid_yr_count (count of non-missing values used in mean)


GPCC Full Data Reanalysis Version 7 1.0x1.0 Monthly Totals (precip.mon.total.1x1.v7), 1901-2013

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7 1.0x1.0 Monthly Totals (precip.mon.total.1x1.v7)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation, mm)


GPCC Full Data Reanalysis Version 7 1.0x1.0 Monthly Totals, 1901-2019

Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis Version 7 1.0x1.0 Monthly Totals (precip.comb.v7to2013-v6monitorafter.total)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation, mm)


GPCC Precipitation Full V7 1.0x1.0 Monthly NOBS (precip.mon.nobs.1x1.v7), 1901-2013

Global Precipitation Climatology Centre (GPCC) Precipitation Full V7 1.0x1.0 Monthly NOBS (precip.mon.nobs.1x1.v7)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly Total Precipitation: nobs, observations)


GPCC Precipitation Full V7 2.5x2.5 Monthly NOBS (precip.mon.nobs.2.5x2.5.v7), 1901-2013

Global Precipitation Climatology Centre (GPCC) Precipitation Full V7 2.5x2.5 Monthly NOBS (precip.mon.nobs.2.5x2.5.v7)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly total of precipitation: nobs, observations)


GPCC Precipitation Full V7 2.5x2.5 Monthly Total (precip.mon.ltm.2.5x2.5.v7.1981-2010), 0001

Global Precipitation Climatology Centre (GPCC) Precipitation Full V7 2.5x2.5 Monthly Total (precip.mon.ltm.2.5x2.5.v7.1981-2010)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean GPCC Monthly Total of precipitation, mm)
valid_yr_count (count of non-missing values used in mean)


GPCC Precipitation Full V7 2.5x2.5 Monthly Total (precip.mon.total.2.5x2.5.v7), 1901-2013

Global Precipitation Climatology Centre (GPCC) Precipitation Full V7 2.5x2.5 Monthly Total (precip.mon.total.2.5x2.5.v7)

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (GPCC Monthly Total of precipitation, mm)


GPCP Version 2.2 Combined Precipitation Dataset (Final) (precip.mon.ltm), 2.5°, 0001

Global Precipitation Climatology Project (GPCP) Version 2.2 Combined Precipitation Dataset (Final). Please redownload if you obtained this file before Aug 1 2012

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean Average Monthly Rate of Precipitation, seconds since 1970-01-01T00:00:00Z)
valid_yr_count (count of non-missing values used in mean)


GPCP Version 2.2 Combined Precipitation Dataset (Final) (precip.mon.mean), 2.5°, 1979-2015

Global Precipitation Climatology Project (GPCP) Version 2.2 Combined Precipitation Dataset (Final). Please redownload if you obtained this file before Aug 1 2012

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Average Monthly Rate of Precipitation, seconds since 1970-01-01T00:00:00Z)


GPCP Version 2.2 Combined Precipitation Dataset (Final) (precip.mon.mean.error), 2.5°, 1979-2015

Global Precipitation Climatology Project (GPCP) Version 2.2 Combined Precipitation Dataset (Final). Please redownload if you obtained this file before Aug 1 2012

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Absolute Error: Monthly Rate of Precipitation, seconds since 1970-01-01T00:00:00Z)


GPCP Version 2.3 Combined Precipitation Dataset (Final) (precip.mon.ltm), 2.5°, 0001

Global Precipitation Climatology Project (GPCP) Version 2.3 Combined Precipitation Dataset (Final). http://eagle1.umd.edu/GPCP_ICDR/GPCPmonthlyV2.3.pdf

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Long Term Mean Average Monthly Rate of Precipitation, seconds since 1970-01-01T00:00:00Z)
valid_yr_count (count of non-missing values used in mean)


GPCP Version 2.3 Combined Precipitation Dataset (Final) (precip.mon.mean), 2.5°, 1979-present

Global Precipitation Climatology Project (GPCP) Version 2.3 Combined Precipitation Dataset (Final). https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00970

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Average Monthly Rate of Precipitation, seconds since 1970-01-01T00:00:00Z)


GPCP Version 2.3 Combined Precipitation Dataset (Final) (precip.mon.mean.error), 2.5°, 1979-present

Global Precipitation Climatology Project (GPCP) Version 2.3 Combined Precipitation Dataset (Final). https://data.nodc.noaa.gov/cgi-bin/iso?id=gov.noaa.ncdc:C00970

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
precip (Absolute Error: Monthly Rate of Precipitation, seconds since 1970-01-01T00:00:00Z)


Grays Harbor (P280) Bathymetric Digital Elevation Model (grays harbor P280 2018), 9.259259E-5°

Grays Harbor (P280) Bathymetric Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)


National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [y][x]):
Band1 (GDAL Band Number 1)


Guam 1/3 sec Coastal Digital Elevation Model (guam 13 mhw 2008), 9.259259E-5°

Guam 1/3 sec Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)


Guayama, Puerto Rico Coastal Digital Elevation Model (guayama 13 mhw 2007), 9.259259E-5°

Guayama, Puerto Rico Coastal Digital Elevation Model. National Centers for Environmental Information (NCEI) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. DEMs are used for coastal process modeling (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [latitude][longitude]):
Band1 (GDAL Band Number 1, meters)

DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA; publisher; NOAA NGDC, NESDIS, NOAA; Boulder; Colorado; publisher; NOAA NGDC, NESDIS, NOAA; originator; Citation URL; https://www.ngdc.noaa.gov/dem/squareCellGrid/download/691; Guayama, PR 1/3 arc-second Tsunami Inundation DEM; Download page for accessing DEM, metadata record and development report.; download; Citation URL; https://www.ngdc.noaa.gov/mgg/inundation/tsunami/inundation.html; NOAA Tsunami Inundation Gridding Project; Project web page.; information


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [boundary]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [boundary]):
FSobc_in (free-surface inflow, nudging inverse time scale, second-1)
FSobc_out (free-surface outflow, nudging inverse time scale, second-1)
M2obc_in (2D momentum inflow, nudging inverse time scale, second-1)
M2obc_out (2D momentum outflow, nudging inverse time scale, second-1)
M3obc_in (3D momentum inflow, nudging inverse time scale, second-1)
M3obc_out (3D momentum outflow, nudging inverse time scale, second-1)

The name of the institution principally responsible for originating this data


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [boundary][tracer]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [boundary][tracer]):
Tobc_in (tracers inflow, nudging inverse time scale, second-1)
Tobc_out (tracers outflow, nudging inverse time scale, second-1)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [eta_psi][xi_psi]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [eta_psi][xi_psi]):
lon_psi (longitude of PSI-points, degrees_east)
lat_psi (latitude of PSI-points, degrees_north)
mask_psi (mask on PSI-points)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [eta_rho][xi_rho]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [eta_rho][xi_rho]):
lon_rho (longitude of RHO-points, degrees_east)
lat_rho (latitude of RHO-points, degrees_north)
h (time-independent bathymetry, meter)
f (Coriolis parameter, second-1)
pm (curvilinear coordinate metric in XI, meter-1)
pn (curvilinear coordinate metric in ETA, meter-1)
angle (angle between XI-axis and EAST, radians)
mask_rho (mask on RHO-points)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [eta_u][xi_u]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [eta_u][xi_u]):
lon_u (longitude of U-points, degrees_east)
lat_u (latitude of U-points, degrees_north)
mask_u (mask on U-points)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [eta_v][xi_v]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [eta_v][xi_v]):
lon_v (longitude of V-points, degrees_east)
lat_v (latitude of V-points, degrees_north)
mask_v (mask on V-points)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [s_rho]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [s_rho]):
Cs_r (S-coordinate stretching curves at RHO-points)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [s_w]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [s_w]):
Cs_w (S-coordinate stretching curves at W-points)


Gulf of Maine Operational Forecast System (GoMOFS): Forecast, Fields (Best Time Series) [tracer]

Gulf of Maine Operational Forecast System (GoMOFS): Forecast - Fields. Best time series, taking the data from the most recent run available.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [tracer]):
nl_tnu2 (nonlinear model Laplacian mixing coefficient for tracers, meter2 second-1)
LtracerSponge (horizontal diffusivity sponge activation switch)
Akt_bak (background vertical mixing coefficient for tracers, meter2 second-1)
Tnudg (Tracers nudging/relaxation inverse time scale, day-1)
LtracerSrc (tracer point sources and sink activation switch)
LtracerCLM (tracer climatology processing switch)
LnudgeTCLM (tracer climatology nudging activation switch)

The name of the institution principally responsible for originating this data