Modeled Hydrographic (Benthic) | NOAA/NEFSC | Biass-corrected ROMS and GLORYS | Bottom Temperature | Northeast U.S. Shelf 0.083° | Daily | 1959-2020

Bottom temperature product for the northeast U.S continental shelf obtained from a combination of observations and models, daily, 1/12°, 1959 - 2020. Two ocean products are combined, a regional ocean model (Regional Ocean Modeling System (ROMS)-NWA) and a global ocean data assimilated models (GLORYS12v1) to build a high-resolution, long-term bottom temperature product for the northeast U.S. continental shelf between 1959 and 2020. ROMS-NWA is bias-corrected using monthly decadal climatologies from ocean observations. Model skill was assessed using a large number of in situ observations. This performance assessment is presented in du Pontavice et al. 2023

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sea_water_temperature_at_sea_floor (°C)


Satellite Ocean Color | ESA | CCI (v6.0) | Ocean Colour Products (OC-CCI) | Global 0.0104°, Daily, 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 1km level-3 grid, and later to 1km 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).

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)
chlor_a (Chlorophyll-a concentration in seawater (not log-transformed), generated by as a blended combination of OCI, OCI2, OC2 and OCx algorithms, depending on water class memberships, milligram m-3)


Satellite Ocean Color | ESA | CCI (v6.0) | Ocean Colour Products (OC-CCI) | Global 0.0104°, Monthly, 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 1km level-3 grid, and later to 1km geographic projection, by Brockmann Consult's SNAP. Derived products were generally computed with the standard algorithms through 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).

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)
chlor_a (Chlorophyll-a concentration in seawater (not log-transformed), generated by as a blended combination of OCI, OCI2, OC2 and OCx algorithms, depending on water class memberships, milligram m-3)


Satellite Ocean Color | ESA | CCI (v6.0) | Ocean Colour Products (OC-CCI) | Global 0.0417°, 8-Day, 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_412_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_412_rmsd (Root-mean-square-difference of 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_443_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
Rrs_443_rmsd (Root-mean-square-difference of 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_490_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
Rrs_490_rmsd (Root-mean-square-difference of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
... (84 more variables)


Satellite Ocean Color | ESA | CCI (v6.0) | Ocean Colour Products (OC-CCI) | Global 0.0417°, Daily, 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]):
MERIS_nobs (Count of the number of observations from the MERIS sensor contributing to this bin cell)
MODISA_nobs (Count of the number of observations from the MODIS (Aqua) sensor contributing to this bin cell)
OLCI_A_nobs (Count of the number of observations from the OLCI (Sentinel-3a) sensor contributing to this bin cell)
OLCI_B_nobs (Count of the number of observations from the OLCI (Sentinel-3b) sensor 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_412_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_412_rmsd (Root-mean-square-difference of 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_443_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
... (84 more variables)


Satellite Ocean Color | ESA | CCI (v6.0) | Ocean Colour Products (OC-CCI) | Global 0.0417°, Monthly, 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_412_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 412 nm., sr-1)
Rrs_412_rmsd (Root-mean-square-difference of 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_443_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 443 nm., sr-1)
Rrs_443_rmsd (Root-mean-square-difference of 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_490_bias (Bias of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
Rrs_490_rmsd (Root-mean-square-difference of sea surface reflectance defined as the ratio of water-leaving radiance to surface irradiance at 490 nm., sr-1)
... (84 more variables)


Satellite Sea Surface Tempearture | NOAA/NCEI | Optimum Interpolation SST (OISST) AVHRR-only Analysis (V2.1) | SST Long-term mean | Global 0.25° | Daily | 1971-2000

NOAA High-resolution Blended Analysis: Daily Values using Advanced Very High Resolution Radiometer (AVHRR) only. Reynolds, et al., 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.  Climatology is based on 1971-2000 Optimum Interpolation (OI).v2 SST, Satellite data: Navy NOAA17 NOAA18 AVHRR, Ice data: National Centers for Environmental Prediction (NCEP) ice.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sst (Daily Long Term Mean Sea Surface Temperature, degree_C)


Satellite Sea Surface Tempearture | NOAA/NCEI | Optimum Interpolation SST (OISST) AVHRR-only Analysis (V2.1) | SST Long-term mean | Global 0.25° | Daily | 1982-2010

NOAA High-resolution Blended Analysis: Daily Values using Advanced Very High Resolution Radiometer (AVHRR) only. Reynolds, et al., 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.  Climatology is based on 1971-2000 Optimum Interpolation (OI).v2 SST, Satellite data: Navy NOAA17 NOAA18 AVHRR, Ice data: National Centers for Environmental Prediction (NCEP) ice.

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


Satellite Sea Surface Tempearture | NOAA/NCEI | Optimum Interpolation SST (OISST) AVHRR-only Analysis (V2.1) | SST Long-term mean | Global 0.25° | Daily | 1991-2020


Satellite Sea Surface Tempearture | NOAA/NCEI | Optimum Interpolation SST (OISST) AVHRR-only Analysis (V2.1) | SST Long-term mean | Global 0.25° | Monthly | 1991-2020

NOAA/National Centers for Environmental Information (NCEI) 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1. Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1 (available at https://doi.org/10.1175/Journal of Climate (JCLI)-D-20-0166.1). Anomaly is based on 1971-2000 OI.v2 SST. Satellite data: multi-satellite sensor (Advanced Very High Resolution Radiometer (AVHRR)+Visible and Infrared Imager/Radiometer Suite (VIIRS)) data. Ice data: National Centers for Environmental Prediction (NCEP) Ice and Goddard Space Flight Center (GSFC) Ice. Data less than 15 days old may be subject to revision.

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

NOAA | National Centers for Environmental Information


Satellite Sea Surface Tempearture | NOAA/NCEI | Optimum Interpolation SST (OISST) AVHRR-only Analysis (V2.1) | SST | Global 0.25° | Monthly | 1981-present

NOAA/National Centers for Environmental Information (NCEI) 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1. Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1 (available at https://doi.org/10.1175/Journal of Climate (JCLI)-D-20-0166.1). Anomaly is based on 1971-2000 OI.v2 SST. Satellite data: multi-satellite sensor (Advanced Very High Resolution Radiometer (AVHRR)+Visible and Infrared Imager/Radiometer Suite (VIIRS)) data. Ice data: National Centers for Environmental Prediction (NCEP) Ice and Goddard Space Flight Center (GSFC) Ice. Data less than 15 days old may be subject to revision.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sst (Monthly Mean of Sea Surface Temperature, degree_C)


Satellite Sea Surface Tempearture | NOAA/NCEI | Optimum Interpolation SST (OISST) AVHRR-only Analysis (V2.1) | SST | Global 0.25° | Weekly | 1981-present

NOAA/National Centers for Environmental Information (NCEI) 1/4 Degree Daily Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1. Reynolds, et al.(2007) Daily High-Resolution-Blended Analyses for Sea Surface Temperature (available at https://doi.org/10.1175/2007JCLI1824.1). Banzon, et al.(2016) A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies (available at https://doi.org/10.5194/essd-8-165-2016). Huang, B., C. Liu, V. Banzon, E. Freeman, G. Graham, B. Hankins, T. Smith, and H.-M. Zhang, 2021: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1 (available at https://doi.org/10.1175/Journal of Climate (JCLI)-D-20-0166.1). Anomaly is based on 1971-2000 OI.v2 SST. Satellite data: multi-satellite sensor (Advanced Very High Resolution Radiometer (AVHRR)+Visible and Infrared Imager/Radiometer Suite (VIIRS)) data. Ice data: National Centers for Environmental Prediction (NCEP) Ice and Goddard Space Flight Center (GSFC) Ice. Data less than 15 days old may be subject to revision.

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sst (Weekly Mean of Sea Surface Temperature, degree_C)


Satellite Sea Surface Temperature | NOAA/NCEI | ACSPO Super-collated | SST and Thermal Fronts (near real-time) | Global 0.02° | Daily | 2023-present

Sea surface temperature retrievals produced by NOAA/NESDIS/STAR office

cdm_data_type = Grid
VARIABLES (all of which use the dimensions [time][latitude][longitude]):
sea_surface_temperature (sea surface sub-skin temperature, degree_C)
sses_bias (SSES bias estimate, degree_C)
sses_standard_deviation (degree_C)
l2p_flags
l3s_flags
sst_count (Count of input L3U SST pixels)
quality_level (quality level of SST pixel)
sst_source (Source of major (highest weight) contribution to sst)
satellite_zenith_angle (degrees)
dt_analysis (deviation from SST reference, kelvin)
sea_ice_fraction (1)
wind_speed (m s-1)
sst_dtime (time difference from reference time, seconds)
measurement_dtime (time difference of highest weighted input SST from reference time. Is equal to sst_dtime for pixels where L3S-LEO-PM_N is available, seconds)
sst_gradient_magnitude (SST gradient magnitude value, kelvin/km)
sst_front_position (Binary SST front position indicator)


Satellite Sea Surface Temperature | NOAA/NCEI | ACSPO Super-collated | SST and Thermal Fronts (reanalysis) | Global 0.02° | Daily | 2000-present