Ancillary Data

The algorithms for retrieval of ocean color radiometry and sea surface temperature from satellite radiometry make use of a number of ancillary data sources, in addition to the sensor observed radiometry. Examples include meteorological data (windspeed, surface pressure, relative humidity) and concentrations of atmospheric gases (water vapor, ozone, nitrogen dioxide). In general, the optimal ancillary data is not available when the satellite radiometry is first acquired, so the NASA OBPG processing uses best available ancillary data sources for processing satellite data in near realtime, and then reprocesses the data about one week later to refine the products using the optimal ancillary data.

The primary sources of ancillary data used in NASA ocean color and SST processing are described here. When a primary source is not available, a back-up source is used. If the primary nor it's backup source is available, the secondary source is used.

Dynamic Sources

Primary GMAO MERRA2 Meteorological data:: Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) meterological data from NASA/Goddard's Global Modeling and Assimilation Office (GMAO); source: GES DISC; spatial resolution: 0.5° x 0.625°; temporal resolution: 1-hourly; filename composition: GMAO_MERRA2.YYYYMMDDDTHHMMSS.{MET|AER|PROFILE}.nc; post-processing notes: Various GEOS 2-D datasets are aggregated in order to generate the aforementioned OBPG product.

GMAO Forward Processing for Instrument Teams Meteorological data:: The FP-IT stream generates only assimilation products. FP-IT processing uses a "semi-frozen" Goddard Earth Observing System (GEOS) system to ensure long-term continuity and reproducibility. FP-IT products serve primarily the NASA EOS Instrument Teams who require stable products over a long period of time. Reprocessing of historical periods using the FP-IT system is conducted as needed in coordination with Instrument Teams.; source: GES DISC; spatial resolution:0.5° (lat) x 0.625° (lon) horizontal grid; temporal resolution: 3-hourly; filename composition: N201919000_GMAOFP_3h.nc; post-processing notes: Various GEOS 2-D datasets are aggregated in order to generate the aforementioned OBPG product.

GMAO Forward Processing Instrument Team Profile data:: Profile data; source: GES DISC; spatial resolution: 0.5° (lat) x 0.625° (lon) horizontal grid; temporal resolution: 3-hourly; filename composition: (e.g. N201919000_PROFILE_GMAOFP_3h.nc)

Primary GMAO MERRA2 Aerosol data:: This dataset is based on the off-line "MERRAero" dataset that was integrated using meteorological fields from MERRA.; source: GES DISC; spatial resolution: 0.5° (lat) x 0.625° (lon) horizontal grid; temporal resolution: 1-hourly; filename composition: NYYYYDDD900_AER_MERRA2_1h.nc where YYYY=year, DDD=day of year (e.g. N2019151900_AER_MERRA2_1h.nc)

GMAO Forward Processing Instrument Team Aerosol data:: Analyses produced for Instrument Teams, using a stable version of Goddard Earth Observing System (GEOS) Model.; source: GES DISC; spatial resolution:; temporal resolution: 3-hourly; filename composition: (e.g. N201919003_AER_GMAOFP_3h.nc)

Secondary Real-time Ozone:: The real time ozone data is derived from TOMS-like instrument data. Until Feb 2023, we used data from the AURA OMI instrument (AURAOMI), and prior to 2005, we used data from the Nimbus-7 (N7TOMS) and Earth Probe (EPTOMS) TOMS instruments. We apply a normalization to all the ozone data based on a de-trended merged SBUV record.; source: GES DISC; spatial resolution: 1.25° x 1° (288 x 180 grid); temporal resolution: daily; filename composition: NYYYYDDD00_O3_III_24h.hdf where YYYY=year, DDD=day-of-year, III= instrument, N7TOMS, EPTOMS or AURAOMI. (e.g. N201918800_O3_AURAOMI_24h.hdf)

Primary GHRSST Level 4 CMC Global Foundation Sea Surface Temperature:: This dataset merges infrared satellite SST at varying points in the time series from the (A)TSR series of radiometers from ERS-1, ERS-2 and Envisat, AVHRR from NOAA-16,17,18,19 and METOP-A, and microwave data from TMI, AMSR-E and Windsat in conjunction with in situ observations of SST from drifting buoys and ships from the ICOADS program. It uses the previous days analysis as the background field for the statistical interpolation used to assimilate the satellite and in situ observations. This dataset adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications.; source: PO.DAAC; spatial resolution: 0.2° (lat) x 0.2° (lon); temporal resolution: daily; filename composition: YYYYMMDDHHMMSS-CMC-L4_GHRSST-SSTfnd-CMCXdeg-GLOB-vVER-fvVER.nc, where YYYY=year, MM=month, DD=day, HH=hour, MM=Min, SS=seconds, L4=PODAAC product level,X=spatial resolution, GLOB=global coverage, VER=file version, (e.g. 20170317120000-CMC-L4_GHRSST-SSTfnd-CMC0.2deg-GLOB-v02.0-fv02.0.nc)

Static Sources

Meteorological Climatology: This data set is used when none of the dynamic meteorological files or backup sources are available.; temporal resolution: monthly; filename: met_climatology_v2014.hdf

CAMS Gas Profile Data: These are global monthly means of well mixed gas concentrations (methane, carbon dioxide, nitrous oxide), for the years indicated.; source: Copernicus; temporal resolution: monthly; filename: cams73_latest_ch4_conc_surface_satellite_dm_2019.nc
cams73_latest_co2_conc_surface_inst_2020.nc
cams73_latest_n2o_conc_surface_inst_2019.nc; data download instructions: readme.txt

NO₂ Climatology: We currently use a monthly climatology of NO2 based on the Aura OMI data. Currently, only the climatology is used in ocean color processing. The climatology is created from the daily OMI NO2 files. The daily files of NO2, as it comes from the DAAC has many gaps and only has good coverage when averaged into monthly files.; source: GES DISC; spatial resolution: 0.25° x 0.25° (1440 x 720 grid); temporal resolution: Monthly, not year-specific; filename: no2_climatology_v2013.hdf

Ozone Climatology: This data set is used when none of the dynamic ozone files or backup sources are available.; temporal resolution: monthly; filename: ozone_climatology_v2014.hdf

Contiguous United States Land/Water Mask: Water mask in geographic projection for the Contiguous United States, covering latitudes 23 to 55 degrees and longitudes -127 to -65 degrees. Most values were aggregated to 2-arcsec resolution from Shuttle Radar Topography Mission 1-arcsecond data; missing or erroneous values were corrected with data from other sources.; source: SRTM; spatial resolution: 2 x 2 arcseconds (111601 x 57601 grid); filename: lwm_CONUS_SRTM60m_v3.1.nc

ETOPO1 1 Arc-Minute Global Relief Model: Compact form of ETOPO1 Ice Surface topography and bathymetry, with additional dataset for surface altitude of inland water bodies.; source: NOAA; spatial resolution: 1 x 1 arcminutes (21601 x 10801 grid); filename: ETOPO1_ocssw.nc

GEBCO 15-arcsec Global Relief Map and Land/Water Mask: Compact form of GEBCO surface topography and bathymetry, with additional dataset for surface altitude of inland water bodies, and land/water mask.; source: GEBCO Project; spatial resolution: 15 x 15 arcseconds (86401 x 43201 grid); filename: gebco_ocssw_v2020.nc

Global Land/Water Flags: Global Land/Water flags in geographic projection, with water classified into one of 7 types. Original 15-arcsecond files aggregated into a single global file, using 30-arcsec values where higher resolution is unavailable. Used only for VIIRS geolocation processing.; source: LandSeaMask_DEM; spatial resolution: 15 x 15 arcseconds (86400 x 43200 grid); filename: LandWater15ARC.nc

Global Land/Water Mask

Simple Land/Water mask generated using the GMT command:

grdlandmask -Glandmask_GMT15ARC.nc?watermask=nb -I15s -Rd -Df -N1/0

source: GSHHG

spatial resolution:15 x 15 arcseconds (86401 x 43201 grid)

filename: landmask_GMT15ARC.nc

Land Surface Albedo Climatology: MODIS/TERRA+Aqua BRDF/ land Albedo Gap-Filled Snow-Free 8-Day climatology, L3 Global 30ArcSec CMG, 2001-2017 for bands at 0.659, 0.858, 1.24, 1.64, and 2.13 microns.; source: LP DAAC; spatial resolution: 30 arc sec (42300 points in longitude, 21600 points in latitude); temporal resolution: Average (for the period 2001 – 2017) of albedo for an 8-day period, 46 periods over the year; filename: N2001_2017_ALBEDO_CLIM.nc

Mixed Layer Depth: Mixed layer depth is a type of atmospheric boundary layer characterized by vigorous turbulence tending to stir and uniformly mix, primarily in the vertical, quantities such as conservative tracer concentrations, potential temperature, and momentum or wind speed.; source: NOAA-NODC; filename: mld_climatology_woa1994.hdf

Sea Ice Climatology:: Used for 2 geographical regions centered around N and S pole. This data set is used when the dynamic Sea Ice file is not available.; source: NSIDC; temporal resolution: monthly; filename: ice_climatology.hdf

Sea Surface Salinity Climatology: source: NOAA-NODC; temporal resolution: 1°; filename: sss_climatology_woa2009.hdf

Sea Surface Temperature Climatology: source: PODAAC; temporal: 2048 (lat) x 4096 (lon); filename: sst_climatology.hdf

Shallow Water Mask: filename: watermask.dat

Water Spectra Mask: This file contains the entire range of wavelengths of electromagnetic radiation related to water absorption and scattering coefficients.; filename: water_spectra.nc

References

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., et al. (2011), The ERA-Interim reanalysis configuration and performance of the data assimilation system. Q.J.R. Meteorol. Soc., 137: 553-597. doi: 10.1002/qj.828

Derber, J.C., D.F. Parrish, and S.J. Lord, 1991:L The new global operational analysis system at the National Meteorological Center. Wea. Froecasting, 6, 538-547.

Kanamitsu, M., W. Ebisuzaki, J Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP-DOE AMIP-II Reanalysis (R-2), Bull. Amer. Meteor. Soc., 83, 1631-1643.

Kou, L., D. Labrie, P. Chylek, 1993, Refractive indices of water and ice in the 0.65-2.5 m spectral range, Appl. Opt.,32, 3531-3540.

Pope, R. M. and E. S. Fry, 1997, Absorption spectrum (380-700 nm) of pure water. II. Integrating cavity measurements, Appl. Opt.,36, 8710-8723.

Stammnes, P. (Ed),(2002). OMI Algorithm Theoretical Basis Document, Volume III, Clouds, Aerosols, and Surface UV Irradiance

Zhang, X., L. Hu, and M-X. He, 2009, Scattering by pure seawater: Effect of salinity, Opt. Express 17, 5698-5710.

Ancillary Data

Dynamic Sources

Meterological

Atmospheric Composition

Surface Properties