2000-2005 | 2006-2011 | 2012-2017 | 2018-2023
Bloom, S., A. da Silva, and D. Dee, 2005: Documentation and validation of the Goddard Earth Observing System (GEOS) Data Assimilation System – Version 4, NASA Technical Report Series on Global Modeling and Data Assimilation, NASA/TM-2005-104606, 26.
Collins, W. D., P. J. Rasch, B. E. Eaton, B. V. Khattatov, J.-F. Lamarque, and C. S. Zender, 2001: Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals, Methodology for INDOEX, Journal of Geophysical Research, 106, 7313-7336.
Ignatov, A., and N.R. Nalli, 2002: Aerosol Retrievals from the Multiyear Multisatellite AVHRR Pathfinder Atmosphere (PATMOS) Dataset for Correcting Remotely Sensed Sea Surface Temperatures, Journal of Atmospheric and Oceanic Technology, 19 12, 1986-2008. doi: 10.1175/1520-0426(2002)019<1986:ARFTMM>2.0.CO;2.
Ignatov, A., P. Minnis, N. Loeb, B. Wielicki, W. Miller, S. Sun-Mack, D. Tanré, L. Remer, I. Laszlo, E. Geier, 2005: Two MODIS Aerosol Products over Ocean on the Terra and Aqua CERES SSF Datasets, Journal of the Atmospheric Sciences, 62 4, 1008-1031. doi: 10.1175/JAS3383.1.
Kopp, G., G. Lawrence, G. Rottman, 2003: Total Irradiance Monitor Design and On-Orbit Functionality, SPIE Proceedings, 5171 4, 14-25. doi: 10.1117/12.505235.
Minnis, P., S. Sun-Mack, D.F. Young, P.W. Heck, D.P. Garber, Y. Chen, D.A. Spangenberg, R.F. Arduini, Q.Z. Trepte, W.L. Smith, J.K. Ayers, S.C. Gibson, W.F. Miller, G. Hong, V. Chakrapani, Y. Takano, K. Liou, Y. Xie, P. Yang, 2011: CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data #x2014; Part I: Algorithms, IEEE Transactions on Geoscience and Remote Sensing, 49, 11, 4374-4400. doi: 10.1109/TGRS.2011.2144601.
Nolin, A., R.L. Armstrong, and J. Maslanik, Updated daily. Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, [1998-present]. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media. 1998.
Remer, Tanré, Kaufman, Levy, & Mattoo. Algorithm for Remote Sensing of Tropospheric Aerosol from MODIS for Collection 005: Revision 2 Products: 04_L2, ATML2, 08_D3, 08_E3, 08_M3.
The time series of daily total solar irradiance (TSI) presented here for March 1, 2000 onwards is based primarily on Version-15 data from the Solar Radiation and Climate Experiment (SORCE). The time series is provided here.
Steps outlined below are used to put together the above time series, extended beyond the period covered by SORCE data. The time series is updated on a monthly basis.
Figure 1: The CERES TSI time series at 1 AU formed by compositing all the data products identified as described in the text.
Coddington, O.M., 2017: TSIS Algorithm Theoretical Basis Document. Laboratory for Atmospheres and Space Physics (LASP) Document No. 151430 RevA.
Kopp, G. and J. L. Lean, 2011: A new, lower value of total solar irradiance: Evidence and climate significance, Geophysical Research Letters, 38, L01706. doi: 10.1029/2010GL045777.
For the methodology of the RMIB composite construction:
Mekaoui, S., and S. Dewitte, 2008: Total Solar Irradiance Measurement and Modelling during Cycle 23, Solar Physics, 247, 203-216. doi: 10.1007/s11207-007-9070-y.
For the DIARAD/VIRGO dataset:
Dewitte, S., D. Crommelynck, and A. Joukoff, 2004: Total solar irradiance observations from DIARAD/VIRGO, Journal of Geophysical Research, 109, A02102. doi: 10.1029/2002JA009694.
For the absolute level:
Dewitte, S., E. Janssen, and S. Mekaoui, 2013: Science results from the Sova-Picard total solar irradiance instrument, AIP Conference Proceedings, 1531, 688-691. doi: 10.1063/1.4804863.
S. Dewitte, S. Nevens, 2016: The Total Solar Irradiance Climate Data Record, Astrophysical Journal, 830, 25.
S. Dewitte, N. Clerbaux, 2017: Measurement of the Earth Radiation Budget at the Top of the Atmosphere- A Review. Remote Sensing, 9 11, 1143. doi: 10.3390/rs9111143.
The CERES footprints are 25-km in diameter near nadir, so that there are more footprints on the boundary of a region than inside the region. Moreover, as CERES scans away from nadir, the footprints grow such that they are not small compared to the size of the region, and the distance between footprints in the scan direction increases. If the footprints are large compared to the region, as illustrated in Figure 1, overlap of the footprints with each other and with the boundaries of the region complicates the problem of computing regional averages. The selection of particular footprints to use at the boundaries of the region and the correlation of values of overlapping footprints needs to be considered. Because of these problems, improved techniques for computing regional averages have been developed (Hazra et al. 1993). At present, error studies are underway to define the degree of improvement which these methods provide.
Hazra, R., S. Park, G. L. Smith, and S. E. Reichenbach, 1993. Constrained least-squares image restoration filters for sampled image data, SPIE Proceedings, 2028, 177. https://doi.org/10.1117/12.158634.
After processing the CERES nested equal area grid output is transformed into an 360 longitude by 180 latitude equal angle grid. For nested regions greater than 1°, the equal angle regional values are replicated.
The map below shows the Earth subdivided into 18 different scene types. Seventeen were defined by the International Geosphere Biosphere Programme (IGBP) and an 18th (Tundra) was added to differentiate barren regions in high latitudes from barren deserts in the tropics and sub-tropics. The original data set was provided at 1km resolution by the USGS and subsequently degraded into 1/6 degree and 1-degree equal angle maps for use in CERES processing. These data are not intended for the study of land use changes over time but offer a snapshot of the Earth’s surface to provide initial estimates of surface emissivity.
Eighteen surface/biome type categories used by CERES are:
Land Surface Types as Defined by IGBP
*This information was taken from “The DIS 1km Land Cover Data Set” by Alan Belward and Tom Loveland. GLOBAL CHANGE, The IGBP Newsletter, #27, Sep., 1996.
Surface/biome types 1 ‐ 17 correspond to those defined by the International Geosphere-Biosphere Programme (IGBP).
Click on a specific region on the map to display the region information.
Or, enter the specific lat/lon and select "Submit".
CERES Ed2.6 and higher products use geodetically weighting to compute global means. This spherical Earth assumption gives the well-known So/4 expression for mean solar irradiance, where So is the instantaneous solar irradiance at the TOA. When a more careful calculation is made by assuming the Earth is an oblate spheroid instead of a sphere, and the annual cycle in the Earth’s declination angle and the Earth-sun distance are taken into account, the division factor becomes 4.0034 instead of 4. Consequently, the mean solar irradiance for geodetic weighting is ~1361/4.0034 = 340.0 W/m2, compared to 1361/4 = 340.3 W/m2 for spherical weighting.
Spherical earth zonal weighting uses the sin(lat1 lat2), where lat1 and lat2 are the boundaries of the zone geodetic weighting assumes an oblate spheroid, with the equatorial radius = 6378.137 km, and the polar radius = 6356.752 km.
A FORTRAN program used to calculate a global mean from zonal means given that the Earth is not a true sphere is provided here.
The CERES geodetic 1.0-deg zonal weights are provided here.
From the National Geospatial-Intelligence Agency, http://earth-info.nga.mil/GandG/coordsys/csatfaq/math.html
δA = Cpδy
CM = 2πRM
ε = [ƒ(2 − ƒ)] 1/2
CP = 2πRP
ω = (1 − ε2 sin2 φ) 1/2
ω4 = (1 − ε2 sin2 φ) 2
There are two CERES instrument onboard both the Terra and Aqua satellites. One is typically in cross-track mode and the other in either the RAPS (Rotating Azimuth Plane Scan) or FAPS (Fixed Azimuth Plane Scan) mode. The cross-track instrument is recommended by the CERES Science Team since the spatial distribution of footprints is uniform.
Also over time, the instrument in RAPS mode has increased spectral darkening of the transmissive optics. Click here for examples of instrument scanning spatial sampling. Refer to the Operations Tables for further details on instrument scan modes.
Level 3B:Level 3 data products that are adjusted within their range of uncertainty so as to satisfy known constraints on the climate system (e.g., consistency between average global net TOA flux imbalance and ocean heat storage).
Level 3:Data products are the radiative fluxes and cloud properties that are spatially averaged into uniform regional and zonal grids and globally and also temporally averaged into daily, monthly hourly, or monthly means.
Level 2:Data products are derived geophysical variables at the CERES footprint resolution as the Level 1B source data. They include the Level 1B parameters, along with the retrieved or computed geophysical variables such as radiative broadband fluxes and their associated MODIS cloud properties.
Level 1B:Data products are processed to sensor units. The BDS product contains CERES footprint filtered broadband radiances, geolocation and viewing geometry, Sun geometry, satellite position and velocity, and all raw engineering and status data from the instrument.