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CERES Computed Fluxes for Consistent Clouds
CERES and ERBE SW diurnal averaging comparison (1)
- Example Peruvian maritime stratus region, morning stratus clouds that burn off in the afternoon
- One would assume a greater albedo in the morning than in the afternoon
- Terra SSF1deg (ERBE interpolation) product overestimates the daily SW flux
- Aqua SSF1deg (ERBE interpolation) product underestimates the daily SW flux
- Terra SYN1deg (CERES interpolation) estimate is closer to the truth
CERES and ERBE SW diurnal averaging comparison (2)
- Difference the time interpolated SW fluxes based on Terra (10:30 equator local crossing time)
and Aqua (1:30PM) of December 2002
- Terra-only fluxes > Aqua-only fluxes over marine stratus regions (morning clouds)
- Aqua-only fluxes > Terra-only fluxes over land afternoon convection regions
- The SYN1deg have removed the Terra- Aqua sampling bias of the diurnal cycle
CERES and ERBE SW diurnal averaging comparison (1)
- Difference of the monthly regional means of CERES (SYN1deg) and ERBE (SSF1deg) temporal SW averaging
for Terra (10:30 AM sun-synchronous) December 2002
- Terra-SYN1deg monthly means overestimate the SW flux over maritime stratus and underestimate the afternoon convection regions
- Regional monthly differences can be > 20 Wm-2
- Global bias is - 1.0 Wm-2
CERES and ERBE LW diurnal averaging comparison (1)
- CERES-only (ERBE) LW land temporal interpolation uses a half-sine and constant nighttime fit if the daytime LW flux
is greater than the nighttime flux, to fill in 24 hourly time steps during the day
- CERES-only (ERBE) LW ocean and snow temporal interpolation uses linear interpolation, to fill in 24 hourly time steps during the day.
It is assumed that there are no LW diurnal cycles over clear-sky ocean and snow surfaces
- CERES/GEO LW temporal interpolation uses all CERES and 8 3-hourly (GMT) GEO derived BB fluxes normalized at coincident CERES/GEO
measurements to maintain CERES instrument calibration. All remaining hourly time steps are linearly interpolated between measurement times.
For nonpolar regions there are usually 2 CERES and 8 GEO measurements, almost half the hourly time steps
- For ERBE LW interpolation the land LW half-sin fit models the daytime heating, which assumes symmetry about noon.
- For ERBE LW interpolation, for a given daytime measurement, both a previous and following nighttime observation must be present to
apply half-sine fit. If the conditions are not met linear interpolation between measurements are used, causing erroneous flux estimates.
- It seems that the ERBE LW interpolation half-sine fit does not capture the daytime heating lag and nighttime cooling over land.
CERES and ERBE LW diurnal averaging comparison (2)
- Example Peruvian maritime stratus region, morning stratus clouds that burn off in the afternoon
- One would assume a greater albedo in the morning than in the afternoon
- ERBE LW interpolation would show symmetry about noon or no difference between 16:30 and 7:30 local time as the CERES interpolation does.
- The land afternoon convection regions (blue, for example Brazil), which are colder in the afternoon than morning and daytime heating thermal
lag regions (red, for example Sahara) indicating land is warmer near sunset rather than sunrise.
- Also the Peruvian maritime stratus regions are warmer in the afternoon indicating the clear-sky ocean is warmer than the morning cloud tops
- PM-AM differences can be ~ 30 Wm-2
CERES and ERBE LW diurnal averaging comparison (3)
- Terra-ERBE temporally interpolated means are similar to the CERES temporal averaging because the daytime and nighttime LW biases cancel out
- Global bias is +0.5 Wm-2
- CERES LW interpolation averaging reveals 3-hourly or 45° longitude striping due to discretization of the 3-hourly GEO measurements
Radiance view θ, solar θ0, and relative azimuth Φ angle definition
CERES vs ERBE view angle ADM comparison
Loeb, N.G., S. Kato, K. Louckachine, N. Manalo-Smith, D.R. Doelling; 2007: Angular Distribution
Models for Top-of-Atmosphere Radiative Flux Estimation from the Clouds and the Earth's Radiant Energy
System Instrument on the Terra Satellite Part II: Validation; J. Atmos. Oceanic Technol., 24, 564-584 Loeb et al.
- Ideally there would be a constant flux with respect to view angle
- Note the CERES ADM has almost removed the functionality with view angle, whereas ERBE-like is biased with view angle
- ERBE ADMs insufficiently limb-darken in the LW and insufficiently limb-brighten in SW.
CERES vs ERBE Clear-sky Scene Identification Comparison
- Note the cloud contamination in the ERBE-like clear-sky albedo product
- ERBElike scene identification uses 12 scene types to identify clear-sky footprints, whereas CERES uses ~600 scene types
Edition3 vs Edition2 Product Comparison Table
| Products | Edition3 | Edition2 |
|
| EBAF | EBAF | EBAF |
|
| SYN | SYN1deg-3Hour | SYN |
| SYN1deg-Day | SRBAVG (GEO) |
| SYN1deg-M3Hour | SRBAVG (GEO)/AVG/ZAVG |
| SYN1deg-Month | SRBAVG (GEO)/AVG/ZAVG |
|
| CRS | CRS | CRS |
| CRS1deg-Hour | FSW |
| CRS1deg-Day | N/A |
| CRS1deg-Month | N/A |
|
| SSF | SSF | SSF |
| SSF1deg-Hour | SFC |
| SSF1deg-Day | SRBAVG (nonGEO) |
| SSF1deg-MHour | SRBAVG (nonGEO) |
| SSF1deg-Month | SRBAVG (nonGEO) |
|
| ISCCP-D2like | ISCCP-D2like_M3Hour | ISCCP-D2like_Day/Nit* |
| ISCCP-D2like_Month | ISCCP-D2like_Day/Nit* |
|
| ERBElike | ES8 | ES8 |
| ES9 | ES9 |
| ES4 | ES4 |
*Day/Nit contains only the cloud properties whereas FlxDay/FlxNit contains both fluxes and cloud properties.
File Names Tables
Edition and netCDF Products / Lite Products
Edition2, Edition3, & netCDF File names
| Product |
Ed2 HDF Name |
Edition3 HDF Name |
Edition3 netCDF Name |
|
| EBAF |
EBAF | CERES_EBAF_TOA_Terra_Edition3A |
CERES_EBAF_Terra_Ed3A_xxxxx.nc |
|
| SYN |
SYNI |
CER_SYN1deg-1Hour_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_SYN1deg-1hour_Terra_Ed3A_xxxxx.nc |
| SYN |
CER_SYN1deg-3Hour_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_SYN1deg-3hour_Terra_Ed3A_xxxxx.nc |
SRBAVG
(GEO) |
CER_SYN1deg-Day_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_SYN1deg-Day_Terra_Ed3A_xxxxx.nc |
| CER_SYN1deg-M3Hour_Terra-MODIS_Edition3A_013013.YYYYMM |
CERES_SYN1deg-M3Hour_Terra_Ed3A_xxxxx.nc |
| AVG/ZAVG | CER_SYN1deg-Month_Terra-MODIS_Edition3A_013013.YYYYMM |
CERES_SYN1deg-Month_Terra_Ed3A_xxxxx.nc |
|
| CRS |
CRS |
CER_CRS_Terra-FM1-MODIS_Edition3A_013013.YYYYMMDDHH |
|
| FSW |
CER_CRS1deg-Hour_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_CRS1deg-Hour_Terra_Ed3A_xxxxx.nc |
| (new) |
CER_CRS1deg-Day_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_CRS1deg-Day_Terra_Ed3A_xxxxx.nc |
| (new) |
CER_CRS1deg-Month_Terra-MODIS_Edition3A_013013.YYYYMM |
CERES_CRS1deg-Month_Terra_Ed3A_xxxxx.nc |
|
| SSF |
SSF |
CER_SSF_Terra-FM1-MODIS_Edition3A_013013.YYYYMMDDHH |
|
| SFC | CER_SSF1deg-Hour_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_SSF1deg-Hour_Terra_Ed3A_xxxxx.nc |
SRBAVG
(nonGEO) |
CER_SSF1deg-Day_Terra-MODIS_Edition3A_013013.YYYYMMDD |
CERES_SSF1deg-Day_Terra_Ed3A_xxxxx.nc |
| CER_SSF1deg-Month_Terra-MODIS_Edition3A_013013.YYYYMM |
CERES_SSF1deg-Month_Terra_Ed3A_xxxxx.nc |
|
| ISCCP-D2like |
ISCCP-D2like |
CER_ISCCP-D2like-FlxDay_Terra-MODIS_Edition3A_013013.YYYYMM |
CERES_ISCCP-D2like-FluxDay_Terra_Ed3A_xxxxxx.nc |
| CER_ISCCP-D2like-FlxNit_Terra-MODIS_Edition3A_013013.YYYYMM |
CERES_ISCCP-D2like-FluxNit_Terra_Ed3A_xxxxx.nc |
| CER_ISCCP-D2like-GEO_Edition3A_013013.YYYYMM |
CERES_ISCCP-D2like-GEO_Ed3A_xxxxx.nc |
| CER_ISCCP-D2like-Mrg_GEO-Terra_Aqua-Edition3A_013013.YYYYMM |
CERES_ISCCP-D2like-Merge_Ed3A_xxxxx.nc |
|
| FLASHFlux |
FLASH_SSF | FLASH_SSF_Terra-FM1-MODIS_Version2G_008019.YYYYMMDDHH |
FLASH_SSF_Terra_Version2G_xxxxxx.nc |
| FLASH_TISA | FLASH_SSF1deg-Day_Terra+Aqua_Version2G_013013.YYYYMMDD |
FLASH_SSF1deg-Day_Terra+Aqua_Version2G_xxxxx.nc |
| FLASH_SSF1deg-Month_Terra+Aqua_Version2G_013013.YYYYMM |
FLASH_SSF1deg-Month_Terra+Aqua_Version2G_xxxxxx.nc |
|
| ERBElike |
ERBElike | CER_ES4_Terra-FM1_Edition3_013013.YYYYMM |
CERES_ES4_Terra_Ed3_xxxxx.nc |
| CER_ES8_Terra-FM1_Edition3_013013.YYYYMMDD | |
** Last update April 2010
Lite Level3&4 Edition2.6 Product Filenames
| Edition2 HDF Name |
Edition2.6 HDF Name |
Edition2.6 netCDF Name |
| EBAF | CERES_EBAF-TOA-Terra_Ed2.5_xxxxx.nc | CERES_EBAF-TOA-Terra_Ed2.5_Subset_xxxxx.nc |
| SYN | CER_SYN1deg-Day-lite_Terra_Ed2.5_xxxxx.hdf | CERES_SYN1deg-Day-lite_Terra_Ed2.5_Subset_xxxxx.nc |
| CER_SYN1deg-Month-lite_Terra_Ed2.5_xxxxx.hdf | CERES_SYN1deg-Month-lite_Terra_Ed2.5_Subset_xxxxx.nc |
| SSF | CER_SSF1deg-Day-lite_Terra_Ed2.5_xxxxx.hdf | CERES_SSF1deg-Day-lite_Terra_Ed2.5_Subset_xxxxxx.nc |
| CER_SSF1deg-Month-lite_Terra_Ed2.5_xxxxx.hdf | CERES_SSF1deg-Month-lite_Terra_Ed2.5_Subset_xxxxxx.nc |
** Last update April 2010
Level Descriptions
- 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.
Computed Fluxes Description
- CERES executes a forward radiation transfer calculation at both CERES footprint and grid box spatial scale.
- Footprint & grid box calculations are archived in the "CRS" and "SYN" products respectively.
- Model results are archived at 5 levels in the atmosphere:
Top-of-Atmosphere (TOA), 70hPa, 200hPa, 500hPa and at the Earth's surface.
- After an initial run, modeled and observed TOA fluxes are compared, based on this difference, various model
inputs are adjusted to bring TOA calculations closer to CERES observations.
- This adjustment of input variables (or "tuning") allows for uncertainty in each relevant variable as a function
atmospheric conditions.
- For "clear sky" then, surface albedo, skin temperature, humidity may be adjusted.
- For "cloudy sky" situations, cloud fraction, height and optical depth may be adjusted.
- Assuming our uncertainty of input variables is larger than the uncertainty of the observed TOA fluxes, tuning
"pushes" the clouds and atmospheric state to be more consistent with respect to CERES observed fluxes.
Computation of Modeled Fluxes & Consistent Input Variables
| Acronym | Definition |
| AOD | Aerosol Optical Depth |
| GEOS | Goddard Earth Observing System |
| GMAO | Global Modeling & Assimilation Office |
| hPa | hecto-Pascals |
| LW | Longwave (~5um-200um) |
| MATCH | Model for Atmospheric Chemistry & Transport |
| MODIS | Moderate resolution Imaging Spectrometer |
| NCEP | National Center for Environmental Predication |
| OPAC | Optical Properties of Aerosols & Clouds |
|
|
| Acronym | Definition |
| p | pressure level |
| q | water vapor mixing ration |
| SBUV | Solar Backscatter Ultra-Violet Radiometer |
| SW | Shortwave (~0.2um-4.0um) |
| t | temperature |
| TIROS | Television Infrared Observation Satellites |
| TOA | Top Of Atmosphere (0.1hPa) |
| TOVS | TIROS Operational Vertical Sounder |
|
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