Announcement of Opportunity: TOA fluxes

Introduction

ScaRaB(Scannerfor Radiation Budget) is an optical scanning radiometer devoted tothe measurements of radiative fluxes at the top-of-atmosphere (TOA) in theshortwave and longwave domain.

The conversion between radiance and hemispherical flux at thetop-of-atmosphere is classically obtained by application of scene type andobservation geometry dependent anisotropy models often referred to as angulardistribution models (ADMs).

A DPC (Data Product Catalog) will be available soon where you could find,for each product, all the variables and their definitions.

 

Level-2 products

Our first level 2 algorithm, called SEL, for ScaRaB ERBELike, is based on the ERBE ADMs (Suttles et al. 1988, 1989) and correspondinginversion methods (Wielicki and Green 1989). These ADMs were based on a crudedistinction of only 12 scene types (composed of 5 surface types and 4 cloudcover intervals). A better distinction for the ERBE instruments was notachievable; the information on scene content from the available two broadbandchannels is limited.

Theobjective of Megha-Tropiques is focused on the tropical zone and on processesat a scale of 100 kmand between a few hours to a few days. In this context, the instantaneous fluxestimates are the most important dataset and the accuracy needed on them had tobe improved. This means improving the scene identification and the angularcorrections. With SEL algorithm, the accuracy on the instantaneous fluxes isabout 20 W.m-2 twice more than the requirements for Megha-Tropiques.

 
 

IAO_ScaRaB_texte_image1.jpg

 
 
Example of instantaneous fluxes in Wm-2 LW domain on 01/03/1999 (ScaRaB/Ressurs 1) 

 
The CERES approach to improve anisotropy correction (Loeb et al. 2003a,b,2005, 2007) is based on synergy with collocated measurements at subpixelsampling from a multispectral visible-infrared (VIS-IR) imager. In thisconfiguration, the function of the imager is to provide the scene identification(including relevant information on cloud fraction, cloud phase, cloud opticalthickness, and cloud emissivity). This allows the consideration of about 600scene types within the operational CERES processing. The corresponding ADMs areconsidered as best knowledge models to describe anisotropy and will be used asreference. Our goal is to obtain ADMs close to these CERES ADMs. To do so weadopt the artificial neural network (ANN) approach described by Loukachine andLoeb (2003, 2004). This second level 2 algorithm developed for ScaRaB onMegha-Tropiques is called SANN for ScaRaB Artificial Neural Network, andthis algorithm achieves an accuracy of 10 W.m-2 on the instantaneous fluxes.You can fin a description of this approach on Viollier et al. (2009).
 
Level-3 products
 
This level 3 product will consist on Clear Sky Means (CSM) which consist of clear sky longwave and shortwave outgoing fluxes average on a 1.0 by 1.0 degrees grid (TBC) LW and SW outgoing fluxes over several (TBD) days.
  

Contacts

Olivier Chomette : chomette@lmd.polytechnique.fr

Patrick Raberanto : raberanto@lmd.polytechnique.fr

 

References

Brooks, D.R., E. H. Harrison, P. Minnis,J. T. Suttles, and R. Kandel, 1986: Development of Algorithms for Understandingthe Temporal and Spatial Variability of the Earth’s Radiation Balance, Rev. Geophys., 24, 2, 422-438.

Loeb, N. G., N. M. Smith, S.Kato, W. F. Miller, S. K. Gupta, P. Minnis, and B. A. Wielicki, 2003a: Angulardistribution models for top-of-atmosphere radiative flux estimation for theClouds and the Earth’s Radiant Energy System instrument on the TropicalRainfall Measuring Mission satellite. Part I: Methodology. J. Appl. Meteor., 42, 240-265.

Loeb, N. G., K. Loukachine, N.Manalo-Smith, B. A. Wielicki, and D. F. Young, 2003b: Angular distributionmodels for top-of-atmosphere radiative flux estimation from the Clouds and theEarth’s Radiant Energy System instrument on the Tropical Rainfall MeasuringMission satellite. Part II: Validation. J. Appl. Meteor., 42, 1748-1769.

Loeb, N. G., S. Kato, K.Loukachine, and N. Manalo-Smith, 2005: Angular distribution models fortop-of-atmosphere radiative flux estimation from the Clouds and the Earth’sRadiant Energy System instrument on the Terra satellite. Part I: Methodology. J. Atmos. Oceanic Technol., 22, 338-351.

Loeb, N. G., S. Kato, K.Loukachine, N. Manalo-Smith, and D. R. Doelling, 2007: Angular distributionmodels for top-of-atmosphere radiative flux estimation from the Clouds and theEarth’s Radiant Energy System instrument on the Terra satellite. Part II: Validation. J.Atmos. Oceanic Technol., 24, 564-584.

Loukachine, K., and N. G. Loeb,2003: Application of an artificial neural network simulation fortop-of-atmosphere radiative flux estimation from CERES. J. Atmos. Oceanic Technol., 20, 1749-1757.

Loukachine, K., and N. G. Loeb,2004: Top-of-atmosphere flux retrievals from CERES using artificial neuralnetwork. Remote Sens. Environ., 93, 381-390.

Suttles, J. T., and Co-authors,1988: Angular radiation models for Earth-atmosphere system. Volume 1: Shortwaveradiation. NASA Rep. RP 1184, 144 pp.

Suttles, J. T., R. N. Green, G.L. Smith, B. A. Wielicki, I. J. Walker, V. R. Taylor, and L. L. Stowe, 1989:Angular radiation models for Earth-atmosphere system. Volume 2: Longwaveradiation. NASA Rep. RP 1184-VOL-2, 88 pp.

Viollier, M., C. Standfuss, O. Chomette and A.Quesney 2009: Top-of-Atmosphere Radiance-to-Fluxconversion in the SW domain for the ScaRaB-3 instrument on Megha-Tropiques. J. Atmos. Oceanic Technol., 26, 2161-2171.

Wielicki, B. A., and R. N. Green,1989: Cloud identification for ERBE radiative flux retrieval. J. Appl. Meteor., 28, 1133-1146.