88th Annual Meeting (20-24 January 2008)

Wednesday, 23 January 2008
Use of SEVIRI cloud properties to simulate radiative fluxes from GOES-R ABI
Exhibit Hall B (Ernest N. Morial Convention Center)
R. T. Pinker, University of Maryland, College Park, MD; and R. Hollmann and H. Wang
Two types of approaches are widely used for deriving radiative fluxes from satellite observations. In one, utilized is a relationship between the broadband reflected radiation at the top of the atmosphere (TOA) and atmospheric transmittance. Once the atmospheric transmittance is known, the surface irradiance can be computed from the incoming solar flux at the TOA and auxiliary information on the state of the atmosphere and the surface. An alternative approach is to determine first atmospheric optical parameters (aerosol optical depth from clear sky radiances, and cloud optical depth and phase, from cloudy radiances). The rationale for the latter approach is that additional channels can be utilized to learn about cloud properties that in turn, affect the radiative fluxes. It is of interest to evaluate both approaches in a given situation. In order to implement the second approach, a suitable inference scheme is needed. Such an inference scheme has been previously developed at the University of Maryland. In this study, it is implemented with cloud properties derived at the EUMETSAT Satellite Application Facility on Climate Monitoring (CM-SAF) from METEOSAT-8 SEVIRI observations. These were produced under a reprocessing effort at CM-SAF and are presently available at hourly time scale and 15 km spatial scale for the full disk for about four months of 2004. Such data will also become available from the Advanced Baseline Imager (ABI) on GOES-R which has similar channels to those of SEVIRI. It is of interest to utilize this information for deriving radiative fluxes and evaluate them against ground observations as well as against a radiative flux product of the first type as produced at CM-SAF. It is hoped that an effort of this type will provide guidelines for optimal utilization of information from ABI on GOES-R.

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