89th American Meteorological Society Annual Meeting

Tuesday, 13 January 2009
The UW/CIMSS global land surface infrared emissivity database for GOES-R ABI simulation studies
Hall 5 (Phoenix Convention Center)
Eva Borbas, Univ. of Wisconsin, Madison, WI; and R. O. Knuteson, S. W. Seemann, L. Moy, T. Greenwald, D. Tobin, and H. L. Huang
An accurate infrared land surface emissivity product is critical for deriving accurate land surface temperatures, needed in studies of surface energy and water balance. Current sensors provide only limited information useful for deriving surface emissivity and researchers are required to use emissivity surrogates such as land-cover type or vegetation index in making rough estimates of emissivity. Inaccuracies in the emissivity assignment can have a significant effect on atmospheric temperature and moisture retrievals. To accurately retrieve atmospheric parameters, a global database of land surface emissivity with fine spectral resolution is required. An accurate emissivity is also required for any application involving calculations of brightness temperatures such as the assimilation of radiances into climate or weather models or simulation studies for future instrument design, preparation.

The monthly, so-called UW/CIMSS Baseline Fit (BF) global infrared land surface emissivity database has been developed and available since 2006 at the http://cimss.ssec.wisc.edu/iremis/ website and includes data from October 2002 at ten wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 microns) with 0.05 degree spatial resolution. The BF approach uses selected laboratory measurements of emissivity to derive a conceptual model, or baseline spectra, and then incorporates MODIS MYD11 measurements at six wavelengths to adjust the emissivity at 10 hinge points. These wavelengths were chosen to capture as much of the shape of the higher resolution emissivity spectra as possible between 3.6 and 14.3 microns.

As a current effort, an algorithm was developed to derive land surface emissivity database for the GOES-R ABI bands from the UW/CIMSS BF emissivity database. The recently developed algorithm has two parts. One part is a high spectral resolution (HSR) extraction algorithm where the HSR IR land surface emissivity is derived from a combination of HSR laboratory measurements of selected materials, and the UW/CIMSS BF global IR land surface emissivity database by using a principal component analysis (PCA) regression. The first Principal Components of 123 selected laboratory spectra (the wavenumber resolution between 2-4cm-1, at 416 wavenumbers) were regressed against the 10 hinge points of the monthly UW/CIMSS BF emissivity data. The second part of the algorithm is the instrument based spectral response function convolution.

In the presentation, first the UW/CIMSS GOES-R ABI simulated land surface emissivity database and its methodology will be presented including a study of the IR land surface emissivity directional variation. Secondly, comparison with other instrument based emissivity database, and with the UW the AIRS based so-called Best Estimate land surface emissivity spectra over the ARM SGP site will be shown.

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