1 Synthetic Hyperspectral Radiances for Retrieval Algorithm Development J. E. Davies, J. A. Otkin, E. R. Olson, X. Wang, H-L. Huang, Ping Yang # and Jianguo Niu # Cooperative Institute for Meteorological Satellite Studies (CIMSS), Madison, WI # Department of Atmospheric Sciences, Texas A&M University, College Station, TX

2  Motivation  Radiative transfer model evaluation  Idealised cases  Realistic cases  Current status  Future work Outline

3 “The usual approach to solving an atmospheric retrieval problem is will consist of several stages: design a forward model to describe the instrument and the physics of the measurement; determine the criterion by which a solution is acceptable as valid; construct a numerical method to find a solution which satisfies the criterion;…” Clive D Rodgers in his preface to INVERSE METHODS FOR ATMOSPHERIC SOUNDING - Theory and Practice … design a forward model…

4 3 ice cloud models, 1 water cloud model /cm (~3-100 um) Water-spheres De = um Tropical De = um Mid-latitude De = um Polar De = um Two layer cloud model from Texas A&M coupled with UW/CIMSS clear-sky model

5 Altitude (km) Testing ly2g for idealised cases

6 Clear-sky brightness temperature spectrum and surface emissivity for IGBP land class

7 disort/g - asymmetry parameter disort/p - phase 498 phase angles disort/p+s - plus solar 30 deg zenith angle ly2g - LY2 executed for GIFTS channel bandpasses Consistent cloud single scattering properties and hi-res RT model

8 Idealised cloud profile comparisons

9 MM5 simulation to provide more realistic test case cloudy profiles, this example at 4pm local time over U.S. mid-west. Test atmospheric profiles from mesoscale model(s)

x 128 GIFTS cube of profiles over the Mid-west

11 netCDF GIFTS cubes and Unidata IDV

12 View from below

13 View from below

14 Realistic cloud profile comparisons - single layer/phase

15 Realistic cloud profile comparisons - two layer, thin cirrus 4

16 2 Realistic cloud profile comparisons - two layer, thick cirrus

17 Current Status  We have implemented the two-layer cloud model in the framework of the GIFTS fast model (ly2g) and included access to an ecosystem surface emissivity model (MODIS band resolution) - less than 1s per GIFTS spectrum (3000+ chans).  We have created a system for generating ly2g and LBLRTM/DISORT (Dave Turner’s LBLDIS) simulated brightness temperatures for GIFTS channels and equivalent cloudy profiles. [Those computed by LBLDIS operate on a vertical profile of cloud properties, ly2g must select approximately equivalent thin layer height/OD/radii for up to two layers].  We have not yet automated the selection of cloud layer heights, ODs, effective radii nor quantified the error level for “non-ideal” (but realistic) cases. Some errors have been introduced into our testing to date; some tests need to be repeated.  After we have completed our testing, we want to add a netCDF interface option to make easier the visualization of inputs/outputs with Unidata’s IDV.

18 Further Work  Re-visit the spectral emissivity assignment to take advantage of the work from our collaborators at Hawaii’s Institute of Geophysics and Planetology (Paul Lucey).  LBLDIS is well suited to simulation of ground and aircraft observations but introduces some inaccuracies in simulating TOA brightness temperatures for any practical wavenumber step (up to 300 hPa, /cm is fine; above 100 hPa even /cm is inaccurate at the ~ 0.5 K level).  The problem here is to provide the scattering code (required to simulate the underlying scattering/emitting atmosphere) with the angular distribution of the downwelling spectral radiance from the overlying emitting atmosphere - you can aggregate the upper level downwelling radiances to the scattering code spectral step size, and interpolate the lower level exitance to the smaller step size required for upper atmosphere RT. A coding task.  We need to address the inclusion of solar illumination in order to work confidently with the short wavelength end of the GIFTS spectrum. At high spectral resolution, variations in the solar spectrum itself can introduce further uncertainties.  Inclusion of a limited set of aerosol types - our collaborators at TAMU are already working on this.  As a community, to devise an agreed set of diverse but realistic cloudy atmosphere scenarios against which RT codes can be tested/inter-compared.

19 Fin (The End)