Fast and Accurate Radiative Transfer in the Thermal Regime by Simultaneous Optimal Spectral Sampling over All Channels

Abstract

The optimal spectral sampling (OSS) method provides a fast and accurate way to model radiometric observations and their Jacobians (required for inversion problems) as a linear combination of monochromatic quantities. The method is flexible and versatile with respect to the treatment of variable constituents, and the method’s fidelity to reference line-by-line (LBL) calculations is tunable. The focus of this paper is on the modeling of radiances from hyperspectral infrared sounders in both clear and cloudy (scattering) atmospheres for application to retrieval and data assimilation. In earlier articles, the authors presented an approach that performed spectral sampling for each channel sequentially. This approach is particularly robust in terms of preserving fidelity to LBL models and yields ratios of monochromatic calculations per channel of approximately 1:1 for such hyperspectral sensors as the Infrared Atmospheric Sounding Interferometer (IASI) or the Atmospheric Infrared Sounder (AIRS) (when tuned for nominal 0.05-K accuracy). This paper describes the generalization of the OSS concept to minimize the total number of monochromatic points required to model a set of channels across individual spectral bands or across the entire domain of the measurements. Its application to principal components of radiance measurements is addressed. It is found that the optimal solution produced by the OSS method offers computational advantages over existing models based on principal components, but, more importantly, it has superior error characteristics.

Characterization and evaluation of AIRS-based estimates of the deuterium content of water vapor

“…The radiative transfer forward model used for this work is the optimal spectral sampling (OSS) fast radiative transfer model (RTM) (Moncet et al, 2008(Moncet et al, , 2015. The OSS approach is integrated in the operational Cross-Track Infrared Sounder (CrIS; Han et al, 2013) processing system (Divarkala et al, 2014) and has also been utilized for trace gas retrievals from CrIS (e.g., Shephard and Cady-Pereira, 2015).…”

An update on the RTTOV fast radiative transfer model (currently at version 12)

“…extinction coefficient, β ext ; single-scattering albedo, ω 0 ) from the input profile of ice crystal effective diameter. Users can explicitly provide ice effective diameter or can choose among four parameterizations in terms of ice water content and temperature (Ou and Liou, 1995;Wyser, 1998;Boudala et al, 2002;McFarquhar et al, 2003). The second scheme uses the methodology developed initially for the IR (Vidot et al, 2015) by using a large database of optical properties of ice clouds provided by Baran et al (2014).…”

Comparison of optimal estimation HDO∕H<sub>2</sub>O retrievals from AIRS with ORACLES measurements

“…Early remote sensing of atmospheric HDO was made by the ATMOS (Atmospheric Trace Molecule Spectroscopy) mission on the Space Shuttle (Rinsland et al, 1991;Moyer et al, 1996;Kuang et al, 2003), retrieving in the upper troposphere-lower stratosphere. Global stratospheric HDO measurements have been provided by satellite instruments, including Envisat/MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) (Steinwagner et al, 2007(Steinwagner et al, , 2010Lossow et al, 2011), Odin/SMR (Sub-Millimetre Radiometer) (Murtagh et al, 2002;Urban et al, 2007) and SCISAT-1 (Scientific Satellite)/ACE-FTS (Atmospheric Chemistry Experiment Fourier transform spectrometer) (Bernath et al, 2005;Nassar et al, 2007;Lossow et R. L. Herman et al: Comparison of optimal estimation HDO/H 2 O retrievals al., 2011; Randel et al, 2012).…”