Gauss-Seidel Limb Scattering (GSLS) radiative transfer model development in support of the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler mission

Abstract

Abstract. The Gauss-Seidel Limb Scattering (GSLS) radiative transfer (RT) model simulates the transfer of solar radiation through the atmosphere, and is imbedded in the retrieval algorithm used to process data from the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP), which was launched on the Suomi NPP satellite in October 2011. A previous version of this model has been compared with several other limb scattering RT models in previous studies, including Siro, MCC++, CDIPI, LIMBTRAN, SASKTRAN, VECTOR, and McSCIA. To address deficiencies in the GSLS radiance calculations revealed in earlier comparisons, several recent changes have been added that improve the accuracy and flexibility of the GSLS model, including: 1. Improved treatment of the variation of the extinction coefficient with altitude, both within atmospheric layers and above the nominal top of the atmosphere (TOA). 2. Addition of multiple scattering source function calculations at multiple zeniths along the line of sight (LOS). 3. Re-introduction of the ability to simulate vector (polarized) radiances. 4. Introduction of variable surface properties along the limb LOS, with minimal effort required to add variable atmospheric properties along the LOS as well. 5. Addition of the ability to model multiple aerosol types within the model atmosphere. The model improvements numbered 1–3 above are verified by comparison to previously published results (using standard radiance tables whenever possible), demonstrating significant improvement in cases for which previous versions of the GSLS model performed poorly. The single-scattered radiance errors that were as high as 4% in earlier studies are now generally reduced to < 0.5%, while total radiance errors generally decline from > 10% to 1–2%. In all cases, the height dependence of the GSLS radiance error is greatly reduced.

Improvement of stratospheric aerosol extinction retrieval from OMPS/LP using a new aerosol model

“…The original LP aerosol extinction retrieval algorithm described in (Rault and Loughman, 2013) uses radiance data measured at multiple wavelengths in the visible and near-IR spectral 10 region. The updated version V1.0 algorithm described in detail by (Loughman et al, 2018) is based on Mie theory, using radiances from one wavelength at 675 nm. The aerosol extinction profiles are retrieved from limb-scatter observations using the aerosol scattering index (ASI) as the measurement vector.…”

“…This quantity is roughly proportional to aerosol extinction, as described in (Loughman et al, 2018), and is defined at wavelength λ at altitude z in equations (1)-(2):…”

“…This formulation removes the first order effects of both Rayleigh scattering and reflectivity, though there are second order effects which will be discussed later. As discussed in (Loughman et al, 2018), the effect of Rayleigh and aerosol scattering on radiances is not strictly additive when the optical path along the line of sight (LOS) becomes thick. For example, Rayleigh scattering also attenuates aerosol scattering, reducing the sensitivity of the ASI to 30 aerosol loading at lower altitudes (i.e., where Rayleigh scattering is high).…”

“…The Gauss-Seidel limb scattering (GSLS) radiative transfer model (Herman et al, 1994;10 Herman et al, 1995;Loughman et al, 2004;Loughman et al, 2015) calculates the ASI c vector at each iteration, using the extinction profile given by ( ) . The cross-section and aerosol scattering phase function are calculated from an assumed ASD using Mie theory assuming spherical droplets of sulfuric acid (H 2 SO 4 ).…”

A comparison of lognormal and gamma size distributions for characterizing the stratospheric aerosol phase function from OPC measurements

“…The OSIRIS version 5 (Bourassa et al, 2007) and the OMPS version 0.5 (Loughman et al, 2015;DeLand et al, 2016) aerosol extinction retrievals use a single-mode lognormal ASD to model P a (Θ), by using the median radii (r m ) and widths 70 (σ) given in Table 1. For both algorithms, P a (Θ) does not vary with altitude or location.…”