Abstract. The theoretical basis of the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) Version 1 aerosol extinction retrieval algorithm is presented. The algorithm uses an assumed bimodal lognormal aerosol size distribution to retrieve aerosol extinction profiles at 675 nm from OMPS LP radiance measurements. A first-guess aerosol extinction profile is updated by iteration using the Chahine nonlinear relaxation method, based on comparisons between the measured radiance profile at 675 nm and the radiance profile calculated by the Gauss–Seidel limb-scattering (GSLS) radiative transfer model for a spherical-shell atmosphere. This algorithm is discussed in the context of previous limb-scattering aerosol extinction retrieval algorithms, and the most significant error sources are enumerated. The retrieval algorithm is limited primarily by uncertainty about the aerosol phase function. Horizontal variations in aerosol extinction, which violate the spherical-shell atmosphere assumed in the version 1 algorithm, may also limit the quality of the retrieved aerosol extinction profiles significantly.

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, SASK-TRAN, 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;2. addition of multiple-scattering source function calculations at multiple solar zenith angles along the line of sight (LOS);3. introduction of variable surface properties along the limb LOS, with minimal effort required to add variable atmospheric properties along the LOS as well;4. addition of the ability to model multiple aerosol types within the model atmosphere.The model improvements 1 and 2 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 0.3 %, while total radiance errors generally decline from 10 % to 1-3 %. In all cases, the tangent height dependence of the GSLS radiance error is greatly reduced.

Abstract.The theoretical basis of the Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) Version 1 aerosol extinction (AE) retrieval algorithm is presented. The algorithm uses an assumed bi-modal log-normal aerosol size distribution to retrieve AE profiles at 675 nm from OMPS LP radiance measurements. A first-guess AE profile is updated by iteration using the Chahine non-linear relaxation method, based on comparisons between the measured radiance profile at 675 nm and the radiance 5 profile calculated by the Gauss-Seidel Limb Scattering (GSLS) radiative transfer model for a spherical-shell atmosphere. This algorithm is discussed in the context of previous limb-scattering AE retrieval algorithms, and the most significant error sources are enumerated. The retrieval algorithm is limited primarily by uncertainty about the aerosol phase function, and by horizontal variations in aerosol extinction, which violate the spherical-shell atmosphere assumed in the Version 1 algorithm.

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.

Abstract. A series of in situ measurements made by optical particle counters (OPCs) at Laramie, Wyoming, provides size-resolved stratospheric aerosol concentration data over the period 1971–2018. A subset of these data covering the period of 2008–2017 is analyzed in this study for the purpose of assessing the sensitivity of the stratospheric aerosol phase function to the aerosol size distribution (ASD) model used to fit the measurements. The two unimodal ASD models investigated are the unimodal lognormal (UMLN) and gamma distribution models, with the minimum χ2 method employed to assess how well each ASD fits the measurements. The aerosol phase function (Pa(Θ)) for each ASD is calculated using Mie theory and is compared to the Pa(Θ) derived from the Community Aerosol and Radiation Model for Atmospheres (CARMA) sectional aerosol microphysics module. Comparing the χ2 values for the fits at altitudes of 20 and 25 km shows that the UMLN distribution better represents the OPC measurements; however, the gamma distribution fits the CARMA model results better than the UMLN model when the CARMA model results are subsetted into the OPC measurement bins. Comparing phase functions derived from the UMLN distribution fit to OPC data with gamma distributions fit to CARMA model results at the location of the OPC measurements shows a satisfying agreement (±5 %) within the scattering angle range of limb sounding satellites. This uncertainty is considerably larger if the CARMA data are fit with a UMLN.