Revised and extended benchmark results for Rayleigh scattering of sunlight in spherical atmospheres

Korkin, Sergey; Yang, Eun-Su; Spurr, Robert; Emde, Claudia; Krotkov, Nickolay A.; Vasilkov, A. P.; Haffner, D. P.; Mok, Jungbin; Lyapustin, Alexei

doi:10.1016/j.jqsrt.2020.107181

Cited by 15 publications

(5 citation statements)

References 93 publications

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“…Sophisticated variance reduction methods have been implemented (Buras and Mayer, 2011) which enable the calculation of radiances for scattering media characterized by strongly peaked phase functions without any approximations. The MYSTIC model has been validated in various model intercomparison studies (Cahalan et al, 2005;Kokhanovsky et al, 2010a;Emde et al, 2015Emde et al, , 2018Korkin et al, 2020;Zawada et al, 2021) and by comparison to benchmark results and always agreed well to other participating radiative transfer codes.…”

Section: Three-dimensional Radiative Transfer Model Mysticmentioning

confidence: 75%

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 1: Synthetic dataset for validation of trace gas retrieval algorithms

Emde¹,

Kylling

et al. 2022

Atmos. Meas. Tech.

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Abstract. Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

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Section: Three-dimensional Radiative Transfer Model Mysticmentioning

confidence: 75%

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 1: Synthetic dataset for validation of trace gas retrieval algorithms

Emde¹,

Kylling

et al. 2022

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sophisticated variance reduction methods have been implemented (Buras and Mayer, 2011) which enable the calculation of unbiased radiances for scattering media characterized by strongly peaked phase functions without any approximations. The MYSTIC model has been validated in various model intercomparison studies (Cahalan et al, 2005;Kokhanovsky et al, 2010a;Emde et al, 2015Emde et al, , 2018Korkin et al, 2020;Zawada et al, 2021) and by comparison to benchmark results, and always agreed perfectly to the core of participating radiative transfer codes.…”

Section: Introductionmentioning

confidence: 83%

Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders – Part I: Synthetic dataset for validation of trace gas retrieval algorithms

Emde¹,

Kylling

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC. The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMF) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: The first includes simple box-clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical for central Europe such as shallow cumulus, convective cloud cells, cirrus, and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analyzed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behavior of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

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“…Following Ceamanos et al (2021), SEVIRI radiances were recalibrated to account for the systematic biases found by Meirink et al (2013) with respect to collocated near-nadir reflectance measurements from MODIS. Furthermore, SEVIRI measurements with a value of SZA or VZA higher than 75° were discarded to mitigate the lower accuracy of the RTM used in iAERUS-GEO at these geometries and the neglect of the sphericity of the Earth (Korkin et al, 2020). Finally, the greater difficulty of retrieving AOD in the presence of intense sun glint is solved by not processing ocean measurements with a sun glint angle (γ) lower than 35°, where γ = arccos(𝜇 % 𝜇 1 − sin 𝜃 % sin 𝜃 1 cos 𝜑).…”

Section: Input Datamentioning

confidence: 99%

Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – Estimation of 15-min AOD from MSG/SEVIRI and evaluation with reference data

Ceamanos

Six

Moparthy

et al. 2023

Preprint

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Abstract. Geostationary meteorological satellites are unique tools to monitor atmospheric aerosols from space. The observation of the Earth several times per hour allows this type of imaging systems to provide high temporal resolution observations of these suspended particles which are of interest for research topics including air quality, numerical weather prediction, and volcanic risk management. However, some challenges need to be addressed to achieve the sub-daily retrieval of aerosol properties mainly due to the varying sensitivity of geostationary imagers to aerosols during the day. In this article we propose a new algorithm named iAERUS-GEO (instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit) that estimates the diurnal evolution of aerosol optical depth (AOD) from the Meteosat Second Generation (MSG) satellite. This is achieved by the use of an optimal estimation method combined with several aerosol models and other features including the daily retrieval of the surface reflectance directionality using Kalman filtering. AOD estimates provided by iAERUS-GEO every 15 minutes —the acquisition frequency of the Spinning Enhanced Visible Infra-Red Imager (SEVIRI) on MSG— are assessed with collocated reference aerosol observations. First, comparison to AERONET ground-based data proves the overall satisfactory accuracy of iAERUS-GEO with the exception of some higher biases found over bright surfaces and for high scattering angles. The confidence measure provided by iAERUS-GEO is proved useful to filter these less satisfactory retrievals that generally arise due to a low information content on aerosols provided by SEVIRI. Second, comparison to the GRASP/POLDER satellite product shows similar scores for the two aerosol data sets, with a significantly larger number of retrievals for iAERUS-GEO. This added value —which we illustrate here by inspecting the sub-daily variation of AOD over selected regions— allows geostationary satellites to break the temporal barrier set by traditional aerosol remote sensing from the low Earth orbit. Furthermore, the aerosol retrievals presented in this work are expected to be improved in the near future thanks to the enhanced sensing capabilities of the upcoming Meteosat Third Generation-Imager mission.

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Revised and extended benchmark results for Rayleigh scattering of sunlight in spherical atmospheres

Cited by 15 publications

References 93 publications

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 1: Synthetic dataset for validation of trace gas retrieval algorithms

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 1: Synthetic dataset for validation of trace gas retrieval algorithms

Impact of 3D Cloud Structures on the Atmospheric Trace Gas Products from UV-VIS Sounders – Part I: Synthetic dataset for validation of trace gas retrieval algorithms

Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – Estimation of 15-min AOD from MSG/SEVIRI and evaluation with reference data

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