A Multiple Scattering Polarized Radiative Transfer Model: Application to HD 189733b

We present a multiple scattering vector radiative transfer model which produces disk integrated, full phase polarized light curves for reflected light from an exoplanetary atmosphere. We validate our model against results from published analytical and computational models and discuss a small number of cases relevant to the existing and possible near-future observations of the exoplanet HD 189733b. HD 189733b is arguably the most well observed exoplanet to date and the only exoplanet to be observed in polarized light, yet it is debated if the planet's atmosphere is cloudy or clear. We model reflected light from clear atmospheres with Rayleigh scattering, and cloudy or hazy atmospheres with Mie and fractal aggregate particles. We show that clear and cloudy atmospheres have large differences in polarized light as compared to simple flux measurements, though existing observations are insufficient to make this distinction. Futhermore, we show that atmospheres that are spatially inhomogeneous, such as being partially covered by clouds or hazes, exhibit larger contrasts in polarized light when compared to clear atmospheres. This effect can potentially be used to identify patchy clouds in exoplanets. Given a set of full phase polarimetric measurements, this model can constrain the geometric albedo, properties of scattering particles in the atmosphere and the longitude of the ascending node of the orbit. The model is used to interpret new polarimetric observations of HD 189733b in a companion paper.Comment: 13 pages, 13 figures. Accepted for publication in Ap

Retrieval of XCO2 from simulated Orbiting Carbon Observatory measurements using the fast linearized R-2OS radiative transfer model

In a recent paper, we introduced a novel technique to compute the polarization in a vertically inhomogeneous, scattering-absorbing medium using a two orders of scattering (2OS) radiative transfer (RT) model. The 2OS computation is an order of magnitude faster than a full multiple scattering scalar calculation and can be implemented as an auxiliary code to compute polarization in operational retrieval algorithms. In this paper, we employ the 2OS model for polarization in conjunction with a scalar RT model (Radiant) to simulate backscatter measurements in near infrared (NIR) spectral regions by space-based instruments such as the Orbiting Carbon Observatory (OCO). Computations are performed for six different sites and two seasons, representing a variety of viewing geometries, surface and aerosol types. The aerosol extinction (at 13000 cm^−1) was varied from 0 to 0.3. The radiance errors using the Radiant/2OS (R-2OS) RT model are an order of magnitude (or more) smaller than errors arising from the use of the scalar model alone. In addition, we perform a linear error analysis study to show that the errors in the retrieved column-averaged dry air mole fraction of CO2 (XCO2) using the R-2OS model are much lower than the “measurement” noise and smoothing errors appearing in the inverse model. On the other hand, we show that use of the scalar model alone induces X CO2 errors that could dominate the retrieval error budget

Quantifying the impact of aerosol scattering on the retrieval of methane from airborne remote sensing measurements

As a greenhouse gas with strong global warming potential, atmospheric methane (CH₄) emissions have attracted a great deal of attention. Although remote sensing measurements can provide information about CH₄ sources and emissions, accurate retrieval is challenging due to the influence of atmospheric aerosol scattering. In this study, imaging spectroscopic measurements from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) in the shortwave infrared are used to compare two retrieval techniques – the traditional matched filter (MF) method and the optimal estimation (OE) method, which is a popular approach for trace gas retrievals. Using a numerically efficient radiative transfer model with an exact single-scattering component and a two-stream multiple-scattering component, we also simulate AVIRIS-NG measurements for different scenarios and quantify the impact of aerosol scattering in the two retrieval schemes by including aerosols in the simulations but not in the retrievals. The presence of aerosols causes an underestimation of CH₄ in both the MF and OE retrievals; the biases increase with increasing surface albedo and aerosol optical depth (AOD). Aerosol types with high single-scattering albedo and low asymmetry parameter (such as water-soluble aerosols) induce large biases in the retrieval. When scattering effects are neglected, the MF method exhibits lower fractional retrieval bias compared to the OE method at high CH₄ concentrations (2–5 times typical background values) and is suitable for detecting strong CH₄ emissions. For an AOD value of 0.3, the fractional biases of the MF retrievals are between 1.3 % and 4.5 %, while the corresponding values for OE retrievals are in the 2.8 %–5.6 % range. On the other hand, the OE method is an optimal technique for diffuse sources (<1.5 times typical background values), showing up to 5 times smaller fractional retrieval bias (8.6 %) than the MF method (42.6 %) for the same AOD scenario. However, when aerosol scattering is significant, the OE method is superior since it provides a means to reduce biases by simultaneously retrieving AOD, surface albedo, and CH₄. The results indicate that, while the MF method is good for plume detection, the OE method should be employed to quantify CH₄ concentrations, especially in the presence of aerosol scattering

Observing Oceans in Tightly Packed Planetary Systems: Perspectives from Polarization Modeling of the TRAPPIST-1 System

The recently discovered TRAPPIST-1 system is exciting due to the possibility of several rocky, Earth-sized planets harboring liquid water on their surface. To assess the detectability of oceans on these planets, we model the disk-integrated phase curves and polarization signals for planets in this system for reflected starlight. We examine four cases: (1) dry planet, (2) cloud-covered planet, (3) planet with regional-scale oceans, and (4) planet with global oceans. Polarization signals are strongest for optically thin (≾ 0.1) atmospheres over widespread oceans, with the degree of polarization being up to 90% for a single planet or on the order of 100 parts per billion for the star–planet system. In cases where reflected light from different planets in a tightly packed system cannot be separated, observing in polarized light allows for up to a tenfold increase in star–planet contrast compared to photometric observations alone. However, polarization from other sources, such as atmospheric scattering and cloud variability, will pose major challenges to the detection of glint (specularly reflected starlight) polarization signals. Planned telescopes like LUVOIR may be capable of observing glint from Earth-like planets around Sun-like stars, and if equipped with a polarimeter can significantly improve our ability to detect and study oceans on rocky exoplanets

Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach

The Orbiting Carbon Observatories-2 and -3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands using the Atmospheric Carbon Observations from Space (ACOS) retrieval. Within ACOS, a Bayesian optimal estimation approach is employed to retrieve the column-averaged CO2 dry air mole fraction from these measurements. This retrieval requires a large number of polarized, multiple-scattering radiative transfer calculations for each iteration. These calculations take up the majority of the processing time for each retrieval and slow down the algorithm to the point that reprocessing data from the mission over multiple years becomes especially time consuming. To accelerate the radiative transfer model and, thereby, ease this bottleneck, we have developed a novel approach that enables modeling of the full spectra for the three OCO-2/3 instrument bands from radiances calculated at a small subset of monochromatic wavelengths. This allows for a reduction of the number of monochromatic calculations by a factor of 10, which can be achieved with radiance errors of less than 0.01% with respect to the existing algorithm and is easily tunable to a desired accuracy-speed trade-off. For the ACOS retrieval, this speeds up the over-retrievals by about a factor of two. The technique may be applicable to similar retrieval algorithms for other greenhouse gas sensors with large data volumes, such as GeoCarb, GOSAT-3, and CO2M

A Spectral Data Compression (SDCOMP) Radiative Transfer Model for High-Spectral-Resolution Radiation Simulations

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models