Atmospheric Retrieval Research Articles

DeepSAT4D: Deep learning empowers four-dimensional atmospheric chemical concentration and emission retrieval from satellite

<p>Accurate measurement of atmospheric chemicals is essential for understanding their impact on human health, climate, and ecosystems. Satellites provide a unique advantage by capturing data across the entire atmosphere, but their measurements often lack vertical details. Here, we introduce DeepSAT4D, an innovative method that efficiently reconstructs 4D chemical concentrations from satellite data. It achieves this by regenerating the dynamic evolution of vertical structure, intricately linked to complex atmospheric processes such as plume rise and transport, using advanced deep learning techniques. Its application with the Ozone Monitoring Instrument - Nitrogen Dioxide, a commonly used satellite product, demonstrates good agreement with ground-based monitoring sites in China from 2017 to 2021. Additionally, DeepSAT4D successfully captures emission reductions during 2020-pandemic shutdown. These findings emphasize DeepSAT4D��s potential to enhance our understanding of the complete atmospheric chemical composition and to provide improved assessments of its impact on human health and Earth��s ecosystem in the future.</p>

Parameterizing pressure–temperature profiles of exoplanet atmospheres with neural networks

Context. Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure–temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. Aims. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps P to T. Results. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. Conclusions. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.

Constraining Background N2 Inventories on Directly Imaged Terrestrial Exoplanets to Rule Out O2 False Positives

Direct imaging spectroscopy with future space-based telescopes will constrain terrestrial planet atmospheric composition and potentially detect biosignature gases. One promising indication of life is abundant atmospheric O2. However, various non-biological processes could also lead to O2 accumulation in the atmospheres of potentially habitable planets around Sun-like stars. In particular, the absence of non-condensible background gases such as N2 could result in appreciable H escape and abiotic O2 buildup, so identifying background atmosphere composition is crucial for contextualizing any O2 detections. Here, we perform retrievals on simulated directly imaged terrestrial planets using rfast, a new exoplanet atmospheric retrieval suite with direct imaging analysis capabilities. By simulating Earth-analog retrievals for varied atmospheric compositions, cloud properties, and surface pressures, we determine what wavelength range, spectral resolution, and signal-to-noise ratio (S/N) are necessary to constrain background gases’ identity and abundance. We find N2 backgrounds can be uniquely identified with S/N ∼ 20 observations, provided that wavelength coverage extends beyond ∼1.6 μm to rule out CO-dominated atmospheres. Additionally, there is a low probability of O2-dominated atmospheres due to an O2–N2 degeneracy that is only totally ruled out at S/N ∼ 40. If wavelength coverage is limited to 0.2–1.1 μm, then although all other cosmochemically plausible backgrounds can be readily excluded, N2 and CO backgrounds cannot be distinguished. Overall, our simulated retrievals and associated integration time calculations suggest that near-infrared coverage to at least 1.6 μm and apertures approaching 8 m are needed to confidently rule out O2 biosignature false positives within feasible integration times.

Spatially constrained atmosphere and surface retrieval for imaging spectroscopy

Imaging spectrometers provide important Earth surface data for terrestrial and aquatic ecology, hydrology, geology through retrieved surface reflectance spectra. Surface reflectance is generally recovered from measured radiance through model inversions of the surface and atmospheric system. Most retrieval approaches treat all pixels independently and ignore spatial correlations in the atmosphere for neighboring pixels, resulting in surface-related biases in the retrieved surface and atmospheric parameters. In this work, we present a mathematical framework to more accurately retrieve surface reflectance and atmospheric parameters by leveraging the expected spatial smoothness of the atmosphere. This framework retrieves the full spatial–spectral reflectance data cube in a computationally efficient manner and is to our knowledge the first approach capable of accurately finding this global solution for large imaging spectroscopy datasets. We implement this mathematical framework in an example proposed algorithm which we call spatially constrained optimal estimation (SCOE). We show that SCOE improves the overall surface reflectance retrieval error, reduces surface-related biases in the retrieved surface reflectance and atmospheric parameters, and is more robust to noise than a traditional pixel-by-pixel approach and an operational empirical line approach in simulated and experimental results. This reduction in surface reflectance error will impact science data products across application areas, making imaging spectroscopy algorithms more robust for global acquisitions.

Single field-of-view sounder atmospheric product retrieval algorithm: establishing radiometric consistency for hyper-spectral sounder retrievals

Abstract. The single field-of-view (SFOV) sounder atmospheric product (SiFSAP) retrieval algorithm has been developed to address the need to retrieve high-spatial-resolution atmospheric data products from hyper-spectral sounders and ensure the radiometric consistency between the retrieved properties and measured spectral radiances. It is based on an integrated optimal-estimation inversion scheme that processes data from the satellite-based synergistic microwave (MW) and infrared (IR) spectral measurements from advanced sounders. The retrieval system utilizes the principal component radiative transfer model (PCRTM), which performs radiative transfer calculations monochromatically and includes accurate cloud-scattering simulations. SiFSAP includes temperature, water vapor, surface skin temperature and emissivity, cloud height and microphysical properties, and concentrations of essential trace gases for each SFOV at a native instrument spatial resolution. Error estimations are provided based on a rigorous analysis for uncertainty propagation from the top-of-atmosphere (TOA) spectral radiances to the retrieved geophysical properties. As a comparison, the spatial resolution for the traditional hyper-spectral sounder retrieval products is much coarser than the native resolution of the instruments due to the common use of the “cloud-clearing” technique to compensate for the lack of cloud-scattering simulation in the forward model. The degraded spatial resolution in traditional cloud-clearing sounder retrieval products limits their applications for capturing meteorological or climate signals at finer spatial scales. Moreover, a rigorous uncertainty propagation estimation needed for long-term climate trend studies cannot be given due to the lack of direct radiative transfer relationships between the observed TOA radiances and the retrieved geophysical properties. With the advantages of the higher spatial resolution; the simultaneous retrieval of atmospheric, cloud, and surface properties using all available spectral information; and the establishment of “radiance closure” in the sounder spectral measurements, the SiFSAP provides additional information needed for various weather and climate studies and applications using sounding observations. This paper gives an overview of the SiFSAP retrieval algorithm and assessment of SiFSAP atmospheric temperature, water vapor, clouds, and surface products derived from the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) data.

Preliminary Assessment of BDS Radio Occultation Retrieval Quality and Coverage Using FY-3E GNOS II Measurements

The FengYun-3E Global Navigation Satellite System (GNSS) occultation sounder II (FY-3E GNOS II) was launched on 5 July 2021. For the first time, based on the new GNOS II sensor, this mission realizes radio occultation (RO) and reflectometry observations using the navigation signals from the third-generation BeiDou System (BDS-3), and it is hence important to assess and analyze the BDS-3 remote sensing performances relative to other systems. Here, we assessed FY-3E GNOS II RO atmospheric retrievals by inter-comparing with corresponding data from the NCEP FNL global atmospheric analysis and FY-3D GNOS mission. The GNOS RO data quality and consistency of the different FY-3 meteorological satellites, i.e., FY-3D and FY-3E, as well as different GNSS systems (GPS, BDS-2, BDS-3) were analyzed. We find that the FY-3E GNOS II RO data exhibit better quality than FY-3D GNOS, particularly in the number, penetration height toward surface, and global coverage by BDS RO profiles, due to the integration of BDS-2 and BDS-3. Additionally, comparing with co-located NCEP FNL analysis profiles, the mean difference (and standard deviation) of the FY-3E GNOS II RO atmospheric refractivity profile retrievals is found to be smaller than 0.2% (and 1%), in the upper troposphere and lower stratosphere, from 5 to 30 km, and remains consistent at this accuracy and precision level with the FY-3D GNOS RO data. These features provide clear evidence for a high utility of the new GNOS II RO data for weather and climate research and applications.

ELemental abundances of Planets and brown dwarfs Imaged around Stars (ELPIS). I. Potential Metal Enrichment of the Exoplanet AF Lep b and a Novel Retrieval Approach for Cloudy Self-luminous Atmospheres

AF Lep A+b is a remarkable planetary system hosting a gas-giant planet that has the lowest dynamical mass among directly imaged exoplanets. We present an in-depth analysis of the atmospheric composition of the star and planet to probe the planet’s formation pathway. Based on new high-resolution spectroscopy of AF Lep A, we measure a uniform set of stellar parameters and elemental abundances (e.g., [Fe/H] = −0.27 ± 0.31 dex). The planet’s dynamical mass ( M Jup) and orbit are also refined using published radial velocities, relative astrometry, and absolute astrometry. We use petitRADTRANS to perform chemically consistent atmospheric retrievals for AF Lep b. The radiative–convective equilibrium temperature profiles are incorporated as parameterized priors on the planet’s thermal structure, leading to a robust characterization for cloudy self-luminous atmospheres. This novel approach is enabled by constraining the temperature–pressure profiles via the temperature gradient , a departure from previous studies that solely modeled the temperature. Through multiple retrievals performed on different portions of the 0.9–4.2 μm spectrophotometry, along with different priors on the planet’s mass and radius, we infer that AF Lep b likely possesses a metal-enriched atmosphere ([Fe/H] > 1.0 dex). AF Lep b’s potential metal enrichment may be due to planetesimal accretion, giant impacts, and/or core erosion. The first process coincides with the debris disk in the system, which could be dynamically excited by AF Lep b and lead to planetesimal bombardment. Our analysis also determines T eff ≈ 800 K, dex, and the presence of silicate clouds and disequilibrium chemistry in the atmosphere. Straddling the L/T transition, AF Lep b is thus far the coldest exoplanet with suggested evidence of silicate clouds.

Impacts of high-contrast image processing on atmospheric retrievals

Many post-processing algorithms have been developed in order to better separate the signal of a companion from the bright light of the host star, but the effect of such algorithms on the shape of exoplanet spectra extracted from integral field spectrograph data is poorly understood. The resulting spectra are affected by noise that is correlated in wavelength space due to both optical and data processing effects. Within the framework of Bayesian atmospheric retrievals, we aim to understand how these correlations and other systematic effects impact the inferred physical parameters. We consider three algorithms (KLIP, PynPoint, and ANDROMEDA), optimising the choice of algorithmic parameters using a series of injection tests on archival SPHERE and GPI data of the HR 8799 system. The wavelength-dependent covariance matrix was calculated to provide a measure of instrumental and algorithmic systematics. We perform atmospheric retrievals using petit RADTRANS on optimally extracted spectra to measure how these data processing systematics influence the retrieved parameter distributions. The choice of data processing algorithm and parameters significantly impact the accuracy of retrieval results, with the mean posterior parameter bias ranging from 1 to 3 σ from the true input parameters. Including the full covariance matrix in the likelihood improves the accuracy of the inferred parameters, and cannot be accounted for using ad hoc scaling parameters in the retrieval framework. Using the Bayesian information criterion and other statistical measures as heuristic goodness-of-fit metrics, the retrievals including the full covariance matrix are favoured when compared to using only the diagonal elements.

CROCODILE

Context. The investigation of the atmospheres of closely separated, directly imaged gas giant exoplanets is challenging due to the presence of stellar speckles that pollute their spectrum. To remedy this, the analysis of medium- to high-resolution spectroscopic data via cross-correlation with spectral templates (cross-correlation spectroscopy) is emerging as a leading technique. Aims. We aim to define a robust Bayesian framework combining, for the first time, three widespread direct-imaging techniques, namely photometry, low-resolution spectroscopy, and medium-resolution cross-correlation spectroscopy in order to derive the atmospheric properties of close-in directly imaged exoplanets. Current atmospheric characterisation frameworks are indeed either not compatible with all three observing techniques or they lack the commitment to efficient sampling strategies that allow high-dimensional forward models. Methods. Our framework CROCODILE (cross-correlation retrievals of directly imaged self-luminous exoplanets) naturally combines the three techniques by adopting adequate likelihood functions. To validate our routine, we simulated observations of gas giants similar to the well-studied β Pictoris b planet and we explored the parameter space of their atmospheres to search for potential biases. Results. We obtain more accurate measurements of atmospheric properties when combining photometry, low- and medium-resolution spectroscopy into atmospheric retrievals than when using the techniques separately as is usually done in the literature. Indeed, the combined fit is, on average, 20% more accurate than fitting only medium-resolution cross-correlation spectroscopy. We find that medium-resolution (R ≈ 4000) K-band cross-correlation spectroscopy alone is not suitable to constrain the atmospheric properties of our synthetic datasets; however, this problem disappears when simultaneously fitting photometry throughout the Y and M bands and low-resolution (R ≈ 60) spectroscopy between the Y and H bands. Our thorough testing demonstrates that free chemistry is a suitable forward model to retrieve the atmospheric thermal and chemical properties of cloudless gas giants at chemical equilibrium. Conclusions. CROCODILE provides a robust statistical framework to interpret medium-resolution spectroscopic data of close-in directly imaged exoplanets, where speckles originating from stellar stray light render the extraction of the continuum difficult. Our framework allows the atmospheric characterisation of directly imaged exoplanets using the high-quality spectral data that will be provided by the new generation of instruments such as the Enhanced Resolution Imager and Spectrograph (ERIS) at the Very Large Telescope, the Mid-Infrared Instrument (MIRI) aboard the James Webb Space Telescope, and in the future the Mid-infrared ELT Imager and Spectrograph (METIS) at the Extremely Large Telescope.

JWST-TST DREAMS: Quartz Clouds in the Atmosphere of WASP-17b

Clouds are prevalent in many of the exoplanet atmospheres that have been observed to date. For transiting exoplanets, we know if clouds are present because they mute spectral features and cause wavelength-dependent scattering. While the exact composition of these clouds is largely unknown, this information is vital to understanding the chemistry and energy budget of planetary atmospheres. In this work, we observe one transit of the hot Jupiter WASP-17b with JWST’s Mid-Infrared Instrument Low Resolution Spectrometer and generate a transmission spectrum from 5 to 12 μm. These wavelengths allow us to probe absorption due to the vibrational modes of various predicted cloud species. Our transmission spectrum shows additional opacity centered at 8.6 μm, and detailed atmospheric modeling and retrievals identify this feature as SiO2(s) (quartz) clouds. The SiO2(s) clouds model is preferred at 3.5–4.2σ versus a cloud-free model and at 2.6σ versus a generic aerosol prescription. We find the SiO2(s) clouds are composed of small ∼0.01 μm particles, which extend to high altitudes in the atmosphere. The atmosphere also shows a depletion of H2O, a finding consistent with the formation of high-temperature aerosols from oxygen-rich species. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS).

VLTI/GRAVITY Observations and Characterization of the Brown Dwarf Companion HD 72946 B

Tension remains between the observed and modeled properties of substellar objects, but objects in binary orbits, with known dynamical masses, can provide a way forward. HD 72946 B is a recently imaged brown dwarf companion to a nearby, solar-type star. We achieve ∼100 μas relative astrometry of HD 72946 B in the K band using VLTI/GRAVITY, unprecedented for a benchmark brown dwarf. We fit an ensemble of measurements of the orbit using orbitize! and derive a strong dynamical mass constraint M B = 69.5 ± 0.5 M Jup assuming a strong prior on the host star mass M A = 0.97 ± 0.01 M ⊙ from an updated stellar analysis. We fit the spectrum of the companion to a grid of self-consistent BT-Settl-CIFIST model atmospheres, and perform atmospheric retrievals using petitRADTRANS. A dynamical mass prior only marginally influences the sampled distribution of effective temperature, but has a large influence on the surface gravity and radius, as expected. The dynamical mass alone does not strongly influence retrieved pressure–temperature or cloud parameters within our current retrieval setup. Independently of the cloud prescription and prior assumptions, we find agreement within ±2σ between the C/O of the host (0.52 ± 0.05) and brown dwarf (0.43–0.63), as expected from a molecular cloud collapse formation scenario, but our retrieved metallicities are implausibly high (0.6–0.8) in light of the excellent agreement of the data with the solar-abundance model grid. Future work on our retrieval framework will seek to resolve this tension. Additional study of low surface gravity objects is necessary to assess the influence of a dynamical mass prior on atmospheric analysis.

Retrieval of the dayside atmosphere of WASP-43b with CRIRES+

Accurately estimating the C/O ratio of hot Jupiter atmospheres is a promising pathway towards understanding planet formation and migration, as well as the formation of clouds and the overall atmospheric composition. The atmosphere of the hot Jupiter WASP-43b has been extensively analysed using low-resolution observations with HST and Spitzer, but these previous observations did not cover the K band, which hosts prominent spectral features of major carbon-bearing species such as CO and CH4. As a result, the ability to establish precise constraints on the C/O ratio was limited. Moreover, the planet has not been studied at high spectral resolution, which can provide insights into the atmospheric dynamics. In this study, we present the first high-resolution dayside spectra of WASP-43b with the new CRIRES+ spectrograph. By observing the planet in the K band, we successfully detected the presence of CO and provide evidence for the existence of H2O using the cross-correlation method. This discovery represents the first direct detection of CO in the atmosphere of WASP-43b. Furthermore, we retrieved the temperature-pressure profile, abundances of CO and H2O, and a super-solar C/O ratio of 0.78 by applying a Bayesian retrieval framework to the data. Our findings also shed light on the atmospheric characteristics of WASP-43b. We found no evidence for a cloud deck on the dayside, and recovered a line broadening indicative of an equatorial super-rotation corresponding to a jet with a wind speed of ~5kms−1, matching the results of previous forward models and low-resolution atmospheric retrievals for this planet.

Earth as a Transiting Exoplanet: A Validation of Transmission Spectroscopy and Atmospheric Retrieval Methodologies for Terrestrial Exoplanets

The James Webb Space Telescope (JWST) will enable the search for and characterization of terrestrial exoplanet atmospheres in the habitable zone via transmission spectroscopy. However, relatively little work has been done to use solar system data, where ground truth is known, to validate spectroscopic retrieval codes intended for exoplanet studies, particularly in the limit of high resolution and high signal-to-noise ratio (S/N). In this work, we perform such a validation by analyzing a high-S/N empirical transmission spectrum of Earth using a new terrestrial exoplanet atmospheric retrieval model with heritage in solar system remote sensing and gaseous exoplanet retrievals. We fit the Earth’s 2–14 μm transmission spectrum in low resolution (R = 250 at 5 μm) and high resolution (R = 100,000 at 5 μm) under a variety of assumptions about the 1D vertical atmospheric structure. In the limit of noiseless transmission spectra, we find excellent agreement between model and data (deviations <10%) that enable the robust detection of H2O, CO2, O3, CH4, N2, N2O, NO2, HNO3, CFC-11, and CFC-12 thereby providing compelling support for the detection of habitability, biosignature, and technosignature gases in the atmosphere of the planet using an exoplanet-analog transmission spectrum. Our retrievals at high spectral resolution show a marked sensitivity to the thermal structure of the atmosphere, trace gas abundances, density-dependent effects, such as collision-induced absorption and refraction, and even hint at 3D spatial effects. However, we used synthetic observations of TRAPPIST-1e to verify that the use of simple 1D vertically homogeneous atmospheric models will likely suffice for JWST observations of terrestrial exoplanets transiting M dwarfs.

Modelling the effect of 3D temperature and chemistry on the cross-correlation signal of transiting ultra-hot Jupiters: a study of five chemical species on WASP-76b

ABSTRACT Ultra-hot Jupiters are perfect targets for transmission spectroscopy. However, their atmospheres feature strong spatial variations in temperature, chemistry, dynamics, cloud coverage, and scale height. This makes transit observations at high spectral resolution challenging to interpret. In this work, we model the cross-correlation signal of five chemical species – Fe, CO, H2O, OH, and TiO – on WASP-76b, a benchmark ultra-hot Jupiter. We compute phase-dependent high-resolution transmission spectra of three-dimensional (3D) SPARC/MITgcm models. The spectra are obtained with gCMCRT, a 3D Monte-Carlo radiative-transfer code. We find that, on top of atmospheric dynamics, the phase-dependent Doppler shift of the absorption lines in the planetary rest frame is shaped by the combined effect of planetary rotation and the unique 3D spatial distribution of chemical species. For species probing the dayside (e.g. refractories or molecules like CO and OH), the two effects act in tandem, leading to increasing blueshifts with orbital phase. For species that are depleted on the dayside (e.g. H2O and TiO), the two effects act in an opposite manner, and could lead to increasing redshifts during the transit. This behaviour yields species-dependent offsets from a planet’s expected Kp value that can be much larger than planetary wind speeds. The offsets are usually negative for refractory species. We provide an analytical formula to estimate the size of a planet’s Kp offsets, which can serve as a prior for atmospheric retrievals. We conclude that observing the phase-resolved absorption signal of multiple species is key to constraining the 3D thermochemical structure and dynamics of ultra-hot Jupiters.

Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning

Understanding differences between substellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the random forest supervised machine-learning method, we study the information content of 14 previously published model grids of brown dwarfs (from 1997 to 2021). The random forest method allows us to analyze the predictive power of these model grids, as well as interpret data within the framework of Approximate Bayesian Computation. Our curated data set includes three benchmark brown dwarfs (Gl 570D, ϵ Indi Ba, and Bb) as well as a sample of 19 L and T dwarfs; this sample was previously analyzed by Lueber et al. using traditional Bayesian methods (nested sampling). We find that the effective temperature of a brown dwarf can be robustly predicted independent of the model grid chosen for the interpretation. However, inference of the surface gravity is model-dependent. Specifically, the BT-Settl, Sonora Bobcat, and Sonora Cholla model grids tend to predict –4 (cgs units) even after data blueward of 1.2 μm have been disregarded to mitigate for our incomplete knowledge of the shapes of alkali lines. Two major, longstanding challenges associated with understanding the influence of clouds in brown dwarf atmospheres remain: our inability to model them from first principles and also to robustly validate these models.

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Fengyun-3E (FY-3E)/Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) is an extension Fengyun-3D (FY-3D)/HIRAS-I. It is crucial to fully explore and analyze the detection capabilities of these two instruments for atmospheric gas composition. Based on the observed spectral data from the infrared hyperspectral detection instruments FY-3D/HIRAS-I and FY-3E/HIRAS-II, simulated radiance data and Jacobian matrices are obtained using the Rapid Radiative Transfer Model RTTOV (Radiative Transfer for TOVS (TIROS Operational Vertical Sounder)). By perturbing temperature (T), surface temperature (Tsurf), water vapor (H2O), ozone (O3), carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and nitrous oxide (N2O), the brightness temperature differences before and after the perturbations are calculated to analyze the sensitivity of temperature and various atmospheric gas components. The Improved Optimal Sensitivity Profile (OSP) algorithm is used to select the channels for atmospheric gas retrieval. The observation error covariance and background error covariance matrices are calculated, and then the information capacity is calculated, specifically the degrees of freedom for signal(DFS) and the entropy reduction (ER). Based on this, a comparative analysis is conducted on the information capacity of atmospheric water vapor and ozone components contained in the hyperspectral detection data from HIRAS-I and HIRAS-II instruments, respectively, to explore the retrieval capabilities of the two instruments for atmospheric gas components. We selected clear-sky data from the African oceanic region and the Chinese Yangtze River Delta terrestrial region for quantitative analysis of the information capacity of HIRAS-I and HIRAS-II. The results show that FY-3D/HIRAS-I and FY-3E/HIRAS-II exhibit different sensitivities to atmospheric gas components. In different experimental regions, temperature and water vapor show the most dramatic sensitivity changes, followed by ozone, methane, and nitrous oxide, while carbon monoxide and carbon dioxide exhibit the lowest variability. Regarding channel selection, HIRAS-II identifies more gas channels compared to HIRAS-I. The experiments concluded that HIRAS-II has a significantly higher information capacity than HIRAS-I, and the information capacity of atmospheric gas components varies across different experimental regions. Water vapor and ozone exhibit the highest information capacity, followed by nitrous oxide and methane, while carbon monoxide and carbon dioxide demonstrate the lowest capacity. The H2O ER (DFS) contained in FY-3E/HIRAS-II is 1.51 (0.35) higher than that in FY-3D/HIRAS-I, the O3 ER (DFS) in FY-3E/HIRAS-II is 1.51 (0.36) higher than that in FY-3D/HIRAS-I, while the N2O ER (DFS) in FY-3E/HIRAS-II is 0.17 (0.19) higher and the CH4 ER (DFS) is 0.07 (0.04) higher than that in FY-3D/HIRAS-I.

Improving atmospheric CO2 retrieval based on the collaborative use of Greenhouse gases Monitoring Instrument and Directional Polarimetric Camera sensors on Chinese hyperspectral satellite GF5-02

ABSTRACT The Greenhouse Gas Monitoring Instrument (GMI) onboard the Chinese hyperspectral satellite GF5–02 can provide abundant observations of global atmospheric CO2, which plays an important role in climate research. CO2 retrieval precision is the key to determining the application value of the GMI. To reduce the influence of atmospheric scattering on retrieval, we combined the Directional Polarimetric Camera (DPC) data on the same satellite to improve the anti-interference ability of GMI CO2 retrieval and ensure its retrieval precision. To realize the reliability and feasibility of the collaborative use of the GMI and DPC, this paper designs the pointing registration method of the GMI based on coastline observations, the spatial resolution matching method and the collaborative cloud screening method of the GMI and DPC observations. Combined with the DPC, which supplied the spectral data and aerosol product, the retrieval ability of the coupled bidirectional reflectance distribution function CO2 retrieval (CBCR) method developed for GMI CO2 retrieval was improved, with the retrieval efficiency of CO2 products increasing by 27%, and the CO2 retrieval precision increasing from 3.3 ppm to 2.7 ppm. Moreover, collaborative use not only guaranteed the GMI’s ability to detect global and area CO2 concentration distribution characteristics, such as significant concentration differences between the Northern and Southern Hemispheres in winter and high CO2 concentrations in urban agglomeration areas caused by human activities, but also extended the GMI’s potential for monitoring anomalous events, such as the Tonga volcanic eruption.

Grid-based Atmospheric Retrievals for Reflected-light Spectra of Exoplanets Using PSGnest

Techniques to retrieve the atmospheric properties of exoplanets via direct observation of their reflected light have often been limited in scope owing to computational constraints imposed by the forward-model calculations. We have developed a new set of techniques that significantly decrease the time required to perform a retrieval while maintaining accurate results. We constructed a grid of 1.4 million precomputed geometric albedo spectra valued at discrete sets of parameter points. Spectra from this grid are used to produce models for a fast and efficient nested sampling routine called PSGnest. Beyond the upfront time to construct a spectral grid, the amount of time to complete a full retrieval using PSGnest is on the order of seconds to minutes using a personal computer. An extensive evaluation of the error induced from interpolating intermediate spectra from the grid indicates that this bias is insignificant compared to other retrieval error sources, with an average coefficient of determination between interpolated and true spectra of 0.998. We apply these new retrieval techniques to help constrain the optimal bandpass centers for retrieving various atmospheric and bulk parameters from a LuvEx-type mission observing several planetary archetypes. We show that spectral observations made using a 20% bandpass centered at 0.73 μm can be used alongside our new techniques to make detections of H2O and O2 without the need to increase observing time beyond what is necessary for a signal-to-noise ratio of 10. The methods introduced here will enable robust studies of the capabilities of future observatories to characterize exoplanets.

Brown Dwarf Retrievals on FIRE!: Atmospheric Constraints and Lessons Learned from High Signal-to-noise Medium-resolution Spectroscopy of a T9 Dwarf

Brown dwarf spectra offer vital testbeds for our understanding of the chemical and physical processes that sculpt substellar atmospheres. Recently, atmospheric retrieval approaches have been successfully applied to low-resolution (R ∼ 100) spectra of L, T, and Y dwarfs, yielding constraints on the chemical abundances and temperature structures of these atmospheres. Medium-resolution (R ∼ 103) spectra of brown dwarfs offer additional insight, as molecular features are more easily disentangled and the thermal structure of the upper atmosphere is better probed. We present results from a GPU-based retrieval analysis of a high signal-to-noise, medium-resolution (R ∼ 6000) FIRE spectrum from 0.85 to 2.5 μm of the T9 dwarf UGPS J072227.51–054031.2. At 60× higher spectral resolution than previous brown dwarf retrievals, a number of novel challenges arise. We examine the effect of different opacity sources, in particular for CH4. Furthermore, we find that flaws in the data like errors from order stitching can bias our constraints. We compare these retrieval results to those for an R ∼ 100 spectrum of the same object, revealing how constraints on atmospheric abundances and temperatures improve by an order of magnitude or more with increased spectral resolution. In particular, we can constrain the abundance of H2S, which is undetectable at lower spectral resolution. While these medium-resolution retrievals offer the potential of precise, stellar-like constraints on atmospheric abundances (∼0.02 dex), our retrieved radius is unphysically small ( R Jup), indicating shortcomings with our modeling framework. This work is an initial investigation into brown dwarf retrievals at medium spectral resolution, offering guidance for future ground-based studies and JWST observations.

Confirmation of Subsolar Metallicity for WASP-77Ab from JWST Thermal Emission Spectroscopy

We present the dayside thermal emission spectrum of WASP-77Ab from 2.8 to 5.2 μm as observed with the NIRSpec instrument on the James Webb Space Telescope (JWST). WASP-77Ab was previously found to have a subsolar metallicity and a solar carbon-to-oxygen (C/O) ratio from H2O and CO absorption lines detected using high-resolution spectroscopy. By performing atmospheric retrievals on the JWST spectrum assuming chemical equilibrium, we find a subsolar metallicity [M/H] = and C/O ratio . We identify H2O and CO and constrain their abundances, and we find no CO2 in the spectrum. The JWST and high-resolution spectroscopy results agree within ∼1σ for the metallicity and within 1.8σ for the C/O ratio. However, our results fit less well in the picture painted by the shorter wavelength spectrum measured by Hubble Space Telescope Wide Field Camera 3. Comparing the JWST thermal emission spectra of WASP-77Ab and HD 149026b shows that both hot Jupiters have nearly identical brightness temperatures in the near-infrared but distinctly different atmospheric compositions. Our results reaffirm high-resolution spectroscopy as a powerful and reliable method to measure molecular abundances. Our results also highlight the incredible diversity of hot Jupiter atmospheric compositions.