Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

Schreier, Franz; García, Sebastián Gimeno; Hochstaffl, Philipp; Städt, Steffen

doi:10.3390/atmos10050262

Cited by 33 publications

(29 citation statements)

References 98 publications

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“…In this paper, the focus of our analysis is finding the signature of thermal inversion agents in the transmission spectrum of KELT-20b and confirming the previous detections in Casasayas- Barris et al (2019). First, we calculated the cross-section of the atomic species (Fe I, Fe II, Ca II, Na I, Ti I, Ti II, V I, V II) using PYTHON for Computational ATmospheric Spectroscopy (line-by-line), PY4CATS (Schreier et al 2019). The line-by-line database was taken from Kurucz (2018) and extracted into PY4CATS-supported format.…”

Section: Modeling the Transmission Spectrumsupporting

confidence: 76%

Searching for thermal inversion agents in the transmission spectrum of KELT-20b/MASCARA-2b: detection of neutral iron and ionised calcium H&K lines

Nugroho

Gibson

Mooij

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We analyse the transmission spectra of KELT-20b/MASCARA-2b to search for possible thermal inversion agents. The data consist of three transits obtained using HARPSN and one using CARMENES. We removed stellar and telluric lines before cross-correlating the residuals with spectroscopic templates produced using a 1D plane-parallel model, assuming an isothermal atmosphere and chemical equilibrium at solar metallicity. Using a likelihood-mapping method, we detect Fe i at > 13σ, Ca ii H$\&$K at > 6σ and confirm the previous detections of Fe ii, Ca ii IR Triplet, and Na i D. The detected signal of Fe i is shifted by −3.4 ± 0.4 km s−1 from the planetary rest frame, which indicates a strong day–night wind. Our likelihood-mapping technique also reveals that the absorption features of the detected species extend to different altitudes in the planet’s atmosphere. Assuming that the line lists are accurate, we do not detect other potential thermal inversion agents (NaH, MgH, AlO, SH, CaO, VO, FeH, and TiO) suggesting that non-chemical equilibrium mechanisms (e.g. a cold-trap) might have removed Ti- and V-bearing species from the upper atmosphere. Our results, therefore, show that KELT-20b/MASCARA-2b cannot possess an inversion layer caused by a TiO/VO-related mechanism. The presence of an inversion layer would therefore likely be caused by metal atoms such as Fe i and Fe ii. Finally, we report a double-peak structure in the Fe i signal in all of our data sets that could be a signature of atmospheric dynamics. However, further investigation is needed to robustly determine the origin of the signal.

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Section: Modeling the Transmission Spectrumsupporting

confidence: 76%

Searching for thermal inversion agents in the transmission spectrum of KELT-20b/MASCARA-2b: detection of neutral iron and ionised calcium H&K lines

Nugroho

Gibson

Mooij

et al. 2020

Monthly Notices of the Royal Astronomical Society

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“…The gaseous absorptions for the O 2 A-and CO 2 bands are computed with the LBL model Py4CAtS [34], while the gas absorption cross-sections are taken from the HITRAN 2016 database [35]. The uncertainties in the spectroscopic parameters are not considered because their role is irrelevant for this study [36].…”

Section: Introductionmentioning

confidence: 99%

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

et al. 2020

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Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O 2 A-band at 760 nm and the CO 2 band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands. simulations. In [7,8], the transmission function for a given spectral interval is fitted by a sum of exponentials, while the corresponding fitting coefficients are computed from a reduced number of monochromatic computations. A similar approach is described in Moncet et al. [9], where the fitting weights and the most representative wavelengths are chosen appropriately.The state-of-the-art hyperspectral RTMs employ dimensionality reduction techniques such as principal component analysis (PCA). In [10,11], PCA is applied to the spectral radiance data to establish a set of empirical orthogonal functions (EOFs), so that an arbitrary spectrum at full spectral resolution can be reconstructed as a weighted sum of EOFs. The weights are found by performing monochromatic simulations at a reduced number of wavelengths. To accelerate the computations in the O 2 A-band, Natraj et al. [12] proposed a fundamentally different PCA-based radiative transfer model, in which the dimensionality of the optical properties data is reduced. A two-stream radiative transfer model was used as an approximate model, and the dependency of the corresponding correction factor on the optical parameters was modeled by a second-order Taylor expansion about the mean value of the optical parameters in the reduced optical data space. This approach was extended to other dimensionality reduction techniques [13] and spectral ranges [14-16]; moreover, it was implemented in conjunction with PCA for spectral radiances [17] and with the k-distribution method [18]. The errors of these approaches are usually below 0.1% for the spectral radiances, while the performance enhancement may reach several orders of magnitude depending on the spectral region and the required level of accuracy.In Efremenko et al. [19] it was shown that, aft...

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“…The total transmission for the three molecules is depicted in the lower panel. The Python tool Py4CAtS (Python for Computational ATmospheric Spectroscopy [30]) was used to calculate the absorption cross sections of the individual molecules. Note that CO is only responsible for ≈1% of the total optical thickness τ in that spectral range.…”

Section: Absorption In the Swir And Retrievalmentioning

confidence: 99%

Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from TROPOMI

et al. 2020

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The impact of SEOM–IAS (Scientific Exploitation of Operational Missions–Improved Atmospheric Spectroscopy) spectroscopic information on CO columns from TROPOMI (Tropospheric Monitoring Instrument) shortwave infrared (SWIR) observations was examined. HITRAN 2016 (High Resolution Transmission) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques 2015) were used as a reference upon which the spectral fitting residuals, retrieval errors and inferred quantities were assessed. It was found that SEOM–IAS significantly improves the quality of the CO retrieval by reducing the residuals to TROPOMI observations. The magnitude of the impact is dependent on the climatological region and spectroscopic reference used. The difference in the CO columns was found to be rather small, although discrepancies reveal, for selected scenes, in particular, for observations with elevated molecular concentrations. A brief comparison to Total Column Carbon Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) also demonstrated that both spectroscopies cause similar columns; however, the smaller retrieval errors in the SEOM with Speed-Dependent Rautian and line-Mixing (SDRM) inferred CO turned out to be beneficial in the comparison of post-processed mole fractions with ground-based references.

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Py4CAtS—PYthon for Computational ATmospheric Spectroscopy

Cited by 33 publications

References 98 publications

Searching for thermal inversion agents in the transmission spectrum of KELT-20b/MASCARA-2b: detection of neutral iron and ionised calcium H&K lines

Searching for thermal inversion agents in the transmission spectrum of KELT-20b/MASCARA-2b: detection of neutral iron and ionised calcium H&K lines

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from TROPOMI

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