Accurate rotation-vibration (ro-vibrational) energy levels of the main isotopologue of ozone (O-16(3)) in its ground electronic (X) over tilde (1)A(1) state are determined by performing MARVEL (Measured Active Rotation Vibration Energy Levels) analysis on measured line positions from 29 sources of data published in scientific literature. A total of 50276 ro-vibrational transitions are considered yielding 13664 MARVEL energy levels of O-16(3) with their associated uncertainties. The maximum values of the energy and rotational angular momentum quantum number in the MARVEL dataset are 8167.33 cm(-1) and J = 67 respectively. Variational nuclear motion calculations of O-16(3) energy levels are performed and subsequently used to assess and validate the MARVEL results. Comparisons with alternative data compilations based on effective Hamiltonians are also shown.

Reducing methane (CH4) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH4 retrieval algorithm for MethaneSAT based on the CO2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20 x 20 m(2) pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH4 can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100-150 kg h-1 at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (similar to 20 x 20 m(2)) mapped over large swaths (similar to 100 x 100 km(2)) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (similar to 130 x 400 m(2) pixel size over a target area measuring similar to 200 x 200 km(2)), thereby delivering quantitative data on basin-wide methane emissions.

This work presents the development of the MethaneAIR Level0-Level1B processor, which converts raw L0 data to calibrated and georeferenced L1B data. MethaneAIR is the airborne simulator for MethaneSAT, a new satellite under development by MethaneSAT LLC, a subsidiary of the Environmental Defense Fund (EDF). MethaneSAT's goals are to precisely map over 80 % of the production sources of methane from oil and gas fields across the globe to an accuracy of 2-4 ppb on a 2 km 2 scale. Efficient algorithms have been developed to perform dark corrections, estimate the noise, radiometrically calibrate data, and correct stray light. A forward model integrated into the L0-L1B processor is demonstrated to retrieve wavelength shifts during flight accurately. It is also shown to characterize the instrument spectral response function (ISRF) changes occurring at each sampled spatial footprint. We demonstrate fast and accurate orthorectification of MethaneAIR data in a three-step process: (i) initial orthorectification of all observations using aircraft avionics, a simple camera model, and a medium-resolution digital elevation map; (ii) registration of oxygen (O 2 ) channel grayscale images to reference Multispectral Instrument (MSI) band 11 imagery via Accelerated-KAZE (A-KAZE) feature extraction and linear transformation, with similar co-registration of methane (CH 4 ) channel grayscale images to the registered O 2 channel images; and finally (iii) optimization of the aircraft position and attitude to the registered imagery and calculation of viewing geometry. This co-registration technique accurately orthorectifies each channel to the referenced MSI imagery. However, in the pixel domain, radiance data for each channel are offset by almost 150-200 across-track pixels (rows) and need to be aligned for the full-physics or proxy retrievals where both channels are simultaneously used. We leveraged our orthorectification tool to identify tie points with similar geographic locations in both CH 4 and O 2 images in order to produce shift parameters in the across-track and along-track dimensions. These algorithms described in this article will be implemented into the MethaneSAT L0-L1B processor.

The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12 960 m cruise altitude above ground (13 850 m above sea level), MethaneAIR datasets have a 4.5 km swath gridded to 10 m × 10 m pixels with 17–20 ppb standard deviation on a flat scene. MethaneAIR was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume-controlled release experiment, using two methods. (1) The modified integrated mass enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR). WRF-LES-HRRR simulates winds in stochastic eddy-scale (100–1000 m) variability, which is particularly important for low-wind conditions and informing the error budget. The mIME can estimate emission rates of plumes of any size that are detectable by MethaneAIR. (2) The divergence integral (DI) method applies Gauss's theorem to estimate the flux divergence fields through a series of closed surfaces enclosing the sources. The set of boxes grows from the upwind side of the plume through the core of each plume and downwind. No selection of inflow concentration, as used in the mIME, is required. The DI approach can efficiently determine fluxes from large sources and clusters of sources but cannot resolve small point emissions. These methods account for the effects of eddy-scale variation in different ways: the DI averages across many eddies, whereas the mIME re-samples many eddies from the LES simulation. The DI directly uses HRRR winds, while mIME uses WRF-LES-HRRR wind products. Emissions estimates from both the mIME and DI methods agreed closely with the single-blind volume-controlled experiments (N = 21). The York regression between the estimated emissions and the released emissions has a slope of 0.96 [0.84, 1.08], R = 0.83 and N = 21, with 30 % mean percentage error for the whole dataset, which indicates that MethaneAIR can quantify point sources emitting more than 200 kg h−1 for the mIME and 500 kg h−1 for the DI method. The two methods also agreed on methane emission estimates from various uncontrolled sources in the Permian Basin. The experiment thus demonstrates the powerful potential of the MethaneAIR instrument and suggests that the quantification method should be transferable to MethaneSAT if it meets the design specifications.

Calculations S1.1 mIME Effective Wind SpeedsThe effective wind speeds used in the mIME calculation came from the relationship proposed by Varon et al. (2018), U ef f = α • log(10 m wind) + 0.6 where α is between 0.9-1.1.We took advantage of the LES runs by introducing the LES-specific effective wind U adaptive, ef f for each time step.U adaptive, ef f is defined as Q•L IM E .Because we know all the terms in this equation from the LES, we can calculate the true U adaptive, ef f that gives the best estimate of Q.By calculating the U adaptive, ef f for all the time steps of interest, we end up with multiple pairs of U adaptive, ef f and U , which can be fitted into a linear regression function of the form U ef f = a • log(10 m wind) + b with unique a and b coefficients.However, the LES-specific relationship can introduce overfitting results when the data points are limited and wind speeds are low.Since the wind speeds during the controlled release experiments were lower than 5 m/s for most of the time, we decided to use the coefficients in (Varon et al., 2018) to avoid overfitting.

The HITRAN database is a compilation of molecular spectroscopic parameters. It was established in the early 1970s and is used by various computer codes to predict and simulate the transmission and emission of light in gaseous media (with an emphasis on terrestrial and planetary atmospheres). The HITRAN compilation is composed of five major components: the line-by-line spectroscopic parameters required for high-resolution radiative-transfer codes, experimental infrared absorption cross-sections (for molecules where it is not yet feasible for representation in a line-by-line form), collision-induced absorption data, aerosol indices of refraction, and general tables (including partition sums) that apply globally to the data. This paper describes the contents of the 2020 quadrennial edition of HITRAN. The HITRAN2020 edition takes advantage of recent experimental and theoretical data that were meticulously validated, in particular, against laboratory and atmospheric spectra. The new edition replaces the previous HITRAN edition of 2016 (including its updates during the intervening years). All five components of HITRAN have undergone major updates. In particular, the extent of the updates in the HITRAN2020 edition range from updating a few lines of specific molecules to complete replacements of the lists, and also the introduction of additional isotopologues and new (to HITRAN) molecules: SO, CH3F, GeH4, CS2, CH3I and NF3. Many new vibrational bands were added, extending the spectral coverage and completeness of the line lists. Also, the accuracy of the parameters for major atmospheric absorbers has been increased substantially, often featuring sub-percent uncertainties. Broadening parameters associated with the ambient pressure of water vapor were introduced to HITRAN for the first time and are now available for several molecules. The HITRAN2020 edition continues to take advantage of the relational structure and efficient interface available at www.hitran.org and the HITRAN Application Programming Interface (HAPI). The functionality of both tools has been extended for the new edition. (C) 2021 The Author(s). Published by Elsevier Ltd.

The MethaneSAT satellite mission aims at quantifying anthropogenic methane emissions by measuring reflected solar radiation in two spectral windows in the short wave infrared range. In the 1,249 nm - 1,305 nm spectral range the sensor will measure the oxygen (O2) singlet Delta band with a full width at half maximum (FWHM) of 0.17 nm. Additionally, the instrument will observe most of the 2ν3 absorption band of methane (CH4) and the P-branch of a 3ν1+ν3 carbon dioxide (CO2) band in the spectral range 1,605 nm - 1,683 nm at a spectral resolution of 0.23 nm at FWHM. Clouds and aerosols can introduce biases into the inversion of methane column concentrations from solar backscatter measurements and we therefore develop retrieval processors to filter out contaminated scenes. One processor makes use of surface pressure retrievals to infer the presence of scattering particles in the field of view of the sensor. These retrievals specifically take into account O2 airglow emission following the approach of Sun et al. (2018). Surface pressure is retrieved by fitting spectra in the O2 band, assuming a non-scattering atmosphere. We study thresholds in the variations between retrieved surface pressure and a priori meteorological databases which are suitable to screen for clouds and aerosols. A complementary algorithm takes advantage of differences in the atmospheric light path between the two spectral windows of MethaneSAT in the presence of aerosols and clouds. Here, we retrieve water vapor (H2O) column concentrations from the 1.3 µm and 1.6 µm windows under the assumption of the geometric lightpath. The ratio of the H2O retrievals from the two windows is used to construct a filter for aerosol and cloud contaminated scenes. We simulate MethaneSAT measurements with various cloud and aerosol loads to derive the retrieval configurations with highest sensitivity to scattering events. Both filtering approaches are applied to measurements of the MethaneAIR instrument (Staebell et al., 2021) to demonstrate their capacity in screening for clear scenes. Finally, we discuss our on-going efforts in developing a filter for observations affected by cloud shadows. References Staebell, C., Sun, K., Samra, J., Franklin, J., Chan Miller, C., Liu, X., Conway, E., Chance, K., Milligan, S., and Wofsy, S.: Spectral calibration of the MethaneAIR instrument, Atmospheric Measurement Techniques, 14, 3737–3753, https://doi.org/10.5194/amt-14-3737-2021, 2021. Sun, K., Gordon, I. E., Sioris, C. E., Liu, X., Chance, K., and Wofsy, S. C.: Reevaluating the use of O2 a1Δg band in spaceborne remote sensing of greenhouse gases, Geophysical Research Letters, 45, 5779–5787, https://doi.org/10.1029/2018GL077823, 2018.

Water vapor absorption in the near-ultraviolet region is essential to describe the energy budget of Earth, but little spectroscopic information is available since it is a challenging spectral region for both experimental and theoretical studies. A continuous-wave cavity ring-down spectroscopic experiment was built to record absorption lines of water vapor around 415 nm. With a precision of 4×10-10 cm−1, 40 rovibrational transitions of H216O were observed in this work, and 27 of them were assigned to the (224), (205), (710), (304), (093), (125) and (531) vibrational bands. A comparison of line positions and intensities determined in this work to the most recent HITRAN database is presented. Water vapor absorption cross-sections near 415 nm were calculated based on our measurements, which vary between 1×10-26 and 5×10-26cm2 molec.−1. These data will also significantly impact the spectroscopy detection of trace gas species in the near-UV region.

MethaneAIR is the airborne simulator of MethaneSAT, an area-mapping satellite currently under development with the goal of locating and quantifying large anthropogenic CH4 point sources as well as diffuse emissions at the spatial scale of an oil and gas basin. Built to closely replicate the forthcoming satellite, MethaneAIR consists of two imaging spectrometers. One detects CH4 and CO2 absorption around 1.65 and 1.61 µm, respectively, while the other constrains the optical path in the atmosphere by detecting O2 absorption near 1.27 µm. The high spectral resolution and stringent retrieval accuracy requirements of greenhouse gas remote sensing in this spectral range necessitate a reliable spectral calibration. To this end, on-ground laboratory measurements were used to derive the spectral calibration of MethaneAIR, serving as a pathfinder for the future calibration of MethaneSAT. Stray light was characterized and corrected for through fast-Fourier-transform-based Van Cittert deconvolution. Wavelength registration was examined and found to be best described by a linear relationship for both bands with a precision of ∼ 0.02 spectral pixel. The instrument spectral spread function (ISSF), measured with fine wavelength steps of 0.005 nm near a series of central wavelengths across each band, was oversampled to construct the instrument spectral response function (ISRF) at each central wavelength and spatial pixel. The ISRFs were smoothed with a Savitzky–Golay filter for use in a lookup table in the retrieval algorithm. The MethaneAIR spectral calibration was evaluated through application to radiance spectra from an instrument flight over the Colorado Front Range.

The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/CN/13C-14N/Trihybrid.Please check the reference details according to the following description or directly from the website.NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check CN_13C14N_Trihybrid.md for detailed information.Definitions file 13C-14N__Trihybrid.def[5.68 KB]References:1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [https://doi.org/10.1016/j.jqsrt.2020.107228] Spectroscopic Model https://exomol.com/models/CN/13C-14N/Trihybrid/Trihybrid: line list Trihybrid Experimental-Perturbative-Variational line list for CN in standard ExoMol format13C-14N__Trihybrid.states.bz2[587.54 KB]The states file (energy levels list) for the Trihybrid line list for (13C)(14N). 13C-14N__Trihybrid.trans.bz2[20.75 MB]Trihybrid (13C)(14N) external line list transition file. References:1. Brooke, J. S. A., Ram, R. S., Western, C. M., Li, G., Schwenke, D. W., Bernath, P. F., "Einstein a coefficients and oscillator strengths for the A 2Π-X 2Σ+ (Red) and B 2Σ+ - X 2Σ+ (Violet) systems and rovibrational transitions in the X 2Σ+ state of CN", Astrophysical Journal Supplement Series 210, 23 (2014). [http://dx.doi.org/10.1088/0067-0049/210/2/23][14BrRaWe.CN]2. Syme, A. M., McKemmish, L. K., "Full spectroscopic model and trihybrid experimental perturbation variational line list for CN", Monthly Notices of the Royal Astronomical Society 505, 4383-4395 (2021). [https://doi.org/10.1093/mnras/stab1551][21SyMcxx.CN]Trihybrid: partition function Trihybrid Experimental-Perturbative-Variational line list for CN in standard ExoMol format13C-14N__Trihybrid.pf[25.39 KB]The partition function of CN, (13C)(14N), obtained using the Trihybrid line list. References:1. Syme, A. M., McKemmish, L. K., "Full spectroscopic model and trihybrid experimental perturbation variational line list for CN", Monthly Notices of the Royal Astronomical Society 505, 4383-4395 (2021). [https://doi.org/10.1093/mnras/stab1551][21SyMcxx.CN]

Molecular line lists, particularly those computed for high temperature applications, often have very few states assigned local quantum numbers, i.e rotational and vibrational quantum labels. These are often important components for accurately determining line shape parameters required for radiative transfer simulations. Through variational calculations, the projection of the total angular momentum onto the molecule fixed axis (k) is investigated in the Radau internal coordinate system to determine when it can be considered a good quantum number. In the Radau coordinate system, when the square of the th component of the wavefunction is greater than one half, then we can classify k as a good quantum number in accordance with the theorem of Hose and Taylor, a quantum analogue of the Kolmogorov-Arnold-Moser theorem. When the theorem is satisfied, it is shown that k can reliably be used to determine oblate and prolate quantum labels K-a and K-c. This approach is tested on the states of the water and ozone molecules. (C) 2021 Elsevier Ltd. All rights reserved.

We present an extensive study of the five-dimensional potential energy and induced dipole surfaces of the CH4-N2 complex assuming rigid-rotor approximation. Within the supermolecular approach, ab initio calculations of the interaction energies and dipoles were carried out at the CCSD(T)-F12 and CCSD(T) levels of theory using the correlation-consistent aug-cc-pVTZ basis set, respectively. Both potential energy and induced dipole surfaces inherit the symmetry of the molecular system and transform under the A1+ and A2+ irreducible representations of the molecular symmetry group G48, respectively. One can take advantage of the symmetry when fitting the surfaces; first, when constructing angular basis functions and second, when selecting the grid points. The approach to the construction of scalar and vectorial basis functions exploiting the eigenfunction method [Q. Chen, J. Ping and F. Wang, Group Representation Theory for Physicists, World Scientific, 2nd edn, 2002] is developed. We explore the use of Sobolev-type quadrature grids as building blocks of robust quadrature rules adapted to the symmetry of the molecular system. Temperature variations of the cross second virial coefficient and first classical spectral moments of the rototranslational collision-induced band were derived. A reasonable agreement between calculated values and experimental data was found attesting to the high quality of constructed surfaces.

The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/CN/12C-14N/Trihybrid.Please check the reference details according to the following description or directly from the website.NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check CN_12C14N_Trihybrid.md for detailed information.Definitions file 12C-14N__Trihybrid.def[7.29 KB]References:1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [https://doi.org/10.1016/j.jqsrt.2020.107228] Spectroscopic Model https://exomol.com/models/CN/12C-14N/Trihybrid/Trihybrid: partition function Trihybrid Experimental-Perturbative-Variational line list for CN in standard ExoMol format12C-14N__Trihybrid.pf[25.39 KB]The partition function of CN, (12C)(14N), obtained using the Trihybrid line list. References:1. Syme, A. M., McKemmish, L. K., "Full spectroscopic model and trihybrid experimental perturbation variational line list for CN", Monthly Notices of the Royal Astronomical Society 505, 4383-4395 (2021). [https://doi.org/10.1093/mnras/stab1551][21SyMcxx.CN]Trihybrid: line list Trihybrid Experimental-Perturbative-Variational line list for CN in standard ExoMol format12C-14N__Trihybrid.trans.bz2[10.86 MB]Trihybrid (12C)(14N) external line list transition file. 12C-14N__Trihybrid.states.bz2[522.19 KB]The states file (energy levels list) for the Trihybrid line list for (12C)(14N). References:1. Brooke, J. S. A., Ram, R. S., Western, C. M., Li, G., Schwenke, D. W., Bernath, P. F., "Einstein a coefficients and oscillator strengths for the A 2Π-X 2Σ+ (Red) and B 2Σ+ - X 2Σ+ (Violet) systems and rovibrational transitions in the X 2Σ+ state of CN", Astrophysical Journal Supplement Series 210, 23 (2014). [http://dx.doi.org/10.1088/0067-0049/210/2/23][14BrRaWe.CN]2. Syme, A.-M., McKemmish, L. K., "Experimental energy levels of 12C14N through MARVEL analysis", Monthly Notices of the Royal Astronomical Society 499, 25-39 (2020). [https://doi.org/10.1093/mnras/staa2791][20SyMcxx.CN]3. Syme, A. M., McKemmish, L. K., "Full spectroscopic model and trihybrid experimental perturbation variational line list for CN", Monthly Notices of the Royal Astronomical Society 505, 4383-4395 (2021). [https://doi.org/10.1093/mnras/stab1551][21SyMcxx.CN]

There are long running problems over the precise transition intensities and line positions for ozone a,b .In our work, state of the art, first principles quantum mechanical methods are being used to compute high accuracy transition intensities and line positions for the microwave and infrared regions of the spectrum of ozone.In this presentation I will discuss details of our most recent calculations of the ab initio dipole moment surface (DMS) and analysis of experimental ozone line positions using the MARVEL (Measured Active Rotation Vibration Energy Levels) technique c .To improve the quality of our current d DMS we are exploring the electronic structure model, finer grids and larger basis set sizes.The ozone MARVEL project currently involves the analysis of around 70 sources of scientific literature (we anticipate this number to increase).We will use the MARVEL energy levels to fit a new potential energy surface for ozone.We also intend to replace the calculated energy levels with MARVEL energy levels in our line lists.We hope the results of this study will be important for a range of atmospheric studies, such as self-consistency of ozone concentration retrievals in remote sensing techniques, and possible detections of ozone in exo-planetary atmospheres.

The HITRAN2020 database will be publicly released this year.It is a coordinated effort of experimentalists, theoreticians, atmospheric and planetary scientists who measure, calculate and validate the HITRAN data.The lists for most of the HITRAN molecules in the line-by-line section were updated in comparison with the previous compilation HITRAN2016 a .The extent of the updates ranges from updating a few lines of certain molecules to complete replacements of the lists and introducing additional isotopologues.Six new molecules (SO, CH 3 F, GeH 4 , CS 2 , CH 3 I, and NF 3 ) were also added to HITRAN.In addition, the accuracy of the parameters for major atmospheric absorbers has been increased, often featuring sub-percent uncertainties.The number of parameters was also increased significantly, now incorporating, for instance, non-Voigt line profiles for many gases; broadening by water vapor b ; update of collision-induced absorption sets c .The new edition will continue taking advantage of the modern structure and interface available at www.hitran.organd the HITRAN Application Programming Interface d .Their functionality has been extended for the new edition.This talk will provide a brief overview of HITRAN2020 e and its main improvements with respect to the previous edition.

The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/AlO/27Al-16O/ATP.Please check the reference details according to the following description or directly from the website.NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check AlO_27Al16O_ATP.md for detailed information.Definitions file 27Al-16O__ATP.def[6.48 KB]References:1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [https://doi.org/10.1016/j.jqsrt.2020.107228] Spectroscopic Model https://exomol.com/models/AlO/27Al-16O/ATP/ATP: line list Line lists for four isotopes of aluminium monoxide covering the pure rotation, rotation–vibration and electronic (B − X blue–green and A − X infrared bands) spectrum. States file updated 20-11-2015.ATP_README.txt[5.08 KB]Documentation for the AlO line list of the ATP dataset 27Al-16O__ATP.states.bz2[1.09 MB]Labelled rovibrational states of (27Al)(16O). Many calculated (Ca) energy levels were replaced with Marvelised (Ma), Effective Hamiltonian (EH), Predicted shift (PS) and Pseudo-experimental correction (PE) values. 27Al-16O__ATP.trans.bz2[31.11 MB]Frequency ordered transitions of (27Al)(16O) References:1. Patrascu, A. T., Yurchenko, S. N., Tennyson, J., "ExoMol molecular line lists: IX The spectrum of AlO", Monthly Notices of the Royal Astronomical Society 449, 3613-3619 (2015). [http://dx.doi.org/10.1093/mnras/stv507][15PaYuTe.AlO]2. Bowesman, C. A., Shuai, M., Yurchenko, S. N., Tennyson, J., "A high resolution line list for AlO", Monthly Notices of the Royal Astronomical Society Submitted (2021).ATP: partition function Line lists for four isotopes of aluminium monoxide covering the pure rotation, rotation–vibration and electronic (B − X blue–green and A − X infrared bands) spectrum. States file updated 20-11-2015.ATP_README.txt[5.08 KB]Documentation for the AlO line list of the ATP dataset 27Al-16O__ATP.pf[22.85 KB]Partition function for (27Al)(16O) References:1. Patrascu, A. T., Yurchenko, S. N., Tennyson, J., "ExoMol molecular line lists: IX The spectrum of AlO", Monthly Notices of the Royal Astronomical Society 449, 3613-3619 (2015). [http://dx.doi.org/10.1093/mnras/stv507][15PaYuTe.AlO]ATP: other States files NB: These data are not included in current version on ZenodoData can be accessed via: https://exomol.com/data/molecules/AlO/27Al-16O/ATPATP: opacity Line lists for four isotopes of aluminium monoxide covering the pure rotation, rotation–vibration and electronic (B − X blue–green and A − X infrared bands) spectrum. States file updated 20-11-2015.27Al-16O__ATP.R1000_0.3-50mu.ktable.ARCiS.fits.gz[444.15 MB]ARCiS k-tables at R= 1000 (0.3-50mu) in fits format (gzipped): ATP (27Al)(16O) line list. 27Al-16O__ATP.R1000_0.3-50mu.ktable.petitRADTRANS.h5[370.98 MB]petitRADTRANS k-tables at R= 1000 (0.3-50mu) in HDF5 format: ATP (27Al)(16O) line list. 27Al-16O__ATP.R1000_0.3-50mu.ktable.NEMESIS.kta[232.01 MB]NEMESIS k-tables at R= 1000 (0.3-50mu) in NEMESIS-kta format: ATP (27Al)(16O) line list. 27Al-16O__ATP.R15000_0.3-50mu.xsec.TauREx.h5[348.39 MB]TauREx k-tables at R= 15000 (0.3-50mu) in HDF5 format: ATP (27Al)(16O) line list. References:1. Patrascu, A. T., Yurchenko, S. N., Tennyson, J., "ExoMol molecular line lists: IX The spectrum of AlO", Monthly Notices of the Royal Astronomical Society 449, 3613-3619 (2015). [http://dx.doi.org/10.1093/mnras/stv507][15PaYuTe.AlO]

The dataset is an archive of ExoMol page, https://exomol.com/data/molecules/SiH2/28Si-1H2/CATS.Please check the reference details according to the following description or directly from the website.NB: The html description skips data which are not included in the current version for the purpose of simplicity. Please check SiH2_28Si1H2_CATS.md for detailed information.Definitions file 28Si-1H2__CATS.def[8.4 KB]References:1. Tennyson, J., Yurchenko, S. N., Al-Refaie, A. F., Clark, V. H. J., Chubb, K. L., Conway, E. K., Dewan, A., Gorman, M. N., Hill, C., Lynas-Gray, A. E., Mellor, T., McKemmish, L. K., Owens, A., Polyansky, O. L., Semenov, M., Somogyi, W., Tinetti, G., Upadhyay, A., Waldmann, I., Wang, Y., Wright, S., Yurchenko, O. P., "The 2020 release of the ExoMol database: molecular line lists for exoplanet and other hot atmospheres", J. Quant. Spectrosc. Rad. Transf., 255, 107228 (2020). [https://doi.org/10.1016/j.jqsrt.2020.107228] Spectroscopic Model https://exomol.com/models/SiH2/28Si-1H2/CATS/CATS: partition function SiH2 line list CATS obtained using a refined PES and ab initio DMS, computed with TROVE28Si-1H2__CATS.pf[48.83 KB]A CATS partition function, (28Si)(1H)2. References:1. Clark, V. H. J., Owens, A., Tennyson, J., Yurchenko, S. N., "The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)", Journal of Quantitative Spectroscopy and Radiative Transfer 246, 106929 (2021). [https://doi.org/10.1016/j.jqsrt.2020.106929][21ClOwTe.SiH2]CATS: line list SiH2 line list CATS obtained using a refined PES and ab initio DMS, computed with TROVE28Si-1H2__CATS.states.bz2[6.9 MB]A CATS .states file. (28Si)(1H)2 hot line list. 28Si-1H2__CATS__00000-01000.trans.bz2[80.78 MB]CATS hot line list transitions, (28Si)(1H)2: 0-1000 cm-1. 28Si-1H2__CATS__01000-02000.trans.bz2[105.67 MB]CATS hot line list transitions, (28Si)(1H)2: 1000-2000 cm-1. 28Si-1H2__CATS__02000-03000.trans.bz2[134.01 MB]CATS hot line list transitions, (28Si)(1H)2: 2000-3000 cm-1. 28Si-1H2__CATS__03000-04000.trans.bz2[167.82 MB]CATS hot line list transitions, (28Si)(1H)2: 3000-4000 cm-1. 28Si-1H2__CATS__04000-05000.trans.bz2[205.19 MB]CATS hot line list transitions, (28Si)(1H)2: 4000-5000 cm-1. 28Si-1H2__CATS__05000-06000.trans.bz2[249.32 MB]CATS hot line list transitions, (28Si)(1H)2: 5000-6000 cm-1. 28Si-1H2__CATS__06000-07000.trans.bz2[298.96 MB]CATS hot line list transitions, (28Si)(1H)2: 6000-7000 cm-1. 28Si-1H2__CATS__07000-08000.trans.bz2[354.93 MB]CATS hot line list transitions, (28Si)(1H)2: 7000-8000 cm-1. 28Si-1H2__CATS__08000-09000.trans.bz2[415.19 MB]CATS hot line list transitions, (28Si)(1H)2: 8000-9000 cm-1. 28Si-1H2__CATS__09000-10000.trans.bz2[476.85 MB]CATS hot line list transitions, (28Si)(1H)2: 9000-10000 cm-1. References:1. Clark, V. H. J., Owens, A., Tennyson, J., Yurchenko, S. N., "The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)", Journal of Quantitative Spectroscopy and Radiative Transfer 246, 106929 (2021). [https://doi.org/10.1016/j.jqsrt.2020.106929][21ClOwTe.SiH2]CATS: opacity SiH2 line list CATS obtained using a refined PES and ab initio DMS, computed with TROVE28Si-1H2__CATS.R1000_0.3-50mu.ktable.ARCiS.fits.gz[339.68 MB]ARCiS k-tables at R= 1000 (0.3-50mu) in fits format (gzipped): CATS (28Si)(1H)2 line list. 28Si-1H2__CATS.R1000_0.3-50mu.ktable.petitRADTRANS.h5[370.98 MB]petitRADTRANS k-tables at R= 1000 (0.3-50mu) in HDF5 format: CATS (28Si)(1H)2 line list. 28Si-1H2__CATS.R1000_0.3-50mu.ktable.NEMESIS.kta[232.01 MB]NEMESIS k-tables at R= 1000 (0.3-50mu) in NEMESIS-kta format: CATS (28Si)(1H)2 line list. 28Si-1H2__CATS.R15000_0.3-50mu.xsec.TauREx.h5[348.39 MB]TauREx k-tables at R= 15000 (0.3-50mu) in HDF5 format: CATS (28Si)(1H)2 line list. References:1. Clark, V. H. J., Owens, A., Tennyson, J., Yurchenko, S. N., "The high-temperature rotation-vibration spectrum and rotational clustering of silylene (SiH2)", Journal of Quantitative Spectroscopy and Radiative Transfer 246, 106929 (2021). [https://doi.org/10.1016/j.jqsrt.2020.106929][21ClOwTe.SiH2]2. Chubb, K. L., Rocchetto, M., Yurchenko, S. N., Min, M., Waldmann, I., Barstow, J. K., Molliere, P., Al-Refaie, A. F, Phillips, M. W., Tennyson, J., "The ExoMolOP database: Cross sections and k-tables for molecules of interest in high-temperature exoplanet atmospheres", Astronomy and Astrophysics 646, A21 (2020). [http://dx.doi.org/10.1051/0004-6361/202038350][20ChRoYu.]

Accurate reference spectroscopic information for the water molecule from the microwave to the near-ultraviolet is of paramount importance in atmospheric research. A semi-empirical potential energy surface for the ground electronic state of (H2O)-O-16 has been created by refining almost 4000 experimentally determined energy levels. These states extend into regions with large values of rotational and vibrational excitation. For all states considered in our refinement procedure, which extend to 37 000 cm(-1) and J = 20 (total angular momentum), the average rootmean-square deviation is approximately 0.05 cm(-1). This potential energy surface offers significant improvements when compared to recent models by accurately predicting states possessing high values of J. This feature will offer significant improvements in calculated line positions for high-temperature spectra where transitions between high J states become more prominent. Combining this potential with the latest dipole moment surface for water vapour, a line list has been calculated which extends reliably to 37 000 cm(-1). Obtaining reliable results in the ultraviolet is of special importance as it is a challenging spectral region for the water molecule both experimentally and theoretically. Comparisons are made against several experimental sources of cross sections in the near-ultraviolet and discrepancies are observed. In the near-ultraviolet our calculations are in agreement with recent atmospheric retrievals and the upper limit obtained using broadband spectroscopy by Wilson et al. (2016, p. 194), but they do not support recent suggestions of very strong absorption in this region.

When comparing energy levels calculated from different PES (eg Fig. 1) is it straightforward to identify the same energy levels in the different data sets using only the rigorous labels (J, parity, symmetry) in particular in the high energy range that you