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The recently adopted Ariel ESA mission will measure the atmospheric composition of a large number of exoplanets. This information will then be used to better constrain planetary bulk compositions. While the connection between the composition of a planetary atmosphere and the bulk interior is still being investigated, the combination of the atmospheric composition with the measured mass and radius of exoplanets will push the field of exoplanet characterisation to the next level, and provide new insights of the nature of planets in our galaxy. In this white paper, we outline the ongoing activities of the interior working group of the Ariel mission, and list the desirable theoretical developments as well as the challenges in linking planetary atmospheres, bulk composition and interior structure. [ABSTRACT FROM AUTHOR]

Aims. Our aim in this paper is to refine the orbital and physical parameters of the HATS-2 planetary system and study transit timing variations and atmospheric composition thanks to transit observations that span more than 10 yr and that were collected using different instruments and pass-band filters. We also investigate the orbital alignment of the system by studying the anomalies in the transit light curves induced by starspots on the photosphere of the parent star. Methods. We analysed new transit events from both ground-based telescopes and NASA's TESS mission. Anomalies were detected in most of the light curves and modelled as starspots occulted by the planet during transit events. We fitted the clean and symmetric light curves with the JKTEBOP code and those affected by anomalies with the PRISM+GEMC codes to simultaneously model the photometric parameters of the transits and the position, size, and contrast of each starspot. Results. We found consistency between the values we found for the physical and orbital parameters and those from the discovery paper and ATLAS9 stellar atmospherical models. We identified different sets of consecutive starspot-crossing events that temporally occurred in less than five days. Under the hypothesis that we are dealing with the same starspots, occulted twice by the planet during two consecutive transits, we estimated the rotational period of the parent star and, in turn the projected and the true orbital obliquity of the planet. We find that the system is well aligned. We identified the possible presence of transit timing variations in the system, which can be caused by tidal orbital decay, and we derived a low-resolution transmission spectrum. [ABSTRACT FROM AUTHOR]

The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at developing an open software ecosystem for model agnostic data assimilation. This paper considers implementation of the model‐agnostic family of the local volume solvers in the JEDI framework. The implemented solvers include the Local Ensemble Transform Kalman Filter (LETKF), the Gain form of the Ensemble Transform Kalman Filter (GETKF), and the optimal interpolation variant of the LETKF (LETKF‐OI). This paper documents the implementation strategy for the family of the local volume solvers within the JEDI framework. We also document an expansive set of localization approaches that includes generic distance‐based localization, localization based on modulated ensemble products, and localizations specific to ocean (based on the Rossby radius of deformation), and land (based on the terrain difference between observation and model grid point). Finally, we apply the developed solvers in a limited set of experiments, including single‐observation experiments in atmosphere and ocean, and cycling experiments for the atmosphere, ocean, land, and aerosol assimilation. We also illustrate how JEDI Ensemble Kalman Filter solvers can be used in a strongly coupled framework using the interface solver approximation, which provides increments to the ocean based on observations from the ocean and atmosphere. Plain Language Summary: The Joint Effort for Data assimilation Integration (JEDI) is an international collaboration aimed at reducing the time it takes to transition research on initialization of the Earth system models to operation. The JEDI framework is designed to be agnostic of the specific numerical model and, hence, can facilitate collaboration between research institutions and operational centers. This paper documents implementation of the Ensemble Kalman filtering framework within JEDI. The implementation strategy supports a variety of algorithmic approaches to the Ensemble Kalman Filtering and is appropriate for multiple Earth system applications. Specifically, we demonstrate applications for atmosphere, atmospheric composition, ocean, and land data assimilation. Key Points: Local volume solvers are developed in the Joint Effort for Data assimilation Integration (JEDI) softwareLocalization operators for various components of the Earth system are developedCase studies are presented with various components of the Earth system [ABSTRACT FROM AUTHOR]

​​​​​​​As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOAs) influence public health, regional air quality, and global climate patterns. This paper highlights the use of hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze six standards, environmental standards (1648a and 1649b), and samples collected in urban environments in Guangzhou in the Pearl River Delta region, which serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method. Sodium octyl- d17 sulfate serves as the internal standard, with a linear correlation coefficient of the six standards, boasting a linear correlation coefficient r ranging from 0.993–0.9991 and a slope, k , of the linear equation from 0.966–1.882. The instrument detection limits (IDLs) are established at 0.03–0.20 µgmL-1 , while the method detection limits (MDLs) fall within the range of 0.30–1.75 ngm-3 , demonstrating the method's exceptional sensitivity. Since isoprene-derived organosulfates (iOSs) are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulfate, and as such showed strong retention using this method, this technique employs sodium ethyl sulfate and sodium octyl sulfate standards for semi-quantitative compound analysis of iOSs. The error in sample analysis (EA) ranged from 12.25 %–95.26 %, and the two standards maintained a consistent recovery rate between 116 %–131 % and 86.4 %–127 %. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for iOSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several iOSs using this method. Methyltetrol sulfate (m/z 215, C5H11SO7-) is the highest concentration in the ambient samples, up to 67.3 ngm-3 in the daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

GHOST: Globally Harmonised Observations in Space and Time, represents one of the biggest collection of harmonised measurements of atmospheric composition at the surface. In total, 7,275,148,646 measurements from 1970–2023, of 227 different components, from 38 reporting networks, are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of provided information from the major public reporting networks, saved in 21 data variables, and 163 metadata variables. Extensive effort in particular is put towards the standardisation of measurement process information, and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use), and temporal classifications per measurement (e.g. day / night). A range of standardised network quality assurance flags are associated with each individual measurement. GHOST own quality assurance is also performed and associated with measurements. Measurements prefiltered by some default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset, and give insights and recommendations for data providers based on experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024). [ABSTRACT FROM AUTHOR]

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane's role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability. [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

As a crucial constituent of fine particulate matter (PM2.5), secondary organic aerosols (SOA) influence public health, regional air quality, and global climate patterns. This paper highlights the use of Hydrophilic interaction liquid chromatography (HILIC) which effectively retains strongly polar analytes that might exhibit incomplete or no retention in reverse chromatography, resulting in superior separation efficiency. A HILIC column was used to analyze 7 standards, environmental standards (1648a and 1649b), and samples collected in urban environments in the Pearl River Delta region of Guangzhou. That serve as valuable reference points for evaluating the organic composition of the atmospheric environment. The results indicate a high degree of accuracy in the analytical method, sodium octyl-d17 sulfate serves as the internal standard, with a linear correlation coefficient of the 7 standards, boasting a linear correlation coefficient R ranging from 0.987-0.999 and a slope, k, of the linear equation from 0.9662-2.2927. The instrument detection limit (IDLs) is established at 0.0026-0.0300 µg mL-1, while the method detection limit (MDLs) falls within the range of 0.0077-0.2300 ng m-3, demonstrating the method's exceptional sensitivity. Since isoprene sulphates are highly polar due to containing a hydrophilic bond to the hydroxyl group and a hydrophobic bond to the sulphate, and as such showed strong retention using this method. This technique employs Sodium ethyl sulfate and Sodium octyl sulfate standards for semi-quantitative compound analysis isoprene-derived OSs, the error in sample analysis (EA) ranged from 12.25-95.26% and the two standards maintaining a consistent recovery rate between 116%-131% and 86%-127%. These findings indicate a high level of precision when semi-quantifying compounds with similar structural characteristics, affirming the analysis method's minimal relative error and underscoring its repeatability, process stability, and the reliability of its results for isoprene OSs. To enhance the method's reliability assessment, the study analyzed polar organic components of standard particulate matter samples (1648a and 1649b), providing precise determinations of several isoprene OSs using this method. Methyltetrol sulfate (m/z 215) is the highest concentration in the ambient samples, up to 67.33 ng m-3 at daytime. These results serve as valuable reference points for assessing the organic composition of the atmospheric environment. [ABSTRACT FROM AUTHOR]

Ammonia emission has attracted considerable attention concerning environmental protection in the world. About 33% of the atmospheric ammonia emission comes from farmland fertilization, so the monitoring of ammonia emissions from farmland is of great significance for controlling atmospheric ammonia pollution. In recent years, tuneable diode laser absorption spectroscopy (TDLAS) has been applied to the measurement of farmland ammonia emission; however, the spectral signal of the open optical path is susceptible to distortion due to temperature, noise, turbulence, etc. Meanwhile, the inversion of the distorted spectrum will result in abnormal concentration values that reduce the detection accuracy. This paper proposes a TDLAS spectral quality evaluation method based on dynamic time warping (DTW) algorithm and support vector data description (SVDD) algorithm. DTW algorithm was used to analyse the similarity between the measured spectrum and the reference spectrum, then SVDD algorithm was used to generate a classification model according to the results of DTW algorithm. DTWSVDD algorithm could effectively avoid obtaining wrong inversion concentration and improve the accuracy of concentration monitoring with identification accuracy of spectral quality reached more than 98%. Applied to the measurement of farmland ammonia emission in Fengqiu, Henan Province, China at the Agricultural Ecological Experiment Station of Chinese Academy of Sciences in November 2021, this method could effectively reduce the dispersion degree of measured concentration trend, as a result, which was better than that of the reported correlation coefficient method. DTW-SVDD algorithm should be of further research value for the application of the analysis of the Big Data of atmospheric composition detection based on grand-based optical remote sensing system. [ABSTRACT FROM AUTHOR]

Surface solar irradiance varies on scales down to seconds, and detailed long-term observational datasets of this variable are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz as well as fully resolved variability until at least 0.1 Hz over a period of 10 years from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 m above ground level. Statistics of variability derived from all time series include approximately 185 000 detected events of both cloud enhancement and cloud shadows. Additional observations from the Cabauw measurement site are freely available from the open-data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. Additionally, we discuss and showcase (potential) applications, including limitations due to sensor response time. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability as well as information to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at 10.5281/zenodo.7093164 and 10.5281/zenodo.7462362 ; the reader is referred to the "Code and data availability" section of this paper for the complete list. [ABSTRACT FROM AUTHOR]

The paper describes the development and performance of the Double Extended Stratospheric–Tropospheric (DEST vn1.0) chemistry scheme, which forms a part of the Met Office's Unified Model coupled to the United Kingdom Chemistry and Aerosol (UM–UKCA) chemistry–climate model, which is the atmospheric composition model of the United Kingdom Earth System Model (UKESM). The scheme extends the standard Stratospheric–Tropospheric chemistry scheme (StratTrop) by including a range of important updates to the halogen chemistry. These allow process-oriented studies of stratospheric ozone depletion and recovery, including the impacts from both controlled long-lived ozone-depleting substances (ODSs) and emerging issues around uncontrolled very short-lived substances (VSLS). The main updates in DEST are (i) an explicit treatment of 14 of the most important long-lived ODSs; (ii) an inclusion of brominated VSLS (Br-VSLS) emissions and chemistry; and (iii) an inclusion of chlorinated VSLS (Cl-VSLS) emissions/LBCs (lower boundary conditions) and chemistry. We evaluate the scheme's performance by comparing DEST simulations against analogous runs made with the standard StratTrop scheme and against observational and reanalysis datasets. Overall, our scheme addresses some significant shortcomings in the representation of atmospheric halogens in the standard StratTrop scheme and will thus be particularly relevant for studies of ozone layer recovery and processes affecting it, in support of future World Meteorological Organization (WMO) Ozone Assessment Reports. [ABSTRACT FROM AUTHOR]

Observations of the atmospheric sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are extremely scarce, limiting our knowledge of carbon turnover in this climatically sensitive environment and the fate of the enormous carbon reservoirs conserved in the permafrost. Especially critical are the gaps in the high latitudes of Siberia, covered by the vast permafrost underlain tundra, where only several atmospheric monitoring sites are operational. This paper presents the first two years (September 2018–January 2021) of accurate continuous observations of atmospheric CO2 and CH4 dry mole fractions at the recently deployed tower-based measurement station "DIAMIS" (73.5068° N, 80.5198° E) located on the southwestern coast of the Taimyr Peninsula, Siberia, at the Gulf of the Yenisei River that opens to the Kara Sea (Arctic Ocean). In this paper, we summarized the scientific rationale of the site, examined the seasonal footprint of the station with an analysis of terrestrial vegetation and maritime sector contributing to the captured atmospheric signal, and illustrated temporal patterns of CO2 and CH4 for the daytime mixed atmospheric layer over the continent–sea interface. Along with the temporal variations reflecting a signal caused pan-Arctic and not very much influenced by the local processes, we analyzed the spatiotemporal distribution of the synoptic anomalies representing the atmospheric signatures of regional sources and sinks of CO2 and CH4 for the studied high-arctic Siberian domain of ~625 thousand km2, with nearly equal capturing the land surface (54%) and the ocean (46%) throughout the year. Both for CO2 and CH4, we have observed a sea–continent declining trend, presuming a larger depletion of trace gases in the maritime air masses compared to the continental domain. So far, over the Kara Sea, we have not detected any prominent signals of CH4 that might have indicated processes of subsea permafrost degradation and occurrence of cold seeps–still mainly observed in the eastern Arctic Seas—The Laptev Sea and the East-Siberian Sea. [ABSTRACT FROM AUTHOR]

In order to accurately predict the ozone concentration level, this paper used the EC_ Thin global weather model products, global numerical weather prediction products from GRAPES_GFS, which are the graphs independently developed in China, and South China GRACEs atmospheric composition model output products to integrate meteorological and environmental observation data, and used six machine learning intelligent algorithms to build a hybrid model that coupled numerical prediction models with machine learning, in order to give full play to the advantages, complementarity and synergy of numerical prediction and machine learning intelligent algorithms, and improve the accuracy of ozone concentration level prediction by leaps and bounds. Four control experiments were set up for which different characteristic products were selected and the classical classification algorithm of machine learning was used to classify and predict the ozone concentration level in Changsha for any four days to come. The model output with the highest test accuracy was taken as the result statistics. It is found that the test accuracy of the optimal model in 1 to 4 days is 81.7%, 81.7%, 78.3% and 60.9% respectively, which is much higher than the atmospheric composition model prediction and forecaster experience, and achieves the expected design goal. The contribution of high-quality weather model products to product quality is large while that of atmospheric composition model products is limited. The prediction performance of the model within 3 days is good, and low-level prediction performance is good, and high-level prediction performance is general. The following solutions for discussion are proposed: the number of high-level samples is to be increased; the recognition ability of the model is to be enhanced for such events; the mechanism analysis of high-grade ozone pollution is to be strengthened; and more refined factors for the use of the model are to be combined. [ABSTRACT FROM AUTHOR]

Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO; 43.66 ∘ N, 79.40 ∘ W; 174 m a.s.l.) are presented. These are retrieved from Fourier transform infrared (FTIR) solar absorption spectra recorded with an ABB Bomem DA8 spectrometer from May 2002 to December 2020. The retrievals have been performed for 14 species: O3 , HCl, HF, HNO3 , CH4 , C2H6 , CO, HCN, N2O , C2H2 , H2CO , CH3OH , HCOOH, and NH3 using the SFIT4 algorithm. The measurements have been archived in the Network for the Detection of Atmospheric Composition Change (NDACC) data repository in Hierarchical Data Format version 4 (HDF4) files following the Generic Earth Observation Metadata Standard (GEOMS) and are also publicly available on Borealis, the Canadian Dataverse Repository (10.5683/SP2/VC8JMC, Yamanouchi et al., 2022). In this paper, we describe the instrumentation, the retrieval strategy, the vertical sensitivity of the retrievals, the quality assurance process, and the error analysis of the TAO FTIR measurements and present the current version of the time series. [ABSTRACT FROM AUTHOR]

The near-infrared transmission spectrum of the warm sub-Neptune exoplanet GJ 1214 b has been observed to be flat and featureless, implying a high metallicity atmosphere with abundant aerosols. Recent JWST MIRI Low Resolution Spectrometer observations of a phase curve of GJ 1214 b showed that its transmission spectrum is flat out into the mid-infrared. In this paper, we use the combined near- and mid-infrared transmission spectrum of GJ 1214 b to constrain its atmospheric composition and aerosol properties. We generate a grid of photochemical haze models using an aerosol microphysics code for a number of background atmospheres spanning metallicities from 100 to 1000× solar, as well as a steam atmosphere scenario. The flatness of the combined data set largely rules out atmospheric metallicities ≤300× solar due to their large corresponding molecular feature amplitudes, preferring values ≥1000× solar and column haze production rates ≥10−10 g cm−2 s−1. The steam atmosphere scenario with similarly high haze production rates also exhibits sufficiently small molecular features to be consistent with the transmission spectrum. These compositions imply that atmospheric mean molecular weights ≥15 g mol−1 are needed to fit the data. Our results suggest that haze production is highly efficient on GJ 1214 b and could involve non-hydrocarbon, non-nitrogen haze precursors. Further characterization of GJ 1214 b's atmosphere would likely require multiple transits and eclipses using JWST across the near- and mid-infrared, potentially complemented by ground-based high-resolution transmission spectroscopy. [ABSTRACT FROM AUTHOR]

This paper presents a solution for efficiently and accurately solving separable least squares problems with multiple datasets. These problems involve determining linear parameters that are specific to each dataset while ensuring that the nonlinear parameters remain consistent across all datasets. A well-established approach for solving such problems is the variable projection algorithm introduced by Golub and LeVeque, which effectively reduces a separable problem to its nonlinear component. However, this algorithm assumes that the datasets have equal sizes and identical auxiliary model parameters. This article is motivated by a real-world remote sensing application where these assumptions do not apply. Consequently, we propose a generalized algorithm that extends the original theory to overcome these limitations. The new algorithm has been implemented and tested using both synthetic and real satellite data for atmospheric carbon dioxide retrievals. It has also been compared to conventional state-of-the-art solvers, and its advantages are thoroughly discussed. The experimental results demonstrate that the proposed algorithm significantly outperforms all other methods in terms of computation time, while maintaining comparable accuracy and stability. Hence, this novel method can have a positive impact on future applications in remote sensing and could be valuable for other scientific fitting problems with similar properties. [ABSTRACT FROM AUTHOR]

The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane (CH4). This paper utilizes FY-3E/HIRAS-II observational data to optimize each gas channel using the improved Optimal Sensitivity Profile method (OSP) channel algorithm and establishes a typical convolutional neural network model (CNN) and a representative U-shaped network model (UNET) with deep features and shallow feature links to perform atmospheric profile retrieval calculations of O3, CO, and CH4. We chose the clear sky data of the Indian and its southern seas in December 2021 and January 2022, with reanalysis data from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and European Center for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis v4 (EAC4) serving as the reference values. The retrieval outcomes were then compared against advanced numerical forecast models including the Whole Atmosphere Community Climate Model (WACCM), Global Forecast System (GFS), and satellite products from an Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). Experimental results show that the generalization ability and retrieval accuracy of CNN are slightly higher compared with UNET. For O3 profile retrieval, the mean percentage error (MPE) of the whole layers for CNN and UNET data in relation to ERA5 data was less than 8%, while the root-mean-square error (RMSE) was below 1.5 × 10−7 kg/kg; for CH4 profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 0.7%, while the RMSE was below 1.5 × 10−8 kg/kg. The retrieval of O3 and CH4 are resulted in a significant improvement compared to the forecast data and satellite products in most pressure levels; for CO profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 11%, while the RMSE was below 4 × 10−8 kg/kg. The error of the CO retrieval results was higher than that of the forecast data at the pressure level of 200~500 hPa and lower than that of similar satellite products with most pressure levels. The experiments indicated that the neural network method effectively determines the atmospheric gas profiles using infrared hyperspectral data, exhibiting a positive performance in accuracy and retrieval speed. [ABSTRACT FROM AUTHOR]

The aerosol optical depth (AOD) is an indicator of particles suspended in the atmosphere, which measures the attenuation of transmitted spectral radiant energy through aerosols. It has been proven to be an essential factor in the estimation of air pollutants concentration and surface solar radiation. However, due to the sparse distribution of ground-based measurements and the high missing rate of satellite retrieval products, the reanalysis data with high spatial–temporal continuity and large spatial range is an excellent supplementary data source. However, compared with satellite monitoring and ground-based measurements, the reliability of the reanalysis data has been questioned. Therefore, in this paper, we evaluate the accuracy of the Monitoring Atmospheric Composition and Climate (MACC) AOD values at the wavelength of 550 nm (AOD550) using the ground-based observations data from 19 AERONET stations in the Chinese mainland for the period 2003–2007. In addition, a data fusion correction model based on the random forest regression is constructed and analyzed. The correction results demonstrate that the correction model, which captures the non-linear relationship, has a satisfactory correction effect overall, with R from 0.771 to 0.978, MAE from 0.225 to 0.047, and RMSE from 0.400 to 0.120. Furthermore, with the aid of model-agnostic machine learning explanation algorithms, we weigh the relative importance of the independent variables and visualize the complicated relationship between the model errors and the important input features. In general, the availability of MACC AOD was improved, and the correction model also provided a reference for the correction of other reanalysis data. [ABSTRACT FROM AUTHOR]

The three Infrared Atmospheric Sounding Interferometer (IASI) instruments on board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, or industrial releases is key to take safety measures to protect the inhabitants and the environment in the impacted areas. With its near-real-time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events in order to support operational decisions. In this paper, we describe a new approach to the near-real-time detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analyzing both the IASI raw and compressed spectra, we applied a PCA-granule-based method on various past, well-documented extreme events such as volcanic eruptions, fires, anthropogenic pollution, and industrial accidents. We demonstrate that the method is well suited to the detection of spectral signatures for reactive and weakly absorbing gases, even for sporadic events. Consistent long-term records are also generated for fire and volcanic events from the available IASI/Metop-B data record. The method is running continuously, delivering email alerts on a routine basis, using the near-real-time IASI L1C radiance data. It is planned to be used as an online tool for the early and automatic detection of extreme events, which was not done before. [ABSTRACT FROM AUTHOR]

The NASA Ionospheric Connection Explorer (ICON) was launched in October 2019 and has been observing the upper atmosphere and ionosphere to understand the sources of their strong variability, to understand the energy and momentum transfer, and to determine how the solar wind and magnetospheric effects modify the internally-driven atmosphere-space system. The Far Ultraviolet Instrument (FUV) supports these goals by observing the ultraviolet airglow in day and night, determining the atmospheric and ionospheric composition and density distribution. Based on the combination of ground calibration and flight data, this paper describes how major instrument parameters have been verified or refined since launch, how science data are collected, and how the instrument has performed over the first 3 years of the science mission. It also provides a brief summary of science results obtained so far. [ABSTRACT FROM AUTHOR]

A major motivation of spectroscopic observations of giant exoplanets is to unveil planet formation processes from atmospheric compositions. Several recent studies suggested that atmospheric nitrogen, like carbon and oxygen, can provide important constraints on planetary formation environments. Since nitrogen chemistry can be far from thermochemical equilibrium in warm atmospheres, we extensively investigate under what conditions, and with what assumptions, the observable NH3 abundances can diagnose an atmosphere's bulk nitrogen abundance. In the first paper of this series, we investigate atmospheric T–P profiles across equilibrium temperature, surface gravity, intrinsic temperature, atmospheric metallicity, and C/O ratio using a 1D radiative–convective equilibrium model. Models with the same intrinsic temperature and surface gravity coincide with a shared "universal" adiabat in the deep atmosphere, across a wide equilibrium temperature range (250–1200 K), which is not seen in hotter or cooler models. We explain this behavior in terms of the classic "radiative zero solution" and then establish a semianalytical T–P profile of the deep atmospheres of warm exoplanets. This profile is then used to predict vertically quenched NH3 abundances. At solar metallicity, our results show that the quenched NH3 abundance only coincides with the bulk nitrogen abundance (within 10%) at low intrinsic temperature, corresponding to a planet with a sub-Jupiter mass (≲1 M J) and old age (≳1 Gyr). If a planet has a high-metallicity (≳10× solar) atmosphere, the quenched NH3 abundance significantly underestimates the bulk nitrogen abundance at almost all planetary masses and ages. We suggest modeling and observational strategies to improve the assessment of bulk nitrogen from NH3. [ABSTRACT FROM AUTHOR]

The ability of regional air quality models to skilfully represent pollutant distributions throughout the atmospheric column is important to enabling their skilful prediction at the surface. This provides a requirement for model evaluation at elevated altitudes, though observation datasets available for this purpose are limited. This is particularly true of those offering sampling over extended time periods. To address this requirement and support evaluation of regional air quality models such as the UK Met Offices Air Quality in the Unified Model AQUM), a long-term, quality assured, dataset of the three-dimensional distribution of key pollutants has been collected over the southern United Kingdom from June 2019 to April 2022. This sampling period encompasses operations during the global COVID-19 pandemic, and as such the dataset serves an additional application in providing a unique resource with which to explore changes in atmospheric composition associated with reduced emissions during this period. Measurements were collected using the Met Office Atmospheric Survey Aircraft MOASA), a Cessna-421 instrumented for this project to measure gaseous nitrogen dioxide, ozone, sulphur dioxide and fine mode (PM2.5) aerosol. This paper provides a technical introduction to the MOASA measurement platform, flight strategies and instrumentation. The MOASA air quality dataset includes 63 flight sorties (totalling over 150 hours of sampling), the data from which are openly available for use. Example case studies using data from these sorties are presented, which include an analysis of the spatial scales of measured pollutant variability, initial work to evaluate performance of the AQUM regional air quality model, and an introduction to the vertical structure of pollutants observed during repeated flight patterns over Greater London, including during the COVID-19 impacted period. [ABSTRACT FROM AUTHOR]

Surface solar irradiance varies on scales down to seconds, of which detailed, long-term observational datasets are rare but in high demand. Here, we present an observational dataset of global, direct, and diffuse solar irradiance sampled at 1 Hz over a period of 10 years, from the Baseline Surface Radiation Network (BSRN) station at Cabauw, the Netherlands. The dataset is complemented with irradiance variability classifications, clear-sky irradiance and aerosol reanalysis, information about the solar position, observations of clouds and sky type, and wind measurements up to 200 meters above ground level. Statistics of variability derived from all time series include approximately 185,000 detected events of both cloud enhancement and cloud shadows. The Cabauw measurement site has additional observations freely available at the open data platform of the Royal Netherlands Meteorological Institute. This paper describes the observational site, quality control, classification algorithm with validation, and the processing method of complementary products. These observations and derived statistics provide detailed information to aid research into how clouds and atmospheric composition influence solar irradiance variability, and to help validate models that are starting to resolve variability at higher fidelity. The main datasets are available at https://doi.org/10.5281/zenodo.7093164 (Knap and Mol, 2022) and https://doi.org/10.5281/zenodo.7092058 (Mol et al., 2022b), see the data availability section for the complete list. [ABSTRACT FROM AUTHOR]

Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO, 43.66°N, 79.40°W, 174 m.a.s.l.) are presented. These are retrieved from Fourier Transform InfraRed (FTIR) solar absorption spectra recorded with an ABB Bomem DA8 spectrometer from May 2002 to December 2020. The retrievals have been optimized for fourteen species: O3, HCl, HF, HNO3, CH4, C2H6, CO, HCN, N2O, C2H2, H2CO, CH3OH, HCOOH and NH3 using the SFIT4 algorithm. The measurements have been archived in the Network for Detection of Atmospheric Composition Change (NDACC) data repository in Hierarchical Data Format version 4 (HDF4) files following the Generic Earth Observation Metadata Standard (GEOMS) and are also publicly available on Borealis, the Canadian Dataverse Repository. In this paper, we describe the instrumentation, the retrieval strategy, the vertical sensitivity of the retrievals, the quality assurance process, and error analysis of the TAO FTIR measurements, and present the current version of the time series. [ABSTRACT FROM AUTHOR]

The paper describes the development and performance of the Double Extended Stratospheric-Tropospheric (DEST vn1.0) chemistry scheme, which forms a part of the Met Office's Unified Model coupled to the United Kingdom Chemistry and Aerosol (UM-UKCA) chemistry-climate model, the atmospheric composition model of the United Kingdom Earth System Model (UKESM). The scheme extends the standard Stratospheric-Tropospheric Chemistry scheme (StratTrop) by including a range of important updates to the halogen chemistry. These allow process-oriented studies of stratospheric ozone depletion and recovery, including the impacts from both controlled long-lived ozone-depleting substances (ODSs) and emerging issues around uncontrolled very short-lived substances (VSLS). The main updates in DEST are (i) an explicit treatment of 14 of the most important long-lived ODSs; (ii) an inclusion of Br-VSLS emissions and chemistry; and (iii) an inclusion of Cl-VSLS emissions/lower boundary conditions and chemistry. We evaluate the scheme's performance by comparing DEST simulations against analogous runs made with the standard StratTrop scheme, as well as against observational and reanalysis datasets. Overall, our scheme addresses some significant shortcomings in the representation of atmospheric halogens in the standard StratTrop scheme, and will thus be particularly relevant for studies of ozone layer recovery and processes affecting it, in support of future Ozone Assessment reports. [ABSTRACT FROM AUTHOR]

Retrieving high-precision concentrations of atmospheric trace gases from FTIR (Fourier transform infrared) spectrometry requires a precise knowledge of the instrumental performance. In this context, this paper examines the impact on the ozone (O 3) retrievals of several approaches used to characterize the instrumental line shape (ILS) function of ground-based FTIR spectrometers within NDACC (Network for the Detection of Atmospheric Composition Change). The analysis has been carried out at the subtropical Izaña Observatory (IZO, Spain) by using the 20-year time series of the high-resolution FTIR solar absorption spectra acquired between 1999 and 2018. The theoretical quality assessment and the comparison to independent O 3 observations available at IZO (Brewer O 3 total columns and electrochemical concentration cell, ECC, sondes) reveal consistent findings. The inclusion of a simultaneous retrieval of the ILS parameters in the O 3 retrieval strategy allows, on the one hand, a rough instrumental characterization to be obtained and, on the other hand, the precision of the FTIR O 3 products to be slightly improved. The improvement is of special relevance above the lower stratosphere, where the cross-interference between the O 3 vertical distribution and the instrumental performance is more significant. However, it has been found that the simultaneous ILS retrieval leads to a misinterpretation of the O 3 variations on daily and seasonal scales. Therefore, in order to ensure the independence of the O 3 retrievals and the instrumental response, the optimal approach to deal with the FTIR instrumental characterization is found to be the continuous monitoring of the ILS function by means of independent observations, such as gas cell measurements. [ABSTRACT FROM AUTHOR]

In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. OpenIFS is a portable version of ECMWF's global numerical weather prediction model. Modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS with the modified CB05 tropospheric chemistry scheme, the bulk bin tropospheric aerosol module, and the option to use Belgian Assimilation System for Chemical ObsErvations (BASCOE)-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO 2), and aerosol optical depth. The results give basic confidence in the model implementation and configuration. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license. [ABSTRACT FROM AUTHOR]

The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union's Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF's Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO2 emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases. [ABSTRACT FROM AUTHOR]

A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high-impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been on board China's Fengyun-4 series since 2016 and will be on board Europe's Meteosat Third Generation (MTG) series in the 2024 time frame; the United States and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high-temporal-resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in preconvective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) on board Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications. [ABSTRACT FROM AUTHOR]

Observations of vibrationally excited hydroxyl (OH*) emissions are widely used to obtain information on atmospheric dynamics and composition. In this paper, several analytical approximations are presented for characteristics of the hydroxyl layer in the Martian atmosphere, such as OH* concentrations at the maximum and the height of the maximum. Relationships are also given for estimating the influence of various factors on the OH* layer in nighttime conditions. These characteristics are determined by the ambient temperature and the concentration of atomic oxygen, including their vertical gradients. The obtained relationships are applied to the results of numerical modeling using the global atmospheric circulation model to predict the seasonal behavior of the hydroxyl layer on Mars. Based on the modeling data, the annual and intra-annual variations in the concentration of excited hydroxyl and the height of the OH* layer on Mars show both similarities to and considerable differences from those on Earth. The concentration and the height of the maximum in the equatorial, northern, and southern middle latitudes vary with the season, with the maximum concentrations and the lowest height being recorded in the first half of the year. Model calculations confirmed the presence of a peak in the OH* concentration in polar latitudes in winter at a height of approximately 50 km with volume emission densities of 2.1, 1.4, and 0.6 × 104 photons cm–3 s–1 for the transitions of vibrational levels 1–0, 2–1, and 2– 0, respectively. The resulting relationships can be used to analyze measurements and interpret their variations. [ABSTRACT FROM AUTHOR]

In recent years, major progress has been made in measuring weakly absorbing atmospheric trace gases from high spectral resolution space observations. In this paper, we apply the so‐called whitening transformation on spectra of the Infrared Atmospheric Sounding Interferometer, and show that it allows removing most of the climatological background from spectra, leaving a residual that contains those spectral signatures that depart from normality. These can subsequently be attributed to changes in the abundance of trace species. This is illustrated for two diverging cases: (1) a biomass burning plume from the 2019/2020 Australian bushfires, leading to the unambiguous identification of nine reactive trace gases, including a first observation of glycolaldehyde; (2) spectra observed a decade apart, from which changes in eight long‐lived halogenated substances are identified; three of them never observed before by a nadir sounder. Plain Language Summary: In recent years, several techniques have been developed for the detection of gases present in very small quantities in the atmosphere, which has significantly improved our knowledge on atmospheric composition and chemistry. In this paper, we describe a powerful complementary technique that transforms atmospheric spectra to highlight and attribute the spectral signatures of different species. We apply it on spectra measured by the spaceborne Infrared Atmospheric Sounding Interferometer for two different cases: (1) a plume from the 2019/2020 Australian bushfires leading to the clear identification of nine rare gases including a first observation from space of glycolaldehyde; (2) spectra observed a decade apart, of which eight long‐lived halogenated substances were identified; three of them never observed before by a nadir sounder. The detection of these reactive and long‐lived trace gases is very promising for the monitoring of their temporal evolution and the attribution of their sources. Key Points: The whitening transformation allows analyzing anomalies in hyperspectral infrared spectraIn a biomass burning plume we identify nine compounds including a first observation of glycolaldehydeOn spectra observed a decade apart we identify atmospheric changes in eight long‐lived halocarbons [ABSTRACT FROM AUTHOR]

Accurate observations of atmospheric ozone (O 3) are essential to monitor in detail its key role in atmospheric chemistry. The present paper examines the performance of different O 3 retrieval strategies from FTIR (Fourier transform infrared) spectrometry by using the 20-year time series of the high-resolution solar spectra acquired from 1999 to 2018 at the subtropical Izaña Observatory (IZO, Spain) within NDACC (Network for the Detection of Atmospheric Composition Change). In particular, the effects of two of the most influential factors have been investigated: the inclusion of a simultaneous atmospheric temperature profile fit and the spectral O 3 absorption lines used for the retrievals (the broad spectral region of 1000–1005 cm -1 and single micro-windows between 991 and 1014 cm -1). Additionally, the water vapour (H 2 O) interference in O 3 retrievals has been evaluated, with the aim of providing an improved O 3 strategy that minimises its impact and, therefore, could be applied at any NDACC FTIR station under different humidity conditions. The theoretical and experimental quality assessments of the different FTIR O 3 products (total column (TC) amounts and volume mixing ratio (VMR) profiles) provide consistent results. Combining a simultaneous temperature retrieval with the optimal selection of single O 3 micro-windows results in superior FTIR O 3 products, with a precision of better than 0.6 %–0.7 % for O 3 TCs as compared to coincident NDACC Brewer observations taken as a reference. However, this improvement can only be achieved provided the FTIR spectrometer is properly characterised and stable over time. For unstable instruments, the temperature fit is found to exhibit a strong negative influence on O 3 retrievals due to the increase in the cross-interference between the temperature retrieval and instrumental performance (given by the instrumental line shape function and measurement noise), which leads to a worsening of the precision of FTIR O 3 TCs of up to 2 %. This cross-interference becomes especially noticeable beyond the upper troposphere/lower stratosphere, as documented theoretically as well as experimentally by comparing FTIR O 3 profiles to those measured using electrochemical concentration cell (ECC) sondes within NDACC. Consequently, it should be taken into account for the reliable monitoring of the O 3 vertical distribution, especially over long-term timescales. [ABSTRACT FROM AUTHOR]

This study presents a trend analysis of a two-decade (2000–2020) series of surface meteorological and air quality data measured in Rome (Italy). Data series are collected at three sites in Rome downtown and its coastal surroundings. This paper fills the gap due to missing or incomplete recent information about the meteorological and atmospheric composition tendencies in the Rome area. Datasets are subjected to in-depth quality control and to statistical analysis to verify the data homogeneity, whilst trend analysis is performed using the Seasonal Kendall test. The results show a statistically significant positive trend for average air temperature (0.07 °C year−1 in urban and coastal sites), whilst maximum and minimum temperatures increase more in urban (0.10 °C year−1) than in coastal (0.01 °C year−1) environment. The water vapour mixing ratio trend is higher in the city (0.10 g kg−1 year−1) than along the coast (0.03 g kg−1 year−1). The heat index tendency is more pronounced in the Rome centre (0.11 °C year−1) than in the coast (0.06 °C year−1). The monthly cumulative precipitations do not show statistically significant trends. On the other hand, air quality generally improved: surface pollutant concentrations show a significant decrease thanks to the reduction of local emissions (C6H6, − 0.12 µg m−3 year−1; SO2, − 0.09 µg m−3 year−1; CO, − 0.02 mg m−3 year−1; NOx, − 1.28 µg m−3 year−1; NO, − 0.38 µg m−3 year−1; NO2, − 0.60 µg m−3 year−1, PM10, − 0.35 µg m−3 year−1). Only O3 shows a statistically significant positive trend (0.15 µg m−3 year−1), in agreement with wider scale studies. The outcomes suggest that whilst local and national policies contribute to the improvement of air quality, the rising of temperature—as likely consequence of climate change—may be an emerging cause of concern for human thermal discomfort and potential effect on the conservation of tangible immovable heritage. [ABSTRACT FROM AUTHOR]

Knowledge of the composition of the Earth's interior is highly relevant to many geophysical and geochemical problems. Neutrino oscillations are modified in a non-trivial way by the matter effects and can provide valuable and unique information not only on the density but also on the chemical and isotopic composition of the deep regions of the planet. In this paper, we re-examine the possibility of performing an oscillation tomography of the Earth with atmospheric neutrinos and antineutrinos to obtain information on the composition and density of the outer core and the mantle, complementary to that obtained by geophysical methods. Particular attention is paid to the D ″ layer just above the core-mantle boundary and to the water (hydrogen) content in the mantle transition zone. Our analysis is based on a Monte-Carlo simulation of the energy and azimuthal angle distribution of μ -like events generated by neutrinos. Taking as reference a model of the Earth consisting of 55 concentric layers with constant densities determined from the PREM, we evaluate the effect on the number of events due to changes in the composition and density of the outer core and the mantle. To examine the capacity of a detector like ORCA to resolve such variations, we construct regions in planes of two of these quantities where the statistical significance of the discrepancies between the reference and the modified Earth are less than 1 σ . The variations are implemented in such a way that the constraint imposed by both the total mass of the Earth and its moment of inertia are verified. [ABSTRACT FROM AUTHOR]

The present study aims to demonstrate the effects of horizontal grid resolution on the simulated pollution concentration fields over Bulgaria. The computer simulations are performed with a set of models used worldwide—the Weather Research and Forecasting Model (WRF)—the meteorological preprocessor, the Community Multiscale Air Quality Modeling System (CMAQ)—chemical transport model, Sparse Matrix Operator Kernel Emissions (SMOKE)—emission model. The large-scale (background) meteorological data used in the study were taken from the 'NCEP Global Analysis Data' with a horizontal resolution of 1° × 1°. Using the 'nesting' capabilities of the WRF and CMAQ models, a resolution of 9 km was achieved for the territory of Bulgaria by sequentially solving the task in several consecutive nested areas. Three cases are considered in this paper: Case 1: The computer simulations result from the domain with a horizontal resolution (both of the emission source description and the grid) of 27 km.; Case 2: The computer simulations result from the domain with a horizontal resolution (both of the emission source description and the grid) of 9 km.; Case 3: A hybrid case with the computer simulations performed with a grid resolution of 9 km, but with emissions such as in the 27 km × 27 km domain. The simulations were performed, for all the three cases, for the period 2007–2014 year, thus creating an ensemble large and comprehensive enough to reflect the most typical atmospheric conditions with their typical recurrence. The numerical experiments showed the significant impact of the grid resolution not only in the pollution concentration pattern but also in the demonstrated generalized characteristics. Averaged over a large territory (Bulgaria); however, the performances for cases one and two are quite similar. Bulgaria is a country with a complex topography and with several considerably large point sources. Thus, some of the conclusions made, though based on Bulgarian-specific experiments, may be of general interest. [ABSTRACT FROM AUTHOR]

We continue our comprehensive theoretical investigation of the spectral evolution of white dwarfs based on sophisticated simulations of element transport. In this paper, we focus on the transformation of PG 1159 stars into DO/DB white dwarfs due to the gravitational settling of heavy elements and then into DQ white dwarfs through the convective dredge-up of carbon. We study the impact of several physical parameters on the evolution of the surface carbon abundance over a wide range of effective temperatures. In the hot PG 1159 and DO phases, our calculations confirm that the temperature of the PG 1159-to-DO transition depends sensitively on the stellar mass and the wind mass-loss rate. We show that measured carbon abundances of DOZ white dwarfs are mostly accounted for by our models, with the notable exception of the coolest DOZ stars. In the cooler DB and DQ phases, the predicted atmospheric composition is strongly influenced by the stellar mass, the thickness of the envelope, the initial carbon content, the efficiency of convective overshoot, and the presence of residual hydrogen. We demonstrate that, under reasonable assumptions, our simulations reproduce very well the observed carbon abundance pattern of DQ stars, which thus allows us to constrain the extent of the overshoot region in cool helium-rich white dwarfs. We also argue that our calculations naturally explain a number of recent empirical results, such as the relative excess of low-mass DQ stars and the presence of trace hydrogen and/or carbon at the surface of most DC and DZ stars. [ABSTRACT FROM AUTHOR]

In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. For this, modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS: the modified CB05 tropospheric chemistry scheme, the AER bulk-bin tropospheric aerosol module, as well as the option to use BASCOE-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS Reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO2) and aerosol optical depth. The results give basic confidence in the model implementation and configuration. This OpenIFS version with atmospheric composition components is open to the scientific user community under a standard OpenIFS license. [ABSTRACT FROM AUTHOR]

Recent analysis of the planet K2-18 b has shown the presence of water vapour in its atmosphere. While the H2O detection is significant, the Hubble Space Telescope (HST) WFC3 spectrum suggests three possible solutions of very different nature which can equally match the data. The three solutions are a primary cloudy atmosphere with traces of water vapour (cloudy sub-Neptune), a secondary atmosphere with a substantial amount (up to 50% Volume Mixing Ratio) of H2O (icy/water world) and/or an undetectable gas such as N2 (super-Earth). Additionally, the atmospheric pressure and the possible presence of a liquid/solid surface cannot be investigated with currently available observations. In this paper we used the best fit parameters from Tsiaras et al. (Nat. Astron. 3, 1086, 2019) to build James Webb Space Telescope (JWST) and Ariel simulations of the three scenarios. We have investigated 18 retrieval cases, which encompass the three scenarios and different observational strategies with the two observatories. Retrieval results show that twenty combined transits should be enough for the Ariel mission to disentangle the three scenarios, while JWST would require only two transits if combining NIRISS and NIRSpec data. This makes K2-18 b an ideal target for atmospheric follow-ups by both facilities and highlights the capabilities of the next generation of space-based infrared observatories to provide a complete picture of low mass planets. [ABSTRACT FROM AUTHOR]