Atmospheric Model Research Articles

IRDNet: An image-defogging algorithm based on dark channel prior

Abstract Image-defogging, as an important part of computer vision, has been widely used in intelligent driving, target recognition, satellite detection, underwater exploration and so on. Improving the performance of the defogging algorithm based on deep learning has practical significance for the completion of high-level vision tasks. This paper proposes an IRDNet algorithm by improving the image deep learning algorithm based on the RefineDNet framework and designing a new deep learning network structure. The proposed algorithm combines dark channel prior knowledge and atmospheric degradation model to decompose the input fogged images into dark channel images and degraded images, and then carries out feature extraction and detail enhancement for the two images by convolutional neural network. IRDNet introduces a fully connected convolutional structure, attention mechanism, and a pyramid structure to improve the performance of the overall defogging network. By testing on OTS datasets, we compare the IRDNet algorithm with traditional defogging methods, end-to-end deep learning defogging methods, and prior knowledge-guided deep learning defogging methods. Test results show that the performance of IRDNet is better than other methods, and the defogging network shows richer details of texture and more realistic colors.

Optical orthogonal frequency division multiplexing with differential index modulation

This paper proposes a differential index modulation (DIM) scheme to address the complex channel estimation challenges in optical orthogonal frequency division multiplexing with index modulation (OOFDM-IM). The main idea of the DIM scheme is to design a time-frequency dispersive matrix with unitary characteristics and perform index mapping based on the Lemer code principles. By applying differential operations, the DIM scheme enables decoding at the receiver without requiring channel estimation. The paper provides a detailed explanation of the design principles and theoretical bit error rate (BER) of the proposed scheme. Simulations based on the exponential Weibull atmospheric turbulence channel model are conducted to compare the DIM scheme with the existing OOFDM-IM scheme. Additionally, a carefully designed proof-of-concept experiment is performed to further validate the scheme’s effectiveness and feasibility. Both simulation and experimental results demonstrate that, compared to OOFDM-IM, the proposed scheme can entirely avoid complex channel estimation with a maximum signal-to-noise ratio (SNR) loss of no more than 4 dB, even under various turbulence intensities and higher-order modulation scenarios. This provides a valuable reference for OOFDM-IM in complex environments or where channel estimation is challenging.

Addressing atmospheric absorption in adaptive rectangular decomposition.

This paper focuses on the adaptive rectangular decomposition (ARD) scheme, a wave-based method employed for acoustic simulations. ARD holds promise for diverse applications. In architectural design, it can forecast acoustical parameters, facilitating the creation of spaces with superior sound quality. Moreover, in the domain of acoustic virtual reality, ARD can offer users a more immersive and lifelike acoustic environment. This enhancement proves advantageous for all of these applications, enabling the simulation of larger environments with heightened precision. Despite its notable efficiency, ARD faces a significant drawback: the absence of atmospheric absorption modeling. The principal aim of this study is to rectify this limitation, thereby augmenting the capabilities of the ARD algorithm.

Permafrost Region Greenhouse Gas Budgets Suggest a Weak CO2 Sink and CH4 and N2O Sources, But Magnitudes Differ Between Top‐Down and Bottom‐Up Methods

AbstractLarge stocks of soil carbon (C) and nitrogen (N) in northern permafrost soils are vulnerable to remobilization under climate change. However, there are large uncertainties in present‐day greenhouse gas (GHG) budgets. We compare bottom‐up (data‐driven upscaling and process‐based models) and top‐down (atmospheric inversion models) budgets of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) as well as lateral fluxes of C and N across the region over 2000–2020. Bottom‐up approaches estimate higher land‐to‐atmosphere fluxes for all GHGs. Both bottom‐up and top‐down approaches show a sink of CO2 in natural ecosystems (bottom‐up: −29 (−709, 455), top‐down: −587 (−862, −312) Tg CO2‐C yr−1) and sources of CH4 (bottom‐up: 38 (22, 53), top‐down: 15 (11, 18) Tg CH4‐C yr−1) and N2O (bottom‐up: 0.7 (0.1, 1.3), top‐down: 0.09 (−0.19, 0.37) Tg N2O‐N yr−1). The combined global warming potential of all three gases (GWP‐100) cannot be distinguished from neutral. Over shorter timescales (GWP‐20), the region is a net GHG source because CH4 dominates the total forcing. The net CO2 sink in Boreal forests and wetlands is largely offset by fires and inland water CO2 emissions as well as CH4 emissions from wetlands and inland waters, with a smaller contribution from N2O emissions. Priorities for future research include the representation of inland waters in process‐based models and the compilation of process‐model ensembles for CH4 and N2O. Discrepancies between bottom‐up and top‐down methods call for analyses of how prior flux ensembles impact inversion budgets, more and well‐distributed in situ GHG measurements and improved resolution in upscaling techniques.

China's methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China's CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a−1) in 2020 were closer to the official reported figure (53.57 Tg a−1) than to the prior (63.80 Tg a−1), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

Spectroscopic survey of faint planetary-nebula nuclei

We present an analysis of 17 H-rich central stars of planetary nebulae (PNe) observed in our spectroscopic survey of nuclei of faint Galactic PNe carried out at the 10-m Hobby-Eberly Telescope. Our sample includes ten O(H) stars, four DAO white dwarfs (WDs), two DA WDs, and one sdOB star. The spectra were analyzed by means of NLTE model atmospheres, allowing us to derive the effective temperatures, surface gravities, and He abundances of the central stars. Sixteen of them were analyzed for the first time, increasing the number of hot H-rich central stars with parameters obtained through NLTE atmospheric modeling by approximately 20%. We highlight a rare hot DA WD central star, Abell 24, which has a Teff likely in excess of 100 kK, as well as the unusually high gravity mass of 0.70 ± 0.05 M⊙ for the sdOB star Pa 3, which is significantly higher than the canonical extreme horizontal-branch star mass of ≈0.48 M⊙. By investigating Zwicky Transient Facility light curves, which were available for our 15 northern objects, we found none of them show a periodic photometric variability larger than a few hundredths of a magnitude. This could indicate that our sample mainly represents the hottest phase during the canonical evolution of a single star when transitioning from an asymptotic giant branch star into a WD. We also examined the spectral energy distributions, detecting an infrared excess in six of the objects, which could be due to a late-type companion or to hot (≈103 K) and/or cool (≈100 K) dust. We confirm previous findings that spectroscopic distances are generally higher than found through Gaia astrometry, a discrepancy that deserves to be investigated systematically.

Evaluating the reliability of UAV-based carbon dioxide measurements in the lower troposphere

Vertical profiles of carbon dioxide (CO2) were captured in rural Ontario, Canada, using an Aeroqual Series 500 monitor mounted onboard an Uncrewed Aerial Vehicle (UAV). Measurements at various altitudes within the Transport Canada regulations height limit (5m – 95m) were compared to simultaneous measurements taken by an identical sensor at ground-level. Results demonstrate a high degree of agreement between onboard and ground-level observations, with a mean different of -6.02 ppm, and the majority of differences falling within the sensor’s factory calibration accuracy (±10 ppm + 5%). The study also demonstrates the ability to capture the homogeneity of CO2 in the lower troposphere, with Intraclass Correlation Coefficients (ICC) exceeding 0.75 at all altitudes, without the addition of turbulence by UAV flight. The vertical profiles captured in this study validate the use of UAV-based measurements for understanding the distribution and transport of greenhouse gases near the surface of the Earth. This research has implications for routine air quality monitoring to improve atmospheric models, environmental impact assessments and the development of targeted emission reduction strategies.

A regional ocean–atmosphere coupled model using CMA-TRAMS and LICOM: Preliminary results for tropical cyclone gale prediction over the northern South China Sea

This paper provides a comparative analysis of the performance of a high-resolution regional ocean–atmosphere coupled model in predicting tropical cyclone (TC) gales over the northern South China Sea. The atmosphere and ocean components of the coupled system are represented by the China Meteorological Administration's Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) and the LASG/IAP Climate system Ocean Model (LICOM), respectively. The Ocean Atmosphere Sea Ice Soil VersionH 3 (OASIS3) software has been utilized for the exchange of momentum, heat, and freshwater fluxes between these two components. An assessment of the coupled model's three-day predictions for five TCs’ gales was conducted. Preliminary findings indicate that the predicted TC tracks show less sensitivity to oceanic influences than the predicted TC intensities. Significant improvement in predicting the surface TC gales has been achieved through coupling the ocean model. This improvement is attributed to the impact of the warmer ocean's effect on TC intensification, counteracting the cooling effect of the cold wake. In summary, coupling has enhanced the model's predictive capabilities for TC gales. A detailed assessment of the coupled model's performance in predicting other tropical weather phenomena is forthcoming.摘要本文对一个具有我国自主知识产权的高分辨率区域海气耦合模式在南海北部台风大风预报性能进行对比分析. 该耦合模式的大气和海洋部分分别为中国气象局南海台风模式 (CMA-TRAMS) 和中国科学院大气物理研究所的海洋模式 (LICOM), 并用OASIS耦合器实现海气界面动量, 热量和淡水通量的交换. 本文对海气耦合模式预报5个台风的24, 48和72小时大风的性能进行了检验评估. 初步结果表明, 预报的台风路径对海洋影响的敏感性低于台风强度. 耦合模式使预报台风大风的准确性得到显著提高, 这一改进归结于海气耦合模式更为准确的海表温度对台风增强的影响, 从而抵消了冷尾流的降温效应. 总而言之, 耦合模式增强了模式对台风大风的预报能力. 关于海气耦合模式在其他热带天气中的预报能力评估将在未来工作中展示.

A network of cooler white dwarfs as infrared standards for flux calibration

ABSTRACT The accurate flux calibration of observational data is vital for astrophysics and cosmology because absolute flux uncertainties of stellar standards propagate into scientific results. With the ever higher precision achieved by telescopic missions (e.g. JWST) in the infrared (IR), suitable calibrators are required for this regime. The basis of the Hubble Space Telescope (HST) flux scale is defined by model fits of three hot (${T_\mathrm{eff}} > 30\, 000$ K) hydrogen-atmosphere (DA) white dwarfs, which achieve an accuracy better than 1 per cent at optical wavelengths but falls below this level in the IR range. We present a network of 17 cooler DA white dwarfs with ${T_\mathrm{eff}} < 20\, 000$ K as spectrophotometric flux standards that are equally, if not more, accurate at IR wavelengths. Cooler white dwarfs do not suffer from non-local thermal equilibrium effects in continuum flux or from ultraviolet metal line blanketing, have a larger sky density, are generally closer to Earth with little or negligible interstellar reddening, and have energy distributions peaking in the optical or near-IR. Using the latest grid of DA local thermal equilibrium atmosphere models with three-dimensional convection, the observed Space Telescope Imaging Spectrometer (STIS) and Wide Field Camera 3 (WFC3) fluxes of our network are accurate to 3 per cent over most of the range $1450\!\! - \!\!16\, 000$ Å, with a median standard deviation of 1.41 per cent. Fitting the HST STIS and WFC3 white dwarf spectral energy distributions (SEDs) and Balmer lines independently yields SEDs that agree within $3\sigma$, which demonstrates the precision of the models for our network.

JWST-TST High Contrast: Spectroscopic Characterization of the Benchmark Brown Dwarf HD 19467 B with the NIRSpec Integral Field Spectrograph

We present the atmospheric characterization of the substellar companion HD 19467 B as part of the pioneering James Webb Space Telescope Guaranteed Time Observer program to obtain moderate resolution spectra (R ∼ 2700, 3–5 μm) of a high-contrast companion with the NIRSpec integral field unit (IFU). HD 19467 B is an old, ∼9 Gyr, companion to a solar-type star with multiple measured dynamical masses. The spectra show detections of CO, CO2, CH4, and H2O. We forward model the spectra using Markov Chain Monte Carlo methods and atmospheric model grids to constrain the effective temperature and surface gravity. We then use NEWERA-PHOENIX grids to constrain nonequilibrium chemistry parameterized by K zz and explore molecular abundance ratios of the detected molecules. We find an effective temperature of 1103 K, with a probable range from 1000 to 1200 K, a surface gravity of 4.50 dex, with a range of 4.14–5.00, and deep vertical mixing, log10(K zz ), of 5.03, with a range of 5.00–5.44. All molecular mixing ratios are approximately solar, leading to a C/O ∼ 0.55, which is expected from a T5.5 brown dwarf. Finally, we calculate an updated dynamical mass of HD 19467 B using newly derived NIRCam astrometry, which we find to be 71.6−4.6+5.3MJup , in agreement with the mass range we derive from evolutionary models, which we find to be 63–75 M Jup. These observations demonstrate the excellent capabilities of the NIRSpec IFU to achieve detailed spectral characterization of substellar companions at high contrast close to bright host stars, in this case at a separation of ∼1.″6 with a contrast of 10−4 in the 3–5 μm range.

Unveiling the HD 95086 system at mid-infrared wavelengths with JWST/MIRI

Context. Mid-infrared imaging of exoplanets and disks is now possible with the coronographs of the Mid-InfraRed Instrument (MIRI) on the James Webb Space Telescope (JWST). This wavelength range unveils new features of young directly imaged systems and allows us to obtain new constraints for characterizing the atmosphere of young giant exoplanets and associated disks. Aims. These observations aim to characterize the atmosphere of the planet HD 95086 b by adding mid-infrared information so that the various hypotheses about its atmospheric parameters values can be unraveled. Improved images of cirsumstellar disks are provided. Methods. We present the MIRI coronagraphic imaging of the system HD 95086 obtained with the F1065C, F1140, and F2300C filters at central wavelengths of 10.575 µm, 11.3 µm, and 23 µm, respectively. We explored the method for subtracting the stellar diffraction pattern in the particular case when bright dust emitting at short separation is present. Furthermore, we compared different methods for extracting the photometry of the planet. Using the atmospheric models Exo-REM and ATMO, we measured the atmospheric parameters of HD 95086 b. Results. The planet HD 95086 b is detected at the two shortest MIRI wavelengths F1065C and F1140C. The contribution from the inner disk of the system is also detected. It is similar to that in the HR 8799 system. The outer colder belt is imaged at 23 µm. Background objects are observed in all filters. The mid-infrared photometry provides better constraints on the atmospheric parameters. We evaluate a temperature of 800–1050 K, consistent with one previous hypothesis that only used near-infrared data. The radius measurement of 1.0–1.14 RJup is better aligned with evolutionary models, but still smaller than predicted. These observations allow us to refute the hypothesis of a warm circumplanetary disk. Conclusions. HD 95086 is one of the first exoplanetary systems to be revealed at mid-infrared wavelengths. This highlights the interests and challenges of observations at these wavelengths.

Observations of Extremely Metal-poor O Stars: Weak Winds and Constraints for Evolution Models

Metal-poor massive stars drive the evolution of low-mass galaxies, both locally and at high redshift. However, quantifying the feedback they impart to their local surroundings remains uncertain because models of stellar evolution, mass loss, and ionizing spectra are unconstrained by observations below 20% solar metallicity (Z ⊙). We present new Keck Cosmic Web Imager optical spectroscopy of three O-type stars in the nearby dwarf galaxies Leo P, Sextans A, and WLM, which have gas-phase oxygen abundances of 3%–14% Z ⊙. To characterize their fundamental stellar properties and radiation-driven winds, we fit PoWR atmosphere models to the optical spectra simultaneously with Hubble Space Telescope far-ultraviolet (FUV) spectra and multiwavelength photometry. We find that all three stars have effective temperatures consistent with their spectral types and surface gravities typical of main-sequence dwarf stars. Yet, the combination of those inferred parameters and luminosity for the two lower-Z stars is not reproduced by stellar evolution models, even those that include rotation or binary interactions. The scenario of multiple-star systems is difficult to reconcile with all available data, suggesting that these observations pose a challenge to current evolution models. We highlight the importance of validating the relationship between stellar mass, temperature, and luminosity at very low Z for accurate estimates of ionizing photon production and spectral hardness. Finally, all three stars’ FUV wind profiles reveal low mass-loss rates and terminal wind velocities in tension with expectations from widely adopted radiation-driven wind models. These results provide empirical benchmarks for future development of mass-loss and evolution models for metal-poor stellar populations.

Ensemble Based Estimation of Wet Refractivity Indices Using a Functional Model Approach

AbstractThe estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real‐time positioning techniques. Traditionally, solving the inverse tomography problem to estimate these atmospheric parameters has been challenging due to its ill‐posed nature and high computational demands, necessitating additional constraints. To overcome these challenges, the data assimilation method is proposed here to integrate Global Navigation Satellite System (GNSS) observations into a background model. In this study, the Ensemble Kalman Filter (EnKF) was served as the assimilation core to reduce the computational load and to enable the epoch‐wise estimation of wet refractivity indices. The Global Pressure and Temperature 3 (GPT3w) model was utilized as the background, and wet refractivity indices at each epoch were transformed into B‐spline coefficients, representing state vector parameters. Subsequently, GNSS derived zenith wet delay (ZWD) values were integrated into the model using the EnKF method. The study's region encompassed the western parts of Europe and incorporated approximately 893 GNSS stations. Evaluation spanned from 1 January 2017 to 31 December 2017. The estimated wet refractivity indices from the proposed method were compared with observations from 16 existing radiosonde stations, radio occultation data, and ZWD values from the 47 selected GNSS test stations. Additionally, calculated ZWD values, resulting from the integration of wet refractivity indices, were compared to the ZWD values from 47 test stations in the study region. The numerical results demonstrated that the proposed method achieved a root mean square error value of approximately 2.6 ppm, which was nearly 49% and 18% lower than that of the considered empirical and numerical atmospheric models, respectively.

A Method for Interpreting the Role of Parameterized Turbulence on Global Metrics in the Community Earth System Model

AbstractThe parameterization of subgrid‐scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one‐at‐a‐time (MOAT) parameter sensitivity analysis using short‐term (3‐day), initialized hindcasts of CAM6‐CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally‐averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid‐latitude storm tracks). We next evaluate several experimental 20‐year simulations of CAM6‐CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short‐term and long‐term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long‐term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study.

On the use of kolmogorov–arnold networks for adapting wind numerical weather forecasts with explainability and interpretability: application to madeira international airport

Abstract This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditional numerical weather prediction models, which often fail to accurately forecast localized wind patterns. Using the Kolmogorov-Arnold Network model led to a substantial reduction in wind speed and direction forecast errors, with a performance that reached a 48.5% improvement to the Global Forecast System 3 h nowcast, considering the mean squared error. A key outcome of this study comes from the model’s ability to generate mathematical formulas that provide insights into the physical and mathematical dynamics influencing local wind patterns and improve the transparency, explainability, and interpretability of the employed machine learning models for atmosphere modeling.

Evaluation of Autoconversion Representation in E3SMv2 Using an Ensemble of Large‐Eddy Simulations of Low‐Level Warm Clouds

AbstractIn numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate , which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local is commonly parameterized as a function of DSD moments: cloud droplet number and mass concentrations. When applied in a large‐scale model, the grid‐mean must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large‐eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local , as well as its spatial variability. The analysis of an ensemble of warm low‐level cloud cases shows that the E3SMv2 formulation represents the reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM‐tuned local rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of and its co‐variability with in parameterizing the grid‐mean .

The Wanderer: Charting WASP-77A b’s Formation and Migration Using a System-wide Inventory of Carbon and Oxygen Abundances

The elemental and isotopic abundances of volatiles like carbon, oxygen, and nitrogen may trace a planet’s formation location relative to H2O, CO2, CO, NH3, and N2 “snowlines,” or the distance from the star at which these volatile elements sublimate. By comparing the C/O and 12C/13C ratios measured in giant exoplanet atmospheres to complementary measurements of their host stars, we can determine whether the planet inherited stellar abundances from formation inside the volatile snowlines, or nonstellar C/O and 13C enrichment characteristic of formation beyond the snowlines. To date, there are still only a handful of exoplanet systems where we can make a direct comparison of elemental and isotopic CNO abundances between an exoplanet and its host star. Here, we present a 12C/13C abundance analysis for host star WASP-77A (whose hot Jupiter’s 12C/13C abundance was recently measured). We use MARCS stellar atmosphere models and the radiative transfer code TurboSpectrum to generate synthetic stellar spectra for isotopic abundance calculations. We find a 12C/13C ratio of 51 ± 6 for WASP-77A, which is subsolar (∼91) but may still indicate 13C enrichment in its companion planet WASP-77A b (12C/13C = 26 ± 16, previously reported). Together with the inventory of carbon and oxygen abundances in both the host and companion planet, these chemical constraints point to WASP-77A b’s formation beyond the H2O and CO2 snowlines and provide chemical evidence for the planet’s migration to its current location ∼0.024 au from its host star.

Enhancing Global Simulation of Smoke Injection Height for Intense Pyro‐Convection Through Coupling an Improved One‐Dimensional Plume Rise Model in CAM‐chem

AbstractThe impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi‐angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro‐convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one‐dimensional plume‐rise model (1‐D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp‐10‐585‐2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1‐D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1‐D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM‐chem). The coupled CAM‐chem‐PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1‐D PRM while saw a mere 15% increase of computation time.

Massive White Dwarfs in the 100 pc Sample: Magnetism, Rotation, Pulsations, and the Merger Fraction

We present a detailed model atmosphere analysis of massive white dwarfs with M > 0.9 M ⊙ and T eff ≥ 11,000 K in the Montreal White Dwarf Database 100 pc sample and the Pan-STARRS footprint. We obtained follow-up optical spectroscopy of 109 objects with no previous spectral classification in the literature. Our spectroscopic follow-up is now complete for all 204 objects in the sample. We find 118 normal DA white dwarfs, including 45 massive DAs near the ZZ Ceti instability strip. There are no normal massive DBs: the six DBs in the sample are strongly magnetic and/or rapidly rotating. There are 20 massive DQ white dwarfs in our sample, and all are found in the crystallization sequence. In addition, 66 targets are magnetic (32% of the sample). We use magnetic white dwarf atmosphere models to constrain the field strength and geometry using offset dipole models. We also use magnetism, kinematics, and rotation measurements to constrain the fraction of merger remnant candidates among this population. The merger fraction of this sample increases from 25% for 0.9–1 M ⊙ white dwarfs to 49% for 1.2–1.3 M ⊙. However, this fraction is as high as 78−7+4 % for 1.1–1.2 M ⊙ white dwarfs. Previous works have demonstrated that 5%–9% of high-mass white dwarfs stop cooling for ∼8 Gyr due to the 22Ne distillation process, which leads to an overdensity of Q-branch stars in the solar neighborhood. We demonstrate that the overabundance of the merger remnant candidates in our sample is likely due to the same process.

Analyzing Stellar Spectra for PRV by Accurate Modeling and Retrieval of Telluric Absorption Features

Ground-based Precision Radial Velocity (PRV) measurements are inevitably impeded by contamination from telluric absorption features, particularly in the infrared region. Thus, it is crucial to improve modeling of the telluric absorption features down to the spectral noise level. As part of the efforts towards improved PRV measurements, we have taken an existing atmospheric trace gas retrieval algorithm (GFIT) and have successfully adapted it to fit the telluric absorption features in stellar spectra down to the spectral noise level (typically ∼1%). We have established a stellar spectral fitting processing pipeline, Stellar-GFIT, to analyze a series of stellar spectra observed by two spectrographs, PARVI (1.1–1.76 μm) commissioned at the Palomar Observatory (Palomar Mountain, CA) and iSHELL (1–5 μm) deployed on the IRTF (Mauna Kea, HI). For this, we have (1) implemented a Gaussian instrumental line shape function, (2) generated atmospheric models (consisting of temperature, pressure, and volume mixing ratios of all the known trace gases) for the particular observation sites and times, (3) employed the most up-to-date spectroscopic parameters in the target spectral regions, and finally (4) developed a series of spectral fitting intervals of ∼60 cm−1 width, i.e., micro-windows, customized to the individual orders of each spectrograph. Stellar-GFIT is also capable of handling non-telluric features, such as transitions from a gas cell placed in the starlight beam and stellar features if a model spectrum template is available for the target star. We present spectrum fits from the observations of various target stars and discuss the performance and advantages of our novel approach. One of the major strengths of Stellar-GFIT is an ability to adjust the abundance of atmospheric trace gases simultaneously with determining the stellar doppler shift, mitigating any adverse impacts of short-timescale variations of water vapor.