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Regional trends and drivers of the global methane budget
Abstract The ongoing development of the Global Carbon Project (GCP) global methane (CH 4 ) budget shows a continuation of increasing CH 4 emissions and CH 4 accumulation in the atmosphere during 2000–2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top‐down (TD) (atmospheric and transport model‐based) and bottom‐up (BU) (inventory‐ and process model‐based) CH 4 emission estimates demonstrates robust temporal trends with CH 4 emissions increasing in 16 of the 19 regions. Five regions—China, Southeast Asia, USA, South Asia, and Brazil—account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH 4 yr −1 in 2008–2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH 4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH 4 burden and the greatest opportunities for greenhouse gas abatement.
BayesMT: A Probabilistic Bayesian Framework for the Seismic Moment Tensor
Moment tensors (MTs) have long been used in earthquake and explosion source analysis, and there has been a renewed interest in how they can inform us about the seismic source, particularly in the geophysical monitoring community due to its application in event identification and yield analysis. However, parameter uncertainties in seismic MT inversion are rarely available. The inverse procedure often does not quantify MT model errors such as event location, data noise and Earth model that are essential for estimating solution robustness. To address this need, we propose to adopt the Bayesian probabilistic framework to incorporate uncertainties in MT inversions. In this study, we present the theoretical background of a probabilistic Bayesian framework for MT inversion accounting for model and measurements errors and illustrate the implementation of the method using a synthetic example.
Operating Experience Data Analysis for Digital Instrumentation and Control System Reliability and Risk Assessment in Nuclear Power Plants
The implementation of advanced digital instrumentation and control (DI&C) systems in U.S. nuclear power plants (NPPs) can bring significant advancements in reliability, monitoring, and control capabilities. However, these systems also introduce new challenges, particularly in assessing risks such as common-cause failures (CCFs) and establishing robust reliability estimates for DI&C components. Addressing these challenges is critical for ensuring the safe and efficient operation of NPPs. Recently, Idaho National Laboratory was tasked by the U.S. Nuclear Regulatory Commission (NRC) to conduct a DI&C reliability study using operating experience data from the nuclear industry. The two operating experience data sources for the study are the Institute of Nuclear Power Operations’ Industry Reporting and Information System (IRIS) and the NRC’s Licensee Event Report database which is hosted at Idaho National Laboratory at https://lersearch.inl.gov/LERSearchCriteria.aspx. This report provides a comprehensive examination of DI&C systems, including their architecture, operational advantages, and associated challenges. It reviews existing industry DI&C studies and failure mode taxonomies, along with reliability data from various industries. Through a detailed analysis of these databases, the study provides insights into DI&C system performance. Considerations should be given to incorporate DI&C failure data into the NRC's Integrated Data Collection and Coding System and updating the Reliability and Availability Data System to support ongoing DI&C reliability studies. Recommendations are also provided for modeling DI&C reliability and CCF in probabilistic risk assessment, thereby supporting risk-informed decision-making and enhancing the reliability and safety of NPPs.
Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data
The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.
A KELT–TESS Eclipsing Binary in a Young Triple System Associated with the Local “Stellar String” Theia 301
HD 54236 is a nearby, wide common-proper-motion visual pair that has been previously identified as likely being very young by virtue of strong X-ray emission and lithium absorption. Here, we report the discovery that the brighter member of the wide pair, HD 54236A, is itself an eclipsing binary (EB), comprising two near-equal solar-mass stars on a 2.4 days orbit. It represents a potentially valuable opportunity to expand the number of benchmark-grade EBs at young stellar ages. Using new observations of Ca ii H and K emission and lithium absorption in the wide K-dwarf companion, HD 54236B, we obtain a robust age estimate of 225 ± 50 Myr for the system. This age estimate and Gaia proper motions show HD 54236 is associated with Theia 301, a newly discovered local “stellar string,” which itself may be related to the AB Dor moving group through shared stellar members. Applying this age estimate to AB Dor itself alleviates reported tension between observation and theory that arises for the luminosity of AB Dor C when younger age estimates are used.
The Poor Old Heart of the Milky Way
Our Milky Way should host an ancient, metal-poor, and centrally concentrated stellar population, which reflects the star formation and enrichment in the few most massive progenitors that coalesced at high redshift to form the proto-Galaxy. While metal-poor stars are known to reside in the inner few kiloparsecs of our Galaxy, current data do not yet provide a comprehensive picture of such a metal-poor "heart" of the Milky Way. We use information from Gaia Data Release 3, especially the XP spectra, to construct a sample of 2 million bright (G BP < 15.5 mag) giant stars within 30° of the Galactic center (GC) with robust [M/H] estimates, δ[M/H] ≲ 0.1. For ~1.25 million stars we calculate orbits from Gaia Radial Velocity Spectrometer velocities and astrometry. This sample reveals an extensive, ancient, and metal-poor population that includes ~18,000 stars with -2.7 < [M/H] < -1.5, representing a stellar mass of ≳5 × 10 7 M ⊙ . The spatial distribution of these [M/H] < -1.5 stars has a Gaussian extent of only σ R$_{GC}$ around the GC, with most orbits confined to the inner Galaxy. At high orbital eccentricities, there is clear evidence for accreted halo stars in their pericentral orbit phase. Most stars show [α/Fe] enhancement and [Al/Fe]–[Mn/Fe] abundances expected for an origin in the more massive portions of the proto-Galaxy. Stars with [M/H] < -2 show no net rotation, whereas those with [M/H] ~ -1 are rotation dominated. These central, metal-poor stars most likely predate the oldest disk population (τ age ≈ 12.5 Gyr), which implies that they formed at z ≳ 5, forging the proto-Milky Way.
Comparison of planetary boundary layer height from ceilometer with ARM radiosonde data
Abstract. Ceilometer measurements of aerosol backscatter profiles have been widely used to provide continuous planetary boundary layer height (PBLHT) estimations. To investigate the robustness of ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) ceilometer and balloon-borne sounding data at ARM fixed-location atmospheric observatories and from ARM mobile facilities deployed around the world for various field campaigns. These observatories cover from the tropics to the polar regions and over both ocean and land surfaces. Statistical comparisons of ceilometer-estimated PBLHTs from the Vaisala CL31 ceilometer data with radiosonde-estimated PBLHTs from the ARM PBLHT-SONDE Value-added Product (VAP) are performed under different atmospheric conditions including stable and unstable atmospheric boundary layer, low-level cloud-free conditions, and cloudy conditions at these ARM observatories. Under unstable conditions, good comparisons are found between ceilometer- and radiosonde-estimated PBLHTs at ARM low- and mid-latitude land observatories. However, it is still challenging to obtain reliable PBLHT estimations over ocean surfaces even using radiosonde data. Under stable conditions, ceilometer- and radiosonde-estimated PBLHTs have weak correlations. We compare different PBLHT estimations utilizing the Heffter, the Liu–Liang, and the bulk Richardson number methods applied to radiosonde data with ceilometer-estimated PBLHT. We find that ceilometer-estimated PBLHT compares better with the Liu–Liang method under unstable conditions and compares better with the bulk Richardson number method under stable conditions.
Evaluating Interventions Through Long-Term Human Mobility Data: Initial Methods and Lessons Learned
Evaluating interventions through long-term human mobility data: initial methods and lessons learned. The tool generated a rich dataset for research and evaluation of behavior that would otherwise be unobserved. The data enabled robust energy estimation of the program. Practice revealed a need for further refinement to reduce participant data input burden.
Direct Estimation of Parameters in ODE Models Using WENDy: Weak-Form Estimation of Nonlinear Dynamics
Abstract We introduce the Weak-form Estimation of Nonlinear Dynamics (WENDy) method for estimating model parameters for non-linear systems of ODEs. Without relying on any numerical differential equation solvers, WENDy computes accurate estimates and is robust to large (biologically relevant) levels of measurement noise. For low dimensional systems with modest amounts of data, WENDy is competitive with conventional forward solver-based nonlinear least squares methods in terms of speed and accuracy. For both higher dimensional systems and stiff systems, WENDy is typically both faster (often by orders of magnitude) and more accurate than forward solver-based approaches. The core mathematical idea involves an efficient conversion of the strong form representation of a model to its weak form, and then solving a regression problem to perform parameter inference. The core statistical idea rests on the Errors-In-Variables framework, which necessitates the use of the iteratively reweighted least squares algorithm. Further improvements are obtained by using orthonormal test functions, created from a set of $$C^{\infty }$$ C ∞ bump functions of varying support sizes.We demonstrate the high robustness and computational efficiency by applying WENDy to estimate parameters in some common models from population biology, neuroscience, and biochemistry, including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark model. Software and code for reproducing the examples is available at https://github.com/MathBioCU/WENDy .
Using Ensemble Data Assimilation to Estimate Transient Hydrologic Exchange Flow Under Highly Dynamic Flow Conditions
Abstract Quantifying dynamic hydrologic exchange flows (HEFs) within river corridors that experience high‐frequency flow variations caused by dam regulations is important for understanding the biogeochemical processes at the river water and groundwater interfaces. Heat has been widely used as a tracer to infer steady‐state flow velocities through analytical solutions of heat transport defined by the diurnal temperature signals. Under sub‐daily dynamic flow conditions, however, such analytical solutions are not applicable due to the violation of their fundamental assumptions. In this study, we developed a data assimilation‐based approach to estimate the sub‐daily flux under highly dynamic flow conditions using multi‐depth temperature observations at a 5‐min resolution. If the hydraulic gradient is measured, Darcy's law was used to calculate the flux with permeability estimated from temperature responses below the riverbed. Otherwise, flux was estimated directly by assimilating multi‐depth temperature data at 1‐ or 2‐hr time intervals assuming one‐dimensional flow and heat transport governing equation. By comparing estimated fluxes with model‐generated synthetic truth, we demonstrated that both schemes have robust performance in estimating fluxes under highly dynamic flow conditions. This data assimilation‐based flux estimation method was able to capture the vertical sub‐daily fluxes using multi‐depth high‐resolution temperature data alone, even in the presence of multi‐dimensional flow. This approach has been successfully applied to real field temperature data collected at the Hanford site, which experiences highly dynamic HEFs. Our study shows the promise of adopting distributed 1‐D temperature monitoring to capture spatial and temporal exchange dynamics in river corridors at a watershed scale or beyond.
DESI DR1 Ly α 1D power spectrum: Validation of estimators
The Data Release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI) is the largest sample to date for small-scale Lyα forest cosmology, accessed through its one-dimensional power spectrum (P 1D ). The Lyα forest P 1D is extracted from quasar spectra that are highly inhomogeneous (both in wavelength and between quasars) in noise properties due to intrinsic properties of the quasar, atmospheric and astrophysical contamination, and also sensitive to low-level details of the spectral extraction pipeline. We employ two estimators in DR1 analysis to measure P 1D : the optimal estimator and the fast Fourier transform (FFT) estimator. To ensure robustness of our DR1 measurements, we validate these two power spectrum and covariance matrix estimation methodologies against the challenging aspects of the data. First, using a set of 20 synthetic 1D realizations of DR1, we derive the masking bias corrections needed for the FFT estimator and the continuum fitting bias needed for both estimators. We demonstrate that both estimators, including their covariances, are unbiased with these corrections using the Kolmogorov-Smirnov test. Second, we substantially extend our previous suite of CCD image simulations to include 675,000 quasars, allowing us to accurately quantify the pipeline's performance. This set of simulations reveals biases at the highest k values, corresponding to a resolution error of a few percent. We base the resolution systematics error budget of DR1 P 1D on these values, but do not derive corrections from them since the simulation fidelity is insufficient for precise corrections.
Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological implications from two decades of spectroscopic surveys at the Apache Point Observatory
We present the cosmological implications from final measurements of clustering using galaxies, quasars, and Lyα forests from the completed Sloan Digital Sky Survey (SDSS) lineage of experiments in large-scale structure. These experiments, composed of data from SDSS, SDSS-II, BOSS, and eBOSS, offer independent measurements of baryon acoustic oscillation (BAO) measurements of angular-diameter distances and Hubble distances relative to the sound horizon, r d , from eight different samples and six measurements of the growth rate parameter, fσ 8 , from redshift-space distortions (RSD). This composite sample is the most constraining of its kind and allows us to perform a comprehensive assessment of the cosmological model after two decades of dedicated spectroscopic observation. We show that the BAO data alone are able to rule out dark-energy-free models at more than eight standard deviations in an extension to the flat, ΛCDM model that allows for curvature. When combined with Planck Cosmic Microwave Background (CMB) measurements of temperature and polarization, under the same model, the BAO data provide nearly an order of magnitude improvement on curvature constraints relative to primary CMB constraints alone. Independent of distance measurements, the SDSS RSD data complement weak lensing measurements from the Dark Energy Survey (DES) in demonstrating a preference for a flat ΛCDM cosmological model when combined with Planck measurements. The combined BAO and RSD measurements indicate σ 8 =0.85±0.03, implying a growth rate that is consistent with predictions from Planck temperature and polarization data and with General Relativity. When combining the results of SDSS BAO and RSD, Planck, Pantheon Type Ia supernovae (SNe Ia), and DES weak lensing and clustering measurements, all multiple-parameter extensions remain consistent with a ΛCDM model. Regardless of cosmological model, the precision on each of the three parameters, Ω Λ , H 0 , and σ 8 , remains at roughly 1%, showing changes of less than 0.6% in the central values between models. In a model that allows for free curvature and a time-evolving equation of state for dark energy, the combined samples produce a constraint Ω K =-0.0022±0.0022. The dark energy constraints lead to w 0 =-0.909±0.081 and w a =$-0.49_{-0.30}^{+0.35}$, corresponding to an equation of state of w p =-1.018±0.032 at a pivot redshift z p =0.29 and a Dark Energy Task Force Figure of Merit of 94. The inverse distance ladder measurement under this model yields H 0 =68.18±0.79 km s -1 Mpc -1 , remaining in tension with several direct determination methods; the BAO data allow Hubble constant estimates that are robust against the assumption of the cosmological model. In addition, the BAO data allow estimates of H 0 that are independent of the CMB data, with similar central values and precision under a ΛCDM model. Our most constraining combination of data gives the upper limit on the sum of neutrino masses at Σm ν <0.115 eV (95% confidence). Finally, we consider the improvements in cosmology constraints over the last decade by comparing our results to a sample representative of the period 2000-2010. We compute the relative gain across the five dimensions spanned by w, Ω k , Σm ν , H 0 , and σ 8 and find that the SDSS BAO and RSD data reduce the total posterior volume by a factor of 40 relative to the previous generation. Finally, adding again the Planck, DES, and Pantheon SN Ia samples leads to an overall contraction in the five-dimensional posterior volume of 3 orders of magnitude.
Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms
Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.
Light in the dark forest. Part I. An efficient optimal estimator for 3D Lyman-alpha forest power spectrum
The highly anisotropic nature of the Lyman-alpha (Lyα) forest data introduces a complex survey window function that complicates the measurement of the three-dimensional power spectrum ( P 3D ). In this paper, we present the first fully optimal estimator for P 3D , which exactly deconvolves the survey window function and marginalizes contaminated modes that distort the power spectrum. Our approach adapts optimal estimator techniques developed for the 2D cosmic microwave background data to the 3D case. To achieve computational feasibility, we employ the conjugate gradient method and implement the P 3 M formalism to handle large-scale and small-scale operations separately and efficiently. We validate our estimator using Monte Carlo mocks and Gaussian simulations, demonstrating its accuracy and computational efficiency. We confirm that mode marginalization eliminates distortions arising from quasar continuum errors and delivers robust power spectrum estimation, though it also inflates errors at large scales. This first implementation works in the flat-sky case; we discuss the remaining steps needed to generalize it to the curved-sky case. This formalism offers a foundation for the Lyα forest P 3D measurements and a new path toward cosmological constraints from the Lyα forest data.
Three dimensional cluster analysis for atom probe tomography using Ripley’s K-function and machine learning
The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley’s K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K (r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K (r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. Here we find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K (r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.
Source scaling comparison and validation in Central Italy: data intensive direct S waves versus the sparse data coda envelope methodology
SUMMARY Robustness of source parameter estimates is a fundamental issue in understanding the relationships between small and large events; however, it is difficult to assess how much of the variability of the source parameters can be attributed to the physical source characteristics or to the uncertainties of the methods and data used to estimate the values. In this study, we apply the coda method by Mayeda et al. using the coda calibration tool (CCT), a freely available Java-based code (https://github.com/LLNL/coda-calibration-tool) to obtain a regional calibration for Central Italy for estimating stable source parameters. We demonstrate the power of the coda technique in this region and show that it provides the same robustness in source parameter estimation as a data-driven methodology [generalized inversion technique (GIT)], but with much fewer calibration events and stations. The Central Italy region is ideal for both GIT and coda approaches as it is characterized by high-quality data, including recent well-recorded seismic sequences such as L'Aquila (2009) and Amatrice–Norcia–Visso (2016–2017). This allows us to apply data-driven methods such as GIT and coda-based methods that require few, but high-quality data. The data set for GIT analysis includes ∼5000 earthquakes and more than 600 stations, while for coda analysis we used a small subset of 39 events spanning 3.5 < Mw < 6.33 and 14 well-distributed broad-band stations. For the common calibration events, as well as an additional 247 events (∼1.7 < Mw < ∼5.0) not used in either calibration, we find excellent agreement between GIT-derived and CCT-derived source spectra. This confirms the ability of the coda approach to obtain stable source parameters even with few calibration events and stations. Even reducing the coda calibration data set by 75 per cent, we found no appreciable degradation in performance. This validation of the coda calibration approach over a broad range of event size demonstrates that this procedure, once extended to other regions, represents a powerful tool for future routine applications to homogeneously evaluate robust source parameters on a national scale. Furthermore, the coda calibration procedure can homogenize the Mw estimates for small and large events without the necessity of introducing any conversion scale between narrow-band measures such as local magnitude (ML) and Mw, which has been shown to introduce significant bias.
Qubit control noise spectroscopy with optimal suppression of dephasing
Here, we extend quantum noise spectroscopy (QNS) of amplitude control noise to settings where dephasing noise or detuning errors make significant contributions to qubit dynamics. Previous approaches to characterize amplitude noise are limited by their vulnerability to low-frequency dephasing noise and static detuning errors, which can overwhelm the target control noise signal and introduce bias into estimates of the amplitude noise spectrum. To overcome this problem, we leverage optimal control to identify a family of amplitude control waveforms that optimally suppress low-frequency dephasing noise and detuning errors, while maintaining the spectral concentration in the amplitude filter essential for spectral estimation. The waveforms found via numerical optimization have surprisingly simple analytic forms, consisting of oscillating sine waves obeying particular amplitude and frequency constraints. In numerically simulated QNS experiments, these waveforms demonstrate superior robustness, enabling accurate estimation of the amplitude noise spectrum in regimes where existing approaches are biased by low-frequency dephasing noise and detuning errors.