Multi-year application of WRF-CAM5 over East Asia-Part I: Comprehensive evaluation and formation regimes of O3 and PM2.5
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He, Jian (ORCID:0000000216276859)↗
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Myco-CORPSE simulations assessing mycorrhizal carbon allocation across U.S. forests and global change scenarios
Plants allocate a substantial portion of their fixed carbon belowground to mycorrhizal fungi in exchange for nutrients and other benefits. However, most current ecosystem models omit mycorrhizal processes, limiting our ability to predict plant–soil carbon dynamics under environmental change. To address this gap, we used a mycorrhiza-explicit soil biogeochemical model, Myco-CORPSE (Mycorrhizal Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment), to simulate tree carbon allocation to arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi in temperate forests.The dataset includes outputs from two sets of model simulations:1. Perturbation experiments: Simulations across gradients of ECM dominance (0–100%), nitrogen deposition, soil temperature, and net primary productivity (NPP) to test how these factors affect mycorrhizal C allocation and nutrient cycling.2. FIA-based simulations: Model applications to over 1,800 U.S. forest sites using site-specific data from the U.S. Forest Inventory and Analysis (FIA) program, including vegetation composition, mycorrhizal type, climate, litter traits, soil properties, and N deposition.Model outputs include simulated mycorrhizal carbon allocation and related biogeochemical variables, such as soil and microbial carbon and nitrogen stocks. Data are provided in CSV format and organized by experiment type (in separate ZIP files). Python scripts for running simulations, plotting, and spatial mapping are also included and organized similarly. No proprietary software is required. These outputs support a peer-reviewed study and were used to generate figures and tables in the associated publication.
54 ENVIRONMENTAL SCIENCES↗
Detecting bimodality in astronomical datasets
We discuss statistical techniques for detecting and quantifying bimodality in astronomical datasets. We concentrate on the KMM algorithm, which estimates the statistical significance of bimodality in such datasets and objectively partitions data into subpopulations. By simulating bimodal distributions with a range of properties we investigate the sensitivity of KMM to datasets with varying characteristics. Our results facilitate the planning of optimal observing strategies for systems where bimodality is suspected. Mixture-modeling algorithms similar to the KMM algorithm have been used in previous studies to partition the stellar population of the Milky Way into subsystems. We illustrate the broad applicability of KMM by analyzing published data on globular cluster metallicity distributions, velocity distributions of galaxies in clusters, and burst durations of gamma-ray sources. FORTRAN code for the KMM algorithm and directions for its use are available from the authors upon request.
Ashman, Keith A.↗
Quantifying the agreement between observed and simulated extratropical modes of interannual variability
Using historical simulations of the Coupled Model Intercomparison Project-5 (CMIP5) and multiple observationally-based datasets, we employ skill metrics to analyze the fidelity of the simulated Northern Annular Mode, the North Atlantic Oscillation, the Pacific North America pattern, the Southern Annular Mode, the Pacific Decadal Oscillation, the North Pacific Oscillation, and the North Pacific Gyre Oscillation. We assess the benefits of a unified approach to evaluate these modes of variability, which we call the common basis function (CBF) approach, based on projecting model anomalies onto observed empirical orthogonal functions (EOFs). The CBF approach circumvents issues with conventional EOF analysis, eliminating, for example, corrections of arbitrarily assigned, but inconsistent, signs of the EOF’s/PC’s being compared. It also avoids the problem that sometimes the first observed EOF is more similar to a higher order model EOF, particularly if the simulated EOFs are not well separated. Compared to conventional EOF analysis of models, the CBF approach indicates that models compare significantly better with observations in terms of pattern correlation and root-mean-squared-error (RMSE) than heretofore suggested. In many cases, models are doing a credible job at capturing the observationally-based estimates of patterns; however, errors in simulated amplitudes can be large and more egregious than pattern errors. In the context of the broad distribution of errors in the CMIP5 ensemble, sensitivity tests demonstrate that our results are relatively insensitive to methodological considerations (CBF vs. conventional approach), observational uncertainties in pattern (as determined by using multiple datasets), and internal variability (when multiple realizations from the same model are compared). The skill metrics proposed in this study can provide a useful summary of the ability of models to reproduce the observed EOF patterns and amplitudes. Additionally, the skill metrics can be used as a tool to objectively highlight where potential model improvements might be made. Finally, we advocate more systematic and objective testing of simulated extratropical variability, especially during the non-dominant seasons of each mode, when many models are performing relatively poorly.
58 GEOSCIENCES↗
An approach to the detection of long-term trends in upper stratospheric ozone from space
A central problem in the detection of long-term trends in the upper stratospheric ozone from orbiting remote sensors involves the separation of instrument drifts from true geophysical changes. Periodic flights of a Solar Backscatter Ultraviolet radiometer (SSBUV) on the Space Shuttle will allow the detection of drifts in optically identical sensors (SBUV/2) carried on operational satellites. A detailed simulation of the SSBUV and SBUV/2 datasets defines the accuracy that can be attained by the in-orbit calibration procedure. The repeatability of the SSBUV calibration from one flight to the next is the most critical variable in the analysis. A repeatability near + or - 1.0 percent is essential for detection and correction of drifts in the SBUV/2 radiance measurements. The simulations show that true geophysical trends in backscattered radiance can be inferred to an accuracy of approximately + or - 1.0 percent per decade when SSBUV flies approximately once per year and provides a precise calibration correction to the SBUV/2 dataset over a full decade.
Frederick, John E.↗
Validation of SAS4A/SASSYS-1 for Steady-State Single-Phase Natural Circulation
The development of numerical software for engineering applications requires the validation of the code against experimental benchmark datasets. To support the validation of SAS4A/SASSYS-1 in simulating the single-phase natural circulation within nuclear reactor systems, high-precision steady-state experiments are performed to capture single-phase natural circulation phenomena on an existing scaled facility with comprehensive instrumentation. Forced convection tests are designed precisely capturing the facility’s critical thermal-hydraulic parameters required for one-dimensional modeling, and a comprehensive single-phase natural circulation dataset is obtained with well-documented experimental uncertainty and facility description. Analysis and discussion based on the finalized dataset confirm the dataset’s ability in capturing dominant phenomena and address important physical interpretation of parameters. The dataset reported in this project provides a valuable benchmark resource for the validation of system analysis codes under single-phase natural circulation inside nuclear reactors. Validation of SAS4A/SASSYS-1 is then performed against the obtained benchmark dataset to confirm the capability of its physics model in simulating the steady-state single-phase natural circulation. The experimental facility is modeled in the candidate code. Solution verification is performed using Richardson-extrapolation-based estimators to quantify and restrict numerical errors from discretization. Numerical uncertainty originating from finite maximum pseudo-transient time is also quantified and restricted. Input uncertainty provided by the benchmark dataset is forward propagated through the candidate code, directly quantifying the simulation output in a Monte Carlo approach. The composition of the output uncertainty is further determined by the estimators of Sobol’ indices through a variance-based sensitivity analysis. With uncertainty quantified for each individual condition, detailed comparison between the simulation results and experimental data is performed covering the whole dataset, which shows consistent agreement for all important quantities. The validation activity reported in this project demonstrates that SAS4A/SASSYS-1 can predict the primary parameters with satisfactorily accuracy under steady-state single-phase natural circulation.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Model data for infrastructure-aware simulation of compound flooding at Alligator Bayou Watershed, southeast Texas
This dataset supports infrastructure-aware hydrologic modeling and flood scenario analysis for the Alligator Bayou Watershed, a highly managed urban watershed in Southeast Texas. It includes Jupyter notebooks for figure reproduction, model configuration files, simulation outputs, and derived products used to quantify the influence of engineered stormwater infrastructure on flood behavior across multiple spatial scales. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations on a channel-aligned mesh with explicit representations of pump stations, gate structures, detention basins, and impervious surfaces. Outputs include time series of gate and pump flows, stage observations, and water balance components, as well as spatially explicit fields of peak ponded depth and flood duration across multiple infrastructure scenarios spanning a single-location detention basin expansion, distributed drainage limitations, and compound coastal flooding. These data facilitate full reproducibility of the manuscript figures and support further research on urban flood dynamics and the role of stormwater infrastructure in shaping watershed-scale flood response.
EARTH SCIENCE > OCEANS > COASTAL PROCESSES↗
Detecting and Testing for Structural Error in Computer Models with Application to the Community Atmospheric Model
Bayesian calibration/inversion methods for uncertain input parameters to simulation codes have generated an increasing amount of interest in the climate community in the recent years. This approach uses multiple disparate observational datasets related to the output of the simulator of interest to yield a probabilistic constraint on the uncertain inputs (i.e. probability distribution). A major challenge in applying Bayesian calibration methods to simulation codes is related to the possible presence of a large structural error in the output fields of interest; that is, the presence of discrepancy between the observed and the simulated output that is neither explained by the uncertainty in the inputs nor the observations. We present here methods to test for and visualize structural error in an ensemble of input-perturbed simulations. The methods are based on bootstrap (resmpling) test statistics and well-known metrics to quantify the difference between two probability distributions. The proposed methods are demonstrated using selected precipitation output fields of the Community Atmospheric Model (CAM), which are compared to observations provided by the Global Precipitation Climatology Project (GPCP). The methods are found to be useful in identifying regions and seasons with a large structural error. The proposed structural error detection methodology can be used to screen multiple observational datasets of interest for structural error, making it possible to retain only the datasets with a small structural error for use in Bayesian calibration. In addition, information about the amount of structural error can be valuable to model developers.
58 GEOSCIENCES↗
Assessment of NASA's Physiographic and Meteorological Datasets as Input to HSPF and SWAT Hydrological Models
This paper documents the use of simulated Moderate Resolution Imaging Spectroradiometer land use/land cover (MODIS-LULC), NASA-LIS generated precipitation and evapo-transpiration (ET), and Shuttle Radar Topography Mission (SRTM) datasets (in conjunction with standard land use, topographical and meteorological datasets) as input to hydrological models routinely used by the watershed hydrology modeling community. The study is focused in coastal watersheds in the Mississippi Gulf Coast although one of the test cases focuses in an inland watershed located in northeastern State of Mississippi, USA. The decision support tools (DSTs) into which the NASA datasets were assimilated were the Soil Water & Assessment Tool (SWAT) and the Hydrological Simulation Program FORTRAN (HSPF). These DSTs are endorsed by several US government agencies (EPA, FEMA, USGS) for water resources management strategies. These models use physiographic and meteorological data extensively. Precipitation gages and USGS gage stations in the region were used to calibrate several HSPF and SWAT model applications. Land use and topographical datasets were swapped to assess model output sensitivities. NASA-LIS meteorological data were introduced in the calibrated model applications for simulation of watershed hydrology for a time period in which no weather data were available (1997-2006). The performance of the NASA datasets in the context of hydrological modeling was assessed through comparison of measured and model-simulated hydrographs. Overall, NASA datasets were as useful as standard land use, topographical , and meteorological datasets. Moreover, NASA datasets were used for performing analyses that the standard datasets could not made possible, e.g., introduction of land use dynamics into hydrological simulations
Alacron, Vladimir J.↗
T2-weighted four dimensional magnetic resonance imaging with result-driven phase sorting
Purpose: T2-weighted MRI provides excellent tumor-to-tissue contrast for target volume delineation in radiation therapy treatment planning. This study aims at developing a novel T2-weighted retrospective four dimensional magnetic resonance imaging (4D-MRI) phase sorting technique for imaging organ/tumor respiratory motion. Methods: A 2D fast T2-weighted half-Fourier acquisition single-shot turbo spin-echo MR sequence was used for image acquisition of 4D-MRI, with a frame rate of 2–3 frames/s. Respiratory motion was measured using an external breathing monitoring device. A phase sorting method was developed to sort the images by their corresponding respiratory phases. Besides, a result-driven strategy was applied to effectively utilize redundant images in the case when multiple images were allocated to a bin. This strategy, selecting the image with minimal amplitude error, will generate the most representative 4D-MRI. Since we are using a different image acquisition mode for 4D imaging (the sequential image acquisition scheme) with the conventionally used cine or helical image acquisition scheme, the 4D dataset sufficient condition was not obviously and directly predictable. An important challenge of the proposed technique was to determine the number of repeated scans (N{sub R}) required to obtain sufficient phase information at each slice position. To tackle this challenge, the authors first conducted computer simulations using real-time position management respiratory signals of the 29 cancer patients under an IRB-approved retrospective study to derive the relationships between N{sub R} and the following factors: number of slices (N{sub S}), number of 4D-MRI respiratory bins (N{sub B}), and starting phase at image acquisition (P{sub 0}). To validate the authors’ technique, 4D-MRI acquisition and reconstruction were simulated on a 4D digital extended cardiac-torso (XCAT) human phantom using simulation derived parameters. Twelve healthy volunteers were involved in an IRB-approved study to investigate the feasibility of this technique. Results: 4D data acquisition completeness (C{sub p}) increases as NR increases in an inverse-exponential fashion (C{sub p} = 100 − 99 × exp(−0.18 × N{sub R}), when N{sub B} = 6, fitted using 29 patients’ data). The N{sub R} required for 4D-MRI reconstruction (defined as achieving 95% completeness, C{sub p} = 95%, N{sub R} = N{sub R,95}) is proportional to N{sub B} (N{sub R,95} ∼ 2.86 × N{sub B}, r = 1.0), but independent of N{sub S} and P{sub 0}. Simulated XCAT 4D-MRI showed a clear pattern of respiratory motion. Tumor motion trajectories measured on 4D-MRI were comparable to the average input signal, with a mean relative amplitude error of 2.7% ± 2.9%. Reconstructed 4D-MRI for healthy volunteers illustrated clear respiratory motion on three orthogonal planes, with minimal image artifacts. The artifacts were presumably caused by breathing irregularity and incompleteness of data acquisition (95% acquired only). The mean relative amplitude error between critical structure trajectory and average breathing curve for 12 healthy volunteers is 2.5 ± 0.3 mm in superior–inferior direction. Conclusions: A novel T2-weighted retrospective phase sorting 4D-MRI technique has been developed and successfully applied on digital phantom and healthy volunteers.
60 APPLIED LIFE SCIENCES↗
A generalized machine learning workflow to visualize mechanical discontinuity
Accurate detection and mapping of mechanical discontinuity in materials has widespread industrial and research applications. Herein, we developed a generalized machine-learning framework for visualizing single mechanical discontinuity embedded in material of any composition, velocity, density, porosity, and size with limited data. The proposed visualization of discontinuity requires accurate estimations of the length, location, and orientation of the embedded discontinuity by processing multipoint wave-transmission measurements. k-Wave simulator is used to create a large dataset of elastic waveforms recorded during multi-point wave-transmission measurements through materials containing single mechanical discontinuity. k-Wave simulator considers the wave attenuation, dispersion, and mode conversion in wave motion. Discrete wavelet transform (DWT) and statistical feature extraction are essential for data preprocessing prior to the data-driven model development. DWT also minimizes the effect of noise. Using hyper-parameter tuning and cross validation, gradient boosting regression can visualize the mechanical discontinuity with an accuracy of 0.85, in terms of coefficient of determination. A double-layered neural network-based regression has better performance with an accuracy of 0.95. Use of convolutional neural network converts the predictive task from a waveform processing to an image processing problem. Convolutional neural network achieved a generalization performance of 0.91. The proposed generalized workflow requires robust simulation of wave propagation, signal processing, feature engineering, and model evaluation. Sensors closest to the source and those located opposite the source are the most significant for the desired visualization. Notably, the sensors closest to the source capture the non-linear associations, whereas the sensor on the border opposite to the source capture the linear associations between the measured waveforms and the properties of the mechanical discontinuity.
42 ENGINEERING↗
Evaluation of adjoint-based observation impacts as a function of forecast length using an Observing System Simulation Experiment
Adjoints of numerical weather prediction models may be employed for Forecast Sensitivity to Observation (FSO) in order to monitor the contribution of ingested observation data on short-term forecast skill. However, the calculation of short-term forecast error is difficult due to the lack of a truly independent dataset for verification. In an Observing System Simulation Experiment framework, the Nature Run is able to provide a true and complete verification dataset and allows accurate evaluation of short term forecast errors. In this work, an OSSE developed at the National Aeronautics and Space Administration Global Modeling and Assimilation Office is used to explore the impact of observational data on forecasts in the 6 to 48 hour range. An adjoint of the Global Earth Observing System model is employed to compare the observation impacts estimated using both self-analysis verification and the true Nature Run verification. Self-analysis verification is found to inflate the estimated forecast error growth during the early forecast period, resulting in overestimations of observation impacts, particularly in the 6-12 hour forecast range. By 48 hours, the self-analysis verification estimates of forecast error and observation impacts more closely match the true values. The fraction of beneficial observations is also overinflated at short forecast times when self-analysis verification is used. The progression of impacts of an individual observation or data type depends on the character of the growth of the initial condition error that each observation affects.
numerical weather prediction↗
Validation of Noah-simulated Soil Temperature in the North American Land Data Assimilation System Phase 2
Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North-American Land Data Assimilation (NLDAS) Phase 2 (NLDAS-2) has generated 31-years (1979-2009) of simulated hourly soil temperature data with a spatial resolution of 1/8o. This dataset has not been comprehensively evaluated to date. Thus, the ultimate purpose of the present work is to assess Noah-simulated soil temperature for different soil depths and timescales. We used long-term (1979-2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0-10 cm, 10-40 cm, 40-100 cm) for annual and monthly timescales. We used short-term (1997-1999) observed soil temperature from 72 Oklahoma Mesonet stations to validate simulated soil temperatures for three soil layers and for daily and hourly timescales. The results showed that the Noah land surface model (Noah LSM) generally matches observed soil temperature well for different soil layers and timescales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season due to small downward longwave radiation and issues related to model parameters.
land↗
Water balance in the Amazon basin from a land surface model ensemble
Despite recent advances in modeling and remote sensing of land surfaces, estimates of the global water budget are still fairly uncertain. The objective of this study is to evaluate the water budget of the Amazon basin based on several state-of-the-art land surface model (LSM) outputs. Water budget variables [total water storage (TWS), evapotranspiration (ET), surface runoff (R) and baseflow (B)] are evaluated at the basin scale using both remote sensing and in situ data. Fourteen LSMs were run using meteorological forcings at a 3-hourly time step and 1-degree spatial resolution. Three experiments are performed using precipitation which has been rescaled to match monthly global GPCP and GPCC datasets and the daily HYBAM dataset for the Amazon basin. R and B are used to force the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme and simulated discharges are compared against observations at 165 gauges. Simulated ET and TWS are compared against FLUXNET and MOD16A2 evapotranspiration, and GRACE TWS estimates in different catchments. At the basin scale, simulated ET ranges from 2.39mm.d-1 to 3.26mm.d-1 and a low spatial correlation between ET and P indicates that evapotranspiration does not depend on water availability over most of the basin. Results also show that other simulated water budget variables vary significantly as a function of both the LSM and precipitation used, but simulated TWS generally agree at the basin scale. The best water budget simulations resulted from experiments using the HYBAM dataset, mostly explained by a denser rainfall gauge network the daily rescaling.
water balance, Amazon, land surface models↗
Effect of Grain Boundary Misorientation on Spall Strength in Ta via Shock-Free Simulations with Relatively Few Atoms
A suite of 37 molecular dynamics simulations is conducted at two system sizes to systematically characterize the role of grain boundary (GB) misorientation on spall strength in pure BCC tantalum (Ta). The systems studied consist of bicrystals with a single [110] symmetric tilt grain boundary. Two loading conditions are compared: (i) homogeneous extension under uniaxial strain simulated in this study and (ii) piston/flyer impact of sample, which induces heterogeneous deformation via shockwave propagation along the length of the sample. The piston/flyer impact is taken from the literature and run on the same set of GB misorientation angles using LAMMPS. The major finding here is that both methods result in similar spall strength predictions, but the homogeneous extension method generally requires two to three orders of magnitude fewer atoms and similar reductions in computational costs. Spall strength results systematically overpredict using this method, by about 10% for the dataset three orders of magnitude smaller than piston/flyer simulations, and 5% for the dataset two orders of magnitude smaller. Lastly, the effect of system size and pre-compression magnitude on spall strength is systematically characterized.
36 MATERIALS SCIENCE↗
Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters
Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-hour time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.
16 TIDAL AND WAVE POWER↗
Evaluating the Impact of Price-Responsive Load on Power Systems Using Integrated T&D Simulation
This paper explores the differences between simulating price-responsive load (PRL) interactions with power systems using integrated transmission and distribution (T&D) models and transmission-only (T-only) models. This analysis uses the Integrated Grid Modeling System (IGMS) software to capture 'ISO-to-appliance' simulations using a synthetic T&D model built on the PJM 5-Bus transmission network with multiple full-scale taxonomy feeders that include physics-based models of thousands of customers and PRLs. The results show important differences in the impacts of PRLs between integrated T&D and T-only models. Experiments with the synthetic integrated T&D dataset demonstrated that integrated T&D simulation revealed notably larger differences between the PRL and no-PRL cases for load, and prices compared to T-only simulation. Similarly, differences are observed between the price response of individual buildings and distribution feeders and the corresponding transmission bus in the integrated T&D simulations, which are difficult to capture in traditional T-only simulations.
24 POWER TRANSMISSION AND DISTRIBUTION↗
FY13 Summary Report on the Augmentation of the Spent Fuel Composition Dataset for Nuclear Forensics: SFCOMPO/NF
This report documents the FY13 efforts to enhance a dataset of spent nuclear fuel isotopic composition data for use in developing intrinsic signatures for nuclear forensics. A review and collection of data from the open literature was performed in FY10. In FY11, the Spent Fuel COMPOsition (SFCOMPO) excel-based dataset for nuclear forensics (NF), SFCOMPO/NF was established and measured data for graphite production reactors, Boiling Water Reactors (BWRs) and Pressurized Water Reactors (PWRs) were added to the dataset and expanded to include a consistent set of data simulated by calculations. A test was performed to determine whether the SFCOMPO/NF dataset will be useful for the analysis and identification of reactor types from isotopic ratios observed in interdicted samples.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗