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Data obtained by insturments on the Ulysses spacecraft during its rapid sweep through >90 degrees of solar latitude, crossing the solar equator in early 1995, were combined with data obtained near Earth by the WIND spacecraft to study the spatial structure of the solar wind and to compare with different models of the interplanetary field derived from solar observations.
Recent tests using the Common Research Model (CRM) at the Langley National Transonic Facility (NTF) and the Ames 11-foot Transonic Wind Tunnel (11' TWT) produced large sets of data that have been used to examine the effects of active damping on transonic tunnel aerodynamic data quality. In particular, large statistically significant sets of repeat data demonstrate that the active damping system had no apparent effect on drag, lift and pitching moment repeatability during warm testing conditions, while simultaneously enabling aerodynamic data to be obtained post stall. A small set of cryogenic (high Reynolds number) repeat data was obtained at the NTF and again showed a negligible effect on data repeatability. However, due to a degradation of control power in the active damping system cryogenically, the ability to obtain test data post-stall was not achieved during cryogenic testing. Additionally, comparisons of data repeatability between NTF and 11-ft TWT CRM data led to further (warm) testing at the NTF which demonstrated that for a modest increase in data sampling time, a 2-3 factor improvement in drag, and pitching moment repeatability was readily achieved not related with the active damping system.
The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. In this presentation, we explore the output characteristics of the dataset and various validation analyses. We also present and discuss plans for the integration of this data into power system planning models using a decision-making under deep uncertainty (DMDU) methodology.
The Antarctic Continental Shelf seas (ACSS) are a critical, rapidly changing element of the Earth system. Analyses of global-scale general circulation model (GCM) simulations, including those available through the Coupled Model Intercomparison Project, Phase 6 (CMIP6), can help reveal the origins of observed changes and predict the future evolution of the ACSS. However, an evaluation of ACSS hydrography in GCMs is vital: previous CMIP ensembles exhibit substantial mean-state biases (reflecting, for example, misplaced water masses) with a wide inter-model spread. Because the ACSS are also a sparely sampled region, grid-point-based model assessments are of limited value. Our goal is to demonstrate the utility of clustering tools for identifying hydrographic regimes that are common to different source fields (model or data), while allowing for biases in other metrics (e.g., water mass core properties) and shifts in region boundaries. We apply K-means clustering to hydrographic metrics based on the stratification from one GCM (Community Earth System Model version 2; CESM2) and one observation-based product (World Ocean Atlas 2018; WOA), focusing on the Amundsen, Bellingshausen and Ross seas. When applied to WOA temperature and salinity profiles, clustering identifies “primary” and “mixed” regimes that have physically interpretable bases. For example, meltwater-freshened coastal currents in the Amundsen Sea and a region of high-salinity shelf water formation in the southwestern Ross Sea emerge naturally from the algorithm. Both regions also exhibit clearly differentiated inner- and outer-shelf regimes. The same analysis applied to CESM2 demonstrates that, although mean-state model biases in water mass T–S characteristics can be substantial, using a clustering approach highlights that the relative differences between regimes and the locations where each regime dominates are well represented in the model. CESM2 is generally fresher and warmer than WOA and has a limited fresh-water-enriched coastal regimes. Given the sparsity of observations of the ACSS, this technique is a promising tool for the evaluation of a larger model ensemble (e.g., CMIP6) on a circum-Antarctic basis.
This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript "Hydroclimatic Memory and Watershed Template Shape Stream Intermittency: Multi-scale Attribution Using Process-based Simulation and Explainable ML" by Niroula et al. (2026), submitted to Water Resources Research (WRR). The study investigates the dominant controls on stream intermittency across local, reach, and watershed scales using a coupled process-based simulation and explainable machine-learning framework. Long-term daily simulations from the Advanced Terrestrial Simulator (ATS) were used to generate wetness states and ponded-depth responses over river-corridor cells. These ATS outputs were then aggregated across scales and used to train XGBoost (eXtreme Gradient Boosting) models. SHAP (SHapley Additive exPlanations) was applied to quantify the relative importance of hydroclimatic forcings, watershed template attributes, and antecedent-memory effects in shaping intermittency behavior. The analysis is carried out for three contrasting Pacific Northwest watersheds: Oak Creek (OCW), American River Watershed (ARW), and H.J. Andrews (HJA). Across these testbeds, the package contains ATS-ready watershed inputs, ATS run configuration and selected output files, model-evaluation data products, intermittency-analysis datasets, machine-learning target-feature tables, SHAP outputs, and notebooks used to organize, analyze, and visualize results. At a high level, the package documents a workflow in which ATS provides the physically based simulation backbone and explainable machine learning is used as a post-processing attribution tool. The contents are intended to support interpretation of the manuscript figures and results, provide context for how intermittency metrics were generated at multiple scales, and preserve the key artifacts needed to understand and reuse the analysis workflow. The package contains a high-level directory summary file (`summary.txt`) and four main content folders (1) `evaluation_plots` contains evaluation figures and supporting evaluation datasets; (2) `intermittency_plots` contains intermittency-focused analysis notebook and prepared datasets; (3) `ml-training-and-shap_values_plots` contains ML training inputs, SHAP outputs, and figure-generation notebooks; and (4) `watershed_mesh_and_ats_input` contains ATS model setup materials, forcing inputs, geometry, and selected run files. More specifically, the `evaluation_plots` folder contains the notebook used for ATS evaluation plotting and site-specific evaluation datasets. These include evapotranspiration and water-balance products for three watersheds, as well as an Oak Creek field-measurement discharge file. The `intermittency_plots` folder contains the notebook used for intermittency analysis and the prepared datasets used to analyze intermittent and non-intermittent wetness behavior across the study watersheds. The `ml-training-and-shap_values_plots` folder contains notebooks and outputs for the machine-learning and explainability workflow. This includes the main XGBoost and SHAP notebook(s), a beeswarm plotting notebook, target-feature tables for machine-learning training, SHAP summary tables, and per-sample SHAP value archives. The `watershed_mesh_and_ats_input` folder contains ATS-related watershed inputs and supporting materials. This includes mesh and shape products, ATS-readable LAI and meteorological forcing inputs, selected ATS spinup and transient-run files, and a watershed workflow example notebook. Subdirectories are organized by watershed where applicable.All files are .cpg (codepage files), .csv (comma-separated values), .dbf (database files), .exo (Exodus mesh format), .h5 (HDF5 format), .ipynb (Jupyter notebooks), .pkl (Python pickle), .prj (projection files), .sh (shell scripts), .shp (shapefile geometry), .shx (shapefile index), .txt (text files), or .xml (markup data).
Contemporary astronomy is characterized by increasingly complex instruments and observational techniques, higher data collection rates, and large data archives, placing severe stress on software analysis systems. The object-oriented paradigm represents a significant new approach to software design and implementation that holds great promise for dealing with this increased complexity. The basic concepts of this approach will be characterized in contrast to more traditional procedure-oriented approaches. The fundamental features of objected-oriented programming will be discussed from a C++ programming language perspective, using examples familiar to astronomers. This discussion will focus on objects, classes and their relevance to the data type system; the principle of information hiding; and the use of inheritance to implement generalization/specialization relationships. Drawing on the object-oriented approach, features of a new database model to support astronomical data analysis will be presented.
The field of materials design is currently experiencing a notable evolution, driven by the convergence of sophisticated computational methodologies based on first principles and data-driven modeling approaches. I will review our recent endeavors employing AI/ML to expedite first-principles simulations and mitigate traditional methods' temporal and spatial limitations. Central to our efforts is developing and utilizing ML interatomic potentials (MLPs) across a diverse spectrum of materials. We show that MLPs serve as invaluable tools for navigating the complexities of the simulations, such as understanding the behavior of MgO at extreme environments of ~1 terapascal and temperatures >10,000 Kelvin. Moreover, we show that MLPs can provide precise details of the intricate dynamics governing the oxidation processes of binary alloy systems due to the competition between surface segregation and reconstruction tendencies. In summation, advancements in MLPs open the door to fresh possibilities in material modeling and, ultimately, discovery.
This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.
This dataset provides the output of six Wave Energy Converter Simulator (WEC-Sim) simulations and accompanying documentation for the modeling of Triton Systems' oscillating water column (OWC) system at tank scale (validated using available data for tuning the model, Tests 1-2) and deployment scale (for which no validation data is available, Tests 4-6). Included are the output data in a MATLAB file structure, a comprehensive report on the modeling and design of the Triton OWC system, and a link to the WEC-Sim GitHub page. This work was supported by funding from TEAMER RFTS 5 (Request for Technical Support).
This paper reports an analysis and prediction development of rotor broadband noise. The two primary components of this noise are Blade-Wake Interaction (BWI) noise, due to the blades' interaction with the turbulent wakes of the preceding blades, and "Self" noise, due to the development and shedding of turbulence within the blades' boundary layers. Emphasized in this report is the new code development for Self noise. The analysis and validation employs data from the HART program, a model BO-105 rotor wind tunnel test conducted in the German-Dutch Wind Tunnel (DNW). The BWI noise predictions are based on measured pressure response coherence functions using cross-spectral methods. The Self noise predictions are based on previously reported semiempirical modeling of Self noise obtained from isolated airfoil sections and the use of CAMRAD.Modl to define rotor performance and local blade segment flow conditions. Both BWI and Self noise from individual blade segments are Doppler shifted and summed at the observer positions. Prediction comparisons with measurements show good agreement for a range of rotor operating conditions from climb to steep descent. The broadband noise predictions, along with those of harmonic and impulsive Blade-Vortex Interaction (BVI) noise predictions, demonstrate a significant advance in predictive capability for main rotor noise.
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A series of geomagnetic data assimilation experiments have been carried out to demonstrate the impact of assimilating archeomagnetic data via the CALS3k.4 geomagnetic field model from the period between 10 and 1590 CE. The assimilation continues with the gufm1 model from 1590 to 1990 and CM4 model from 1990 to 2000 as observations, and comparisons between these models and the geomagnetic forecasts are used to determine an optimal maximum degree for the archeomagnetic observations, and to independently estimate errors for these observations. These are compared with an assimilation experiment that uses the uncertainties provided with CALS3k.4. Optimal 20 year forecasts in 1990 are found when the Gauss coefficients up to degree 3 are assimilated. In addition we demonstrate how a forecast and observation bias correction scheme could be used to reduce bias in modern era forecasts. Initial experiments show that this approach can reduce modern era forecast biases by as much as 50 percent.
This data package contains the model inputs and outputs used in "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" (Shuai et al., 2022 HESS). The data.zip file contains the data used to drive the model simulations. The model.zip file contains the XML input file for ATS. The notebook.zip file contains the Jupyter notebooks for pre- and post- processing model results. The figures.zip file contains the raw figures associated with the manuscript.Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) -- Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30% of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.
Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.
The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. As with the first Sup3rCC version, the data still include temperature, wind speed and direction at multiple heights, pressure, three components of downwelling solar radiation, and relative humidity—all at 4-kilometer (km) hourly resolution over the contiguous United States. This is a 25x spatial enhancement and 24x temporal enhancement of the source 100-km daily-average ESM data. This extension of the Sup3rCC dataset includes data from six ESMs from two shared socioeconomic pathways (SSPs) totaling 400 years of data with multiple future projections of changing meteorological conditions. The scenario selection was based on a structured evaluation of historical ESM skill and comprehensive representation of possible trajectories of future climate change in temperature, humidity, precipitation, solar irradiance, and near-surface wind speeds. The inclusion of multiple future projections is intended to enable users to assess key drivers of un 36 certainty and variability. All data are double-bias corrected, resulting in a product that can be used out-of-the-box for energy system analysis with minimal historical bias. The potential applications of Sup3rCC data extend to various topics in renewable energy resource assessment, energy systems modeling, and grid resilience studies. High-resolution future meteorological projections are critical for evaluating the effects of changing meteorological conditions on renewable energy generation, energy demand, and for optimizing energy storage and grid infrastructure. The 4-km hourly resolution of the downscaled data enables understanding of spatial and temporal variability at the scales necessary for energy system operational planning. In addition, the dataset can support risk assessments by providing detailed information on possible future extreme weather events and long-term meteorological variability at scales relevant to energy infrastructure. By offering an enhanced representation of possible future meteorological conditions, the second-generation Sup3rCC dataset enables more precise modeling of energy resilience and adaptation strategies in response to changing meteorological conditions.
In practice, we often need to infer the value of a target variable from functional observation data. A challenge in this task is that the relationship between the functional data and the target variable is very complex: the target variable not only influences the shape but also the location of the functional data. In addition, due to the uncertainties in the environment, the relationship is probabilistic, that is, for a given fixed target variable value, we still see variations in the shape and location of the functional data. To address this challenge, we present a landmark-embedded Gaussian process model that describes the relationship between the functional data and the target variable. A unique feature of the model is that landmark information is embedded in the Gaussian process model so that both the shape and location information of the functional data are considered simultaneously in a unified manner. Gibbs-Metropolis-Hasting algorithm is used for model parameters estimation and target variable inference. The performance of the proposed framework is evaluated by extensive numerical studies and a case study of nano-sensor calibration.
Accurate modeling of photosynthesis is crucial for predicting crop productivity and quantifying the carbon cycle in agroecosystems. Leaf traits are essential inputs for modeling canopy photosynthesis. Yet, many existing models still use fixed plant functional type (PTF)-based values to parameterize leaf traits under a big-leaf or two-big-leaf assumption, neglecting their vertical profiles and seasonal changes. This simplification may introduce significant uncertainties in estimating gross primary productivity (GPP). In this study, we simulated soybean GPP and tested the effects of vertical and seasonal variation in three key leaf photosynthetic traits: the maximum carboxylation rate at 25 °C (Vcmax25), leaf chlorophyll content (LCC), and leaf mass per area (LMA) in the 1D-SCOPE and 3D-Helios models. Weekly field measurements were conducted during the growing season of 2024 to support the simulation. We designed ten leaf trait parameterization schemes by incorporating different combinations of vertical profiles and seasonal changes, while assuming homogeneous canopy architecture in both models. Our results revealed that Vcmax25 vertical and seasonal variation had the strongest influence on simulated GPP in both 1D and 3D models, while LCC and LMA effects were minimal. Particularly, the scheme with an empirically parameterized Vcmax25 profile achieved comparable performance to the scheme with the measured Vcmax25 profile. Both 1D-SCOPE and 3D-Helios accurately modeled GPP (SCOPE: R2 = 0.87, Bias = 0.55 µmol m⁻² s⁻¹; Helios: R2 = 0.9, Bias = 0.22 µmol m⁻² s⁻¹) under the most complex scheme, and their responses to vertical and seasonal variation in leaf traits were consistent, demonstrating the robustness of our findings. Based on our findings, we propose a scalable framework for parameterizing leaf traits to improve GPP simulations. This study contributes to improving the representation of leaf trait dynamics in canopy-level photosynthesis models, potentially enhancing our ability to predict crop productivity and understand agroecosystem carbon dynamics.