SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Conference paper titled, Comparing microclimate weather conditions from the weather research and forecasting (WRF) model with typical meteorological year (TMY) data for building energy simulations accepted for Building Simulation 2025 Conference in Brisbane, Australia in Aug 2025.
This dataset provides measurements of temperature, humidity, rainfall, wind, and sunlight at Ambient Weather and OttHydro stations across Baltimore city. These surface weather stations were deployed by the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (UIFL) project, funded by the Department of Energy (DOE). This dataset currently contains measurements from 2023 to June 2026 and will be periodically updated to include more stations and recent observations when available. Data File Information This dataset contains surface weather measurements data in comma-separated value (CSV) format and documents that describe the weather stations, locations, and measured parameters and units. data/[TIMEAVG]/[YEAR]/BSEC-[STATIONID]_[SENSORTYPE]_[TIMEAVG]_[YEAR].csv Surface weather measurements data in CSV format, where STATIONID indicates the weather station, SENSORTYPE is the type of weather station ('AWS' = Ambient Weather Station and 'OTT' = 'OttHydro Station'), TIMEAVG is time period for each entry (= daily, hourly, or 5min), and YEAR indicate the year in which the measurements were made. Example data file name: BSEC-AAC_AWS_hourly_2023.csv. documents/Station_Locations.csv This CSV file provides location information and measurement start date for each surface weather station. documents/Weather_Station_Descriptions.pdf This document provides detailed description of the instruments along with their setup and accuracy of measurement. documents/File_Contents.pdf This document describes the contents on the data files, including time notation, weather parameters and units of measurement. documents/site-metadata/[STATOINID]-metadata.pdf These PDF files provide information on weather station sites, including land cover characteristics, station mounting, and photographs. Each PDF file corresponds to one station, as indicated by STATIONID.
This study supports the mission of the U.S. Department of Energy’s Weatherization Assistance Program (WAP), which aims to increase the energy efficiency of dwellings and reduce their total residential expenditures. Specifically, we examine how projected future climate conditions may affect residential building energy performance by integrating future weather data into the National Energy Audit Tool (NEAT). Since WAP evaluates the cost-effectiveness of retrofit measures over lifespans of up to 30 years, accounting for evolving climate conditions is increasingly important. To reflect future household energy demands, this study replaces historically based Typical Meteorological Year (TMY3) weather inputs with Future Typical Meteorological Year (fTMY) datasets derived from global climate model (GCM) projections. A simulation-based framework was established to enable NEAT analysis under future weather conditions. This workflow involves converting EPW-format weather files into JSON inputs compatible with NEAT and generating degree-hour metrics needed for load calculations. The fTMY dataset used in this study was developed by Oak Ridge National Laboratory through downscaling of six GCMs under different emission scenarios and covers the period from 2020 to 2100. In contrast, the TMY3 dataset is based on historical weather data from 1961 to 1990. Simulations were conducted for benchmark single-family prototype buildings across ASHRAE climate zones 1–7, which cover all regions of the U.S. except the subarctic Zone 8 in northern Alaska, evaluating both heating and cooling loads under TMY3 and fTMY conditions. Four foundation types were tested, while heating systems were standardized, as NEAT does not differentiate thermal energy load by HVAC system type in its load calculations. Results show that fTMY weather input consistently yield lower heating loads and higher cooling loads across most locations, aligning with expected climate warming trends. Notably, colder regions such as zones 6A, 6B, and 7 experience marked reductions in heating load, while warmer and transitional zones, such as 2A (Lufkin, TX) and 3C (San Francisco, CA), have substantial increases in cooling loads. Although this study does not directly assess the performance of retrofit measures under future climate conditions, it provides a critical foundation for doing so. By quantifying shifts in baseline (i.e., pre-retrofit case) energy loads between historical and future weather files, the study highlights the importance of integrating climate-responsive data into audit tools. These findings will inform future efforts to evaluate the long-term effectiveness and cost-effectiveness of weatherization measures under changing climate conditions.
This dataset release corresponds to the work conducted in ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling, where large-scale AI methods were applied to improve climate and weather resolution. The collection integrates four widely used, publicly available datasets: ERA5, PRISM, DAYMET, and IMERG. To prepare the data for ORBIT-2 model training and evaluation, we applied a preprocessing pipeline that generates paired low-resolution and high-resolution samples, enabling supervised downscaling experiments. The transformation from coarse to fine scales was performed using bilinear regridding, consistent with the procedures described in WeatherBench2, a community benchmark for weather and climate AI models. This dataset supports the development and evaluation of foundation models designed for weather and climate downscaling at exascale. Additional details on methodology and applications can be found in Wang et al., ORBIT-2 (arXiv:2505.04802, 2025).
Secondary organic aerosol (SOA) forms and evolves in the atmosphere through many pathways and processes, over diverse spatial and time scales. Hence, there is a need to represent these widely-varying kinetic processes in large-scale atmospheric models to allow for accurate predictions of the abundance, properties, and impacts of SOA. In this work, we integrated a kinetic, process-level model (simpleSOM-MOSAIC) into a weather-chemistry model (WRF-Chem) to simulate the oxidation chemistry and microphysics of atmospheric SOA. simpleSOM-MOSAIC simulates multigenerational gas-phase chemistry, autoxidation reactions, heterogeneous oxidation, oligomerization, and phase-state-influenced gas/particle partitioning of SOA. As a case study, the integrated WRF-Chem-simpleSOM-MOSAIC (WC-SSM) model was used to simulate the photochemical evolution downwind of a large city (Manaus, Brazil) in the Amazon and, in turn, study the anthropogenic and biogenic interactions in an otherwise pristine environment. Consistent with previous work, we found that OA was enhanced by up to a factor of four in the urban plume due to elevated hydroxyl radical (OH) concentrations, relative to the background, and that this OA was dominated by SOA from biogenic precursors (80%). Further, in addition to accurately simulating the OA enhancement in the urban plume, the model reproduced the magnitude of the OA oxygen-to-carbon (O:C) ratio and broadly tracked the evolution of the aerosol size distribution. Our work highlights the importance of including an integrated, kinetic representation of SOA processes in an atmospheric model
Chemical weathering over geological timescales acts as a source or sink of atmospheric carbon dioxide (CO 2 ), while influencing long-term redox cycling and atmospheric oxygen (O 2 ) at Earth's surface. There is a growing recognition that the oxidative weathering of rock organic carbon (OC petro ) can release more CO 2 than is locally drawn down by silicate weathering, and may vary due to changes in erosion and climate. The element rhenium (Re) has emerged as a proxy to track the oxidative weathering of OC petro , yet uncertainties in its application remain namely that we lack a systematic assessment of the comparative mobility of Re and OC petro during sedimentary rock weathering. Here we measure Re and OC petro loss across gradients in rock weathering at 9 global sites, spanning a range of initial OC petro values from ~0.2 % to >10 %. We use titanium to account for volume changes during weathering and assess Re and OC petro loss alongside major elements that reflect silicate (Na, Mg), carbonate (Ca, Mg) and sulfide (S) weathering. Across the dataset, Re loss is correlated with OC petro loss but not with loss of any other major element. Further, across the weathering profiles, the average molar ratio of OC petro to Re loss was 0.84 ± 0.15, with 8 out of 9 sites having a ratio >0.74. At one site (Marcellus Shale), the average ratio was lower at 0.58 ± 0.11. The excess loss of Re matches expectations that, typically, between ~0 and 20 % of the Re liberated by sedimentary rock weathering derives from silicate or sulfide phases, while some OC petro may be physically or chemically protected from weathering. Overall, our measurements provide validation for the Re proxy of OC petro oxidation and allow future work to further improve our knowledge of regional and global-scale rates of this important source of CO 2 in the geochemical carbon cycle.
Weathering is a fundamental driver of landslide evolution over geological timescales. Despite its ubiquity and importance, quantifying how weathering drives the progressive destabilization of rock slopes remains challenging. In this work, we develop a unified computational framework based on the particle finite element method to investigate the evolution of weathering‐induced landslides, from long‐term weathering to short‐term slope failure and runout dynamics. The framework integrates key processes, including weathering front propagation, time‐dependent strength degradation, rupture surface development, and post‐failure runout dynamics. Through numerical simulation experiments, we elucidate how interactions among weathering characteristics (type, intensity, and rate law), bedrock strength, fracture distribution, and slope geometry govern the failure modes and kinematics of weathering‐induced landslides. Simulations show that matrix‐dominated weathering leads to shallow translational failures, whereas fracture‐dominated weathering produces deep‐seated rotational and compound landslides. Pre‐existing fractures and slope morphology also strongly influence the movement of destabilized landmasses, affecting the failure pattern (e.g., kinematic mode and rupture surface geometry) and post‐failure behavior (e.g., runout velocity). We further demonstrate that the failure time and volume of weathered slopes are governed by the competition between gravitational driving forces and cohesive resisting forces during progressive destabilization. These findings provide new insights into the fundamental mechanisms that drive the emergence of diverse failure patterns of weathering‐induced landslides with important implications for landslide hazard assessment.
Abstract Enhanced weathering has emerged as a promising natural climate solution that has the potential to remove billions of tons of carbon from the atmosphere if widely adopted in agricultural settings. Despite this potential, few field trials have been published that verify the carbon dioxide removal (CDR) potential of enhanced weathering in croplands and, until now, none had been published in grazing lands. Anthony et al. (2025, https://doi.org/10.1029/2024AV001480 ) conducted the first trial of enhanced weathering in a California rangeland and showed weathering of ground silicate rocks despite drought conditions in an already dry climate. Co‐application of inorganic (silicate rocks) with organic (biochar and compost) amendments revealed not just additive, but synergistic effects whereby organic amendments increased rates of weathering. This is important because field CDR rates were <10% of the theoretical maximum (i.e., the rate if basalt was completely weathered); thus, methods to improve weathering rates will be necessary for this practice to scale in a meaningful way. Multi‐carbon pool measurements revealed not only how co‐application of soil amendments heightened net carbon benefits, but also how soil amendments complemented each other to produce net benefits for soil carbon, biomass growth, and greenhouse gas emission reductions. Anthony et al. (2025, https://doi.org/10.1029/2024AV001480 ) produce new insights toward our understanding of enhanced weathering as well as introduce paths for future research concerning combined amendment applications, synergistic mechanisms for carbon storage, and deployment in various agricultural contexts. While questions remain about the fate of weathering products in arid regions, Anthony et al. (2025, https://doi.org/10.1029/2024AV001480 ) present novel findings on the potential for significant weathering to occur even under suboptimal conditions.
Abstract In recent years, machine‐learning (ML) models trained on reanalysis data have rivaled physics‐based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO‐HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics‐based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden‐Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large‐scale circulation and the MJO at week 3 and beyond. Even though overall the mid‐latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO‐HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale. Plain Language Summary Predicting weather patterns and precipitation a few weeks in advance (subseasonal time scale) is of great interest for stakeholders such as water managers in the Southwest United States (US), where arid conditions prevail. Subseasonal forecasts from traditional weather forecast models exhibit low skill in the region, limiting their applicability. Here we examine whether the recent breakthrough in weather forecasting made with machine learning/artificial intelligence models can translate to improved subseasonal forecasts. Recently‐developed machine learning models exhibit comparable skill to a state‐of‐the‐art physics‐based model for predicting weather patterns in the North Pacific/North America region, and associated moisture transport. The same applies to their skill in predicting the tropical pattern, the Madden‐Julian Oscillation, and its important remote perturbations over the midlatitude East Pacific and Southwest US. Additionally, a perturbation experiment carried out with one of the machine learning models illustrates their ability to not only predict the evolution of atmospheric fields, but also to learn and represent physical processes such as tropics‐extratropics Rossby wave propagation. Key Points Two machine learning weather forecast models exhibit state‐of‐the‐art prediction skill at the subseasonal time scale in the Pacific sector The models equal ECWMF in terms of Madden‐Julian oscillation (MJO) prediction skill, and they accurately predict the MJO propagation and associated teleconnections The two machine‐learning models represent key physical processes for subseasonal prediction, despite being trained for weather forecasting
Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires.” We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the hot–dry–windy index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.
Abstract The western United States (U.S.) has been experiencing more severe wildfires, in part due to climate change, but the underlying synoptic patterns and their modulation in driving fire weather is unclear. Here we investigated the relationship between weather regimes (WRs) and fire weather indices, specifically vapor pressure deficit (VPD) and the Canadian Forest Fire Weather Index. By identifying five singular WRs using k-means clustering, we found that a particular regime (WR-2), one characterized by a distinct tripolar wave train pattern over the continental U.S., has exhibited an increased frequency since 1980. The ascribed WR-2 regime was found to be mainly responsible for rising trends in the fire weather indices, especially VPD. Further, the average fire indices of the WR-2 regime played a more important role than the frequency in shaping the rising trends in the fire weather indices. The increased frequency of the WR-2 WR was mainly attributed to anthropogenic forcing and, the year-to-year variation of the frequency was associated with sea surface temperature anomalies over the subtropical eastern Pacific. Human-induced climate change might have furthered the exacerbation of wildfire danger in the western U.S. by modulating the behaviors of WRs and fire weather indices.
Abstract The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior skill than traditional physics-based NWP. However, among these leading DL models, there is a wide variance in both the training settings and architecture used. Further, the lack of thorough ablation studies makes it hard to discern which components are most critical to success. In this work, we show that it is possible to attain high forecast skill even with relatively off-the-shelf architectures, simple training procedures, and moderate compute budgets. Specifically, we train a minimally modified Swin Transformer V2 (SwinV2) on ERA5 data and find that it attains superior skill in terms of mean-square errors of deterministic forecasts when compared against the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS). Almost all DL–NWP systems share a core set of hyperparameters and design decisions. To aid and expedite future DL–NWP research, we present an in-depth, systematic exploration of different loss functions, model sizes and depths, patch sizes, and multistep training objectives. We also examine the model performance with metrics beyond the typical accuracy (ACC) and RMSE and investigate how the performance scales with model size. Through our open-source code, scoring pipelines, and models, we share our findings on key aspects of the training pipeline. These ablations reduce the necessity for expensive hyperparameter tuning and lower the barrier to entry for future DL–NWP research. Significance Statement This study investigates the potential of using large-scale transformer-based models for weather prediction, showing that it is possible to achieve high forecast accuracy with simpler, off-the-shelf architectures. By training a minimally modified SwinV2 transformer on ERA5 data, we show that the model achieves competitive forecast skill in terms of mean-square error for key variables, outperforming the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS) at all lead times. Our findings suggest that effective training strategies, such as multistep fine-tuning and channel-weighted losses, significantly enhance the model’s performance. However, we also highlight that these improvements come with trade-offs in other areas, such as ensemble spread and high-frequency spatial detail. This work highlights the promise of deep learning in improving weather forecasts, which could lead to better preparedness and response to weather events, ultimately benefiting society by providing more reliable weather predictions.
The weathering of sedimentary rocks in high-elevation catchments influences freshwater quality and the global carbon cycle. While individual biogeochemical mechanisms involved in this process are relatively well understood, quantifying their contributions to solute export and carbon fluxes under natural, transient conditions remains challenging. Here, we implement a numerical multidimensional and multiphase model to simulate coupled hydrological and biogeochemical processes in a shale-underlain, snow-dominated hillslope in the Rocky Mountains, Colorado. The model captures the dynamic interplay between soil respiration, mineral weathering, and climate-driven hydrological forcing, reproducing observed soil CO 2 dynamics, groundwater chemistry, and subsurface flow. Our results reveal that seasonal snowmelt enhances carbonate weathering by promoting the infiltration of CO 2 -rich water to depth, while pyrite oxidation is primarily sensitive to low water saturation that facilitates O 2 diffusion through the regolith. Topography modulates the spatial distribution of shale weathering, as steeper slopes enhance lateral drainage, favoring the delivery of reactants to greater depths. While shale weathering at our site acts as a transient carbon sink, with silicates and carbonates buffering acidity and promoting atmospheric CO 2 consumption (1% of soil-derived CO 2 ), the exported dissolved inorganic carbon is predominantly geogenic (∼73%). Consequently, when accounting for long-term marine carbonate precipitation. The current weathering regime represents a net source of carbon to the atmosphere. The oxidation of pyrite and petrogenic organic carbon together release approximately 0.9 mol·m −2 ·yr −1 of CO 2 . Our findings highlight the role of topography, hydroclimate, and the coupling between acid-base reactions in shaping the carbon balance and the solute exports in mountainous critical zones.
Reliable and continuous meteorological data are crucial for modeling the responses of energy systems and their components to weather and climate conditions, particularly in densely populated urban areas. However, existing long-term datasets often suffer from spatial and temporal gaps and inconsistencies, posing great challenges for detailed urban energy system modeling and cross-city comparison under realistic weather conditions. Here we introduce the Historical Comprehensive Hourly Urban Weather Database (CHUWD-H) v1.0, a 23-year (1998-2020) gap-free and quality-controlled hourly weather dataset covering 550 weather station locations across all urban areas in the contiguous United States. CHUWD-H v1.0 synthesizes hourly weather observations from stations with outputs from a physics-based solar radiation model and a reanalysis dataset through a multi-step gap filling approach. A 10-fold Monte Carlo cross-validation suggests that the accuracy of this gap filling approach surpasses that of conventional gap filling methods. Designed primarily for urban energy system modeling, CHUWD-H v1.0 should also support historical urban meteorological and climate studies, including the validation and evaluation of urban climate modeling.
Global weather forecast models are vital tools with numerous applications, including public safety, agriculture, and transportation. Recent advancements in artificial intelligence (AI) and deep learning (DL) have shown the potential to enhance weather forecasting accuracy and speed. In this study, we developed a short-term hourly weather forecast framework with a wavelet transform function for data preprocessing and a spatiotemporal DL model, PredRNN, for predicting five surface atmospheric variables, including wind speed and direction, mean sea level pressure (MSLP), temperature, and precipitation. The framework demonstrated promising results. It produces global forecasts at 0.25° (∼25 km) with a 1-day lead time RMSE of 1.8 m/s for wind components, 180 Pa for MSLP, and 1.8 K for temperature. Although our model does not surpass state-of-the-art AI weather forecast models across all metrics, it outperforms these models in precipitation forecasting and wind prediction at short lead times and achieves comparable accuracy for MSLP. Its native hourly forecasting capability, together with training on widely accessible GPU hardware, contributes meaningfully to the advancement of accessible DL weather forecasting methods. Our work highlights the importance of integrating temporal components and data transformation techniques to improve the predictability and accuracy of weather forecasts.
Machine learning (ML) techniques have emerged as promising approaches to improve regional weather forecast accuracy and reliability through data-driven methods. We propose a novel ML-based weather forecasting model, the Multiperiod Embed Net (MPENet). A key distinguishing feature of MPENet is its explicit utilization of the inherent cyclic nature in weather dynamics, unlike the autoregressive strategies commonly used in other ML weather forecasting approaches. Critical cyclic structures are identified via Fourier analyses of dynamic time series. Cyclicity in the convolutional representation is achieved by transforming one-dimensional time series of meteorological variables into two-dimensional tensors based on identified periods. This approach enables the model to leverage intrinsic weather patterns, enhancing regional forecast performance. To demonstrate the effectiveness of MPENet, we conduct a comparative analysis with Nvidia’s FourCastNet. Both models are trained on High-Resolution Rapid Refresh (HRRR) data from 2015 to 2022, over a 192 km × 192 km region in Tennessee. The comparisons are performed locally at two specific locations known to have different weather dynamics due to orographic effects: Crossville, on the relatively flat Cumberland Plateau with fewer topographic airflow disruptions, and Oak Ridge, in the ridge-and-valley region, where airflow is heavily influenced by surrounding valleys and mountains. Our results indicate that FourCastNet achieves strong accuracy at very short lead times, while MPENet maintains competitive skill and shows advantages in capturing temporal evolution over longer periods. Cross-correlation analyses of MPENet and FourCastNet predictions with the HRRR data suggest that encoding critical cyclicity into the network architecture leads to improvements in the forecasting skill.
The effectiveness of climate change impact assessments and the development of adaptation strategies depend on the availability of high-quality future weather data. However, significant gaps exist between the needs of the energy research community and the focus of the climate modeling community, primarily due to a historical lack of communication and collaboration between the two groups. Here, to address this issue, this work provides a comprehensive overview of the critical aspects involved in creating future weather data for building and energy system modeling, including emissions scenarios, general circulation models, downscaling methods, categories of future weather data, and uncertainties in climate simulations. Moreover, it critically evaluates the applicability and suitability of various types of future weather data in five key application scenarios: energy use analysis, resilience analysis, HVAC design, utility-scale analysis, and renewable energy analysis. Finally, this work presents recommendations for high-level actions and research directions to foster collaboration between the energy research and climate modeling communities and to promote the integration of future weather data into energy codes and the design practices of buildings and energy systems.
High-resolution, high-fidelity weather datasets are essential for testing and evaluating the resilience of power systems, particularly under extreme weather conditions. However, existing extreme weather datasets are typically derived from historical events that are localized and may lack the spatial and temporal resolution or scenario diversity needed to test largescale power systems. In this work, we propose a synthetic extreme weather simulation approach capable of generating targeted extreme events, such as hurricanes, using publicly available data sources. Preliminary results demonstrate the impact of a simulated Category 1 hurricane on renewable generation and critical infrastructure in California. The work aims to provide a flexible approach for creating multiple types of extreme weather scenarios across different regions, enabling comprehensive system stress testing, training, and resilience assessment.