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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.
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In this case study of St. Mary’s Village, Alaska, we present a resilience evaluation exercise. A resilience framework is employed to identify system characteristics, relevant metrics, and resilience hazards and to assess the performance against the hazards with and without a distributed wind system installed. The results show the resilience benefits provided by the distributed wind installation against fuel shortage hazards and cold weather hazards. The resilience benefits can be assigned monetary values, which will be highly dependent on actual circumstances of the hazard, but provide insight into value streams of distributed wind that are not usually considered. For example, the single 900 kW turbine was found to prevent an average of 14,643 kWh of load from being dropped during a two-day diesel fuel shortage event, which saved the community $447,592 from the prevented outages. This case study serves as an example for novel power system resilience analysis and builds understanding of resilience hazards that are common across many power systems.
Numerical building models are typically forced with weather data from a limited number of “representative cities” or weather stations representing different climate regions. The use of representative weather stations reduces computational costs, but often fails to capture spatial heterogeneity in weather that may be important for simulations aimed at understanding how building stocks respond to a changing climate. We quantify the potential reduction in bias from using an increasing number of weather stations over the western U.S. The approach is based on deriving temperature and load time series using incrementally more weather stations, ranging from 8 to roughly 150, to capture weather across different seasons. Using 8 stations, one from each climate zone, across the western U.S. results in an average absolute summertime temperature bias of 7.2°F with respect to a spatially-resolved gridded dataset. The mean absolute bias drops to 2.8°F using all available weather stations. Temperature biases of this magnitude could translate to absolute summertime mean simulated load biases as high as 13.8%, a significant error for capacity expansion planners who may use these types of simulations. Increasing the size of the domain over which biases are calculated reduces their magnitude as positive and negative biases may cancel out. Using 8 representative weather stations can lead to a 20-40% overestimation of peak building loads during both summer and winter. Using weather stations close to population centers reduces both mean and peak load biases. This approach could be used by others designing aggregate building simulations to understand the sensitivity to their choice of weather stations used to drive the models.
We explore the contribution of fractures (joints) in controlling the rate of weathering advance for a low-porosity rock by using methods of homogenization to create averaged weathering equations. The rate of advance of the weathering front can be expressed as the same rate observed in non-fractured media (or in an individual block) divided by the volume fraction of non-fractured blocks in the fractured parent material. In the model, the parent has fractures that are filled with a more porous material that contains only inert or completely weathered material. The low-porosity rock weathers by reaction-transport processes. As observed in field systems, the model shows that the weathering advance rate is greater for the fractured as compared to the analogous non-fractured system because the volume fraction of blocks is <1. The increase in advance rate is attributed both to the increase in weathered material that accompanies higher fracture density, and to the increase in exposure of surface of low-porosity rock to reaction-transport. For constant fracture aperture, the weathering advance rate increases when the fracture spacing decreases. Equations describing weathering advance rate are summarized in the “List of selected equations”. If erosion is imposed at a constant rate, the weathering systems with fracture-bounded bedrock blocks attain a steady state. In the erosional transport-limited regime, bedrock blocks no longer emerge at the air-regolith boundary because they weather away. In the weathering-limited (or kinetic) regime, blocks of various size become exhumed at the surface and the average size of these exposed blocks increases with the erosion rate. For convex hillslopes, the block size exposed at the surface increases downslope. This model can explain observations of exhumed rocks weathering in the Luquillo mountains of Puerto Rico.
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.
Although bedrock weathering strongly influences water quality and global carbon and nitrogen budgets, the weathering depths and rates within subsurface are not well understood nor predictable. Determination of both porewater chemistry and subsurface water flow are needed in order to develop more complete understanding and obtain weathering rates. In a long-term field study, we applied a multiphase approach along a mountainous watershed hillslope transect underlain by marine shale. Here we report three findings. First, the deepest extent of the water table determines the weathering front, and the range of annually water table oscillations determines the thickness of the weathering zone. Below the lowest water table, permanently water-saturated bedrock remains reducing, preventing deeper pyrite oxidation. Secondly, carbonate minerals and potentially rock organic matter share the same weathering front depth with pyrite, contrary to models where weathering fronts are stratified. Thirdly, the measurements-based weathering rates from subsurface shale are high, amounting to base cation exports of about 70 kmolc ha−1 y−1, yet consistent with weathering of marine shale. Finally, by integrating geochemical and hydrological data we present a new conceptual model that can be applied in other settings to predict weathering and water quality responses to climate change.
This dataset provides hourly 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 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/BSEC-[STATIONID]_[SENSORTYPE]_hourly_[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'), 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_Parameters.pdf This document describes the surface weather parameters and units of measurement.
As the frequency and intensity of severe weather has increased, its effect on the electric grid has manifested in the form of significantly more and larger outages in the United States. This has become especially true for regions that were previously isolated from weather extremes. In this paper, we analyze the weather impacts on the electric power grid across a variety of weather conditions, draw correlations, and provide practical insights into the operational state of these systems. High resolution computational modeling of specific meteorological variables, computational approaches to solving power system models under these conditions, and the types of resiliency needs are highlighted as goal-oriented computing approaches are being built to address grid resiliency needs. An example analysis correlating outages to 1km day-ahead weather from two historical winter storms, calculated on a large cluster using a combination of interpolated and extrapolated inputs from multiple instrumented sites to workflows that produce primary meteorological outputs, is shown as initial proof of concept.
Although bedrock weathering strongly influences water quality and global carbon and nitrogen budgets, the weathering depths and rates within subsurface are not well understood nor predictable. Determination of both porewater chemistry and subsurface water flow are needed in order to develop more complete understanding and obtain weathering rates. In a long-term field study, we applied a multiphase approach along a mountainous watershed hillslope transect underlain by marine shale. Here we report three findings. First, the deepest extent of the water table determines the weathering front, and the range of annually water table oscillations determines the thickness of the weathering zone. Below the lowest water table, permanently water-saturated bedrock remains reducing, preventing deeper pyrite oxidation. Secondly, carbonate minerals and potentially rock organic matter share the same weathering front depth with pyrite, contrary to models where weathering fronts are stratified. Thirdly, the measurements-based weathering rates from subsurface shale are high, amounting to base cation exports of about 70 kmol c ha -1 y -1 , yet consistent with weathering of marine shale. Finally, by integrating geochemical and hydrological data we present a new conceptual model that can be applied in other settings to predict weathering and water quality responses to climate change.
Machine learning feature selection analyses were conducted to understand the impact of various weather attributes on power electricity demand and its components in the northwest United States. Load profiles data were obtained from the Northwest Energy Efficiency Alliance (NEEA) and processed to yield hourly load profiles with the same temporal resolution as the weather conditions data obtained from the National Oceanic and Atmospheric Administration (NOAA) for representative weather stations. The data were divided into five seasons; for each season, for each load component, the influences of weather factors were evaluated and quantified using cross-correlation, principal component analysis, and mutual information evaluation; then predictive models were developed based on the ranked/screened factors using the regression tree (RT) approach. After multi-fold cross-validation, the optimal complexity/depth of the regress tree models is found to vary for different load components, and the prediction accuracy using RT and weather data can be higher than 80% for heating and refrigeration, or lower than 30% for vent and cooking. The study provides guidance on improvement of load profile generation, or even serves as a standalone predictive tool for approximating load profiles in balance authorities or climate zones where load profile data is absent.
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.
Recent studies have suggested that thermodynamic limitations on chemical weathering rates exert a first-order control on riverine solute fluxes and by extension, global chemical weathering rates. As such, these limitations may play a prominent role in the regulation of carbon dioxide levels (pCO 2 ) over geologic timescales by constraining the maximum global weathering flux. In this study, we develop a theoretical scaling relationship between equilibrium solute concentrations and pCO 2 based on equilibrium constants and reaction stoichiometry relating primary mineral dissolution and secondary mineral precipitation. Here, we test this theoretical scaling relationship against reactive transport simulations of chemical weathering profiles under open-and closed-system conditions, representing partially and fully water-saturated regolith, respectively. Under open-system conditions, equilibrium bicarbonate concentrations vary as a power-law function of pCO 2 (y =kx n )where nis dependent on reaction stoichiometry and kis dependent on both reaction stoichiometry and the equilibrium constant. Under closed-system conditions, bicarbonate concentrations vary linearly with pCO 2 at low values and approach open-system scaling at high pCO 2 . To describe the potential role of thermodynamic limitations in the global silicate weathering feedback, we develop a new mathematical framework to assess weathering feedback strength in terms of both (1) steady-state atmospheric pCO 2 concentrations, and (2) susceptibility to secular changes in degassing rates and transient carbon cycle perturbations, which we term 1st and 2nd order feedback strength, respectively. Finally, we discuss the implications of these results for the effects of vascular land plant evolution on feedback strength, the potential role of vegetation in controlling modern solute fluxes, and the application of these frameworks to a more complete functional description of the silicate weathering feedback. Most notably, the dependence of equilibrium solute concentrations on pCO 2 may represent a direct weathering feedback largely independent of climate and modulated by belowground organic carbon respiration.
Commercial silicon heterojunction photovoltaic modules, known as amorphous-silicon-based heterojunction with intrinsic thin-film layer (HIT) modules, show average degradation after 10 years in the field. HIT modules weathered outdoors in Colorado and Florida display mostly uniform decreases in intensity when mapped with photoluminescence (PL) imaging compared to a control module. Flash-table-based current-voltage curves show that degradation is dominated by voltage loss. Samples are cored from each of the modules, and deep level transient spectroscopy (DLTS) detects three electron-trap defect states in all modules with activation energies of electron emission from the defects of 0.07, 0.16, and 0.50 eV. DLTS measurements on the weathered modules show an additional deep-level, electron-trap defect state with an activation energy of 0.51 eV and a trap density of approximately 10 12 cm –3 . The capture rate is measured using varying short filling pulse times, and the resulting capture cross section is estimated to be 1.1x10 –16 cm 2 . The development of the weathering-related defect level correlates to decreases in carrier lifetime, PL intensity, and module voltage. Various depths of the space charge region are probed with increments in applied reverse bias and filling-pulse bias. Furthermore, this DLTS depth profiling shows a trend of trap density increasing with less applied reverse bias, suggesting that the weathering-related defect increases carrier recombination toward the interface between the bulk silicon wafer and the junction-forming amorphous-silicon passivation layers.
The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.
We present that alteration of the uppermost surfaces of geologic materials is a pervasive process on planetary surfaces that is dependent upon factors including parent composition and the environment under which alteration is occurring. While rapid and pervasive in hot and humid climates on Earth, chemical weathering of rock surfaces has also been found to dominate in some of Earth's coldest and driest landscapes as well. Specifically, surfaces dominated by resistant fine-grained igneous rocks in the Antarctic preserve evidence of oxidative weathering processes, which represent the initial immature surface alteration processes that stagnate due to the lack of available water and kinetics necessary for the production of more mature alteration phases. In this study, we test the hypothesis that oxidative weathering also dominates the surfaces of sedimentary rocks throughout the Antarctic. We investigated the chemistry and mineralogy of a suite of sedimentary rocks from the Transantarctic Mountains ranging from fine-grained tuffs to coarse-grained sandstones and conglomerates. Additionally, our results show that, like the previously studied fine-grained igneous rocks in the Antarctic, sedimentary rocks generally showed only minor chemical weathering signatures at their surfaces relative to their interiors. However, unlike the igneous rocks in this earlier study, the sedimentary rocks exhibited a wide variety of non-systematic differences between surface and interior compositions. This variability of surface weathering signatures is equally as complex as the physical properties and compositions inherently present within these different sedimentary lithologies. Based on these analyses, it is apparent that oxidative weathering products do not dominate the surfaces of sedimentary rocks throughout the Transantarctic Mountains, which instead exhibit a wide array of weathering signatures that are likely dependent on both lithological and environmental factors. Considering that sedimentary lithologies are widespread across a significant fraction of the martian surface, our results suggest that observed alteration signatures limited to the surfaces of martian sedimentary rocks are most likely to be minor and to vary as a result of the lithological properties of the specific rock unit and not as a result of the widespread influences of the modern cold and dry climatic conditions.
Midlatitude extreme weather events are responsible for a large part of climate related damage, yet our understanding of these extreme events is limited, partly due to the lack of a theoretical basis for midlatitude extreme weather. In this letter, the local finite-amplitude wave activity (LWA) of Huang and Nakamura [2015] is introduced as a diagnostic of the 500-hPa geopotential height (Z500) to characterizing midlatitude weather events. It is found that the LWA climatology and its variability associated with the Arctic Oscillation (AO) agree broadly with the previously reported blocking frequency in literature. There is a strong seasonal and spatial dependence in the trend13 s of LWA in recent decades. While there is no observational evidence for a hemispheric-scale increase in wave amplitude, robust trends in wave activity can be identified at the regional scales, with important implications for regional climate change.