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At least 217 records · Page 12

Thermal Resilience of Buildings and Communities: A Multistakeholder Review of Metrics and Approaches

Increasing temperature-related hazards require a collective effort to assess and enhance the thermal resilience of buildings and communities to protect occupants’ safety and minimize property or infrastructure damage. However, limited coordination across stakeholders and lack of standardized procedures for resilience assessment undermine the effectiveness of extreme temperature mitigation and adaptation strategies across the building life cycle. This review examines the current literature on resilience metrics to address thermal stress and risk due to extreme indoor environments. Stakeholders of thermal resilience include architects and engineers, occupants, property owners, real estate developers, urban planners, and policymakers. Additionally, motivations for measuring thermal resilience are emphasized, such as safeguarding occupant health and survivability, protecting property, and ensuring business continuity during extreme weather events. This review provides actionable insights and identifies future research needs for enhancing resilience through tailored metrics for stakeholders during the planning, design, construction, operation, and retrofitting phases of buildings and communities.

building life cycle↗

Enhancing thermal resilience of US residential homes in hot humid climates during extreme temperature events

Increasing occurrences of extreme weather events such as winter storms and heat waves due to climate change pose enormous safety and health-related risks to people, particularly in economically disadvantaged communities. In this study, we investigate some of the most promising retrofittable and weatherization methods to keep the living zone of residential buildings within an acceptable safety level. We use hours of safety as the resilience metric, which is defined as the time taken by the building's indoor environment to reach a safety threshold temperature. We first identify various passive measures, such as adding extra insulation, improving air sealing, and integrating phase-change materials, which can operate without any external power during winter-storm and heat-wave events. We then employ a whole-building simulation tool to examine the impact of various combinations of retrofit measures and conduct a parametric study to determine the optimal solutions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Social-ecological interactions in a disaster context: Puerto Rican farmer households’ food security after Hurricane Maria

Islands are uniquely vulnerable to extreme weather events and food insecurity, and have additional response challenges due to their limited landmasses and economies, isolation, colonial legacies, and high dependence of food imports. Domestic farmers have a key role in producing food for island communities like Puerto Rico, which can safeguard food security when food importation may be challenging. Nevertheless, in the context of disaster, farmers themselves may be vulnerable to food insecurity and unable to contribute to domestic markets. This paper examines Puerto Rican farmers households’ food security in the aftermath of 2017’s Hurricane Maria using a social-ecological lens. Survey data from 405 farmers gathered eight months after Maria, coupled with biophysical data from the hurricane’s impacts (winds, rains, and landslides), were analyzed. Overall, 69% of farmers experienced at least one month of food insecurity in the aftermath of Hurricane Maria, and 38% reported persistent food insecurity (three months or more). A multinomial logistic regression suggests that biophysical impacts, but especially social factors, such as age and constraint access to external sources of support, are linked with persistent food insecurity. This suggests that the biophysical impacts of the hurricane interact with existing infrastructure and social resources to affect farmer vulnerability and the food environment in different ways. Thus, strengthening adaptive capacity in multiple domains can help farmers and vulnerable populations better navigate the disruptions faced during disasters to alleviate food insecurity.

54 ENVIRONMENTAL SCIENCES↗

Quantifying Moisture Sources of Arctic Atmospheric Rivers During the Recent Historical Period

Atmospheric rivers (ARs), filaments of intense atmospheric moisture transport, play a significant role in delivering moisture poleward into the Arctic and triggering weather extremes. Although previous studies have focused on large‐scale circulations driving these events, this study investigates ARs through attributing their moisture sources using the Community Atmosphere Model version 5 (CAM5) with moisture‐tagging capability. Examining ARs in the Atlantic and Pacific sectors of the Arctic separately revealed distinct contributions from remote versus local, and ocean versus land, moisture sources. Unlike non‐AR events, Arctic ARs primarily draw moisture from their respective ocean basins in lower‐latitude regions during the cold season, and shift to land sources in the warm season. Cold‐season ARs in the Atlantic and Pacific sectors source 73.2% and 85.3% of their moisture from their respective ocean basins at lower latitudes, however warm‐season contributions decrease to 41.3% and 29.4%. In contrast, mid‐ and high‐latitude continents combined contribute 40.1% and 51.3% in the warm season. Trajectory clustering analysis shows that AR moisture sources depend on both their genesis regions and transport pathways. In recent decades, the Arctic has experienced a moistening trend in both the cold and warm seasons. Our results suggest that local and remote sources equally drive the observed cold‐season moistening, whereas remote sources predominantly drive the warm‐season moistening. A better understanding of Arctic AR moisture sources under current climate conditions provides valuable insights into their potential future changes amid the projected heterogeneous northern hemispheric warming.

Arctic↗

Modelling climate analogue regions for a central European city

In this study, we describe a methodology to derive climate analogue cities for spatially highly resolved future climate scenarios. For the computation, a reduced and in hindsight bias-adjusted EURO-CORDEX EUR-11 dataset is used based on two climate scenarios (RCP4.5 and RCP8.5). A total of 389 European cities are processed by the algorithm, which uses five statistical climate variables (2-m air temperature average and amplitude, precipitation sum and amplitude, correlation between 2-m air temperature average and precipitation sum). Additionally, extreme weather events (hot days, summer days, tropical nights, extreme precipitation events) are calculated for further comparison and validation. Finding an appropriate analogue permits a more accurate derivation and depiction of necessary climate adaptation efforts and therefore assist decision-making in city planning. As an example of our method, we searched for plausible climate twins for the mid-sized city of Aachen (Germany) at the end of the twenty-first century. Our results show that the French city of Dijon is highly likely to become Aachen’s climate twin by the end of the century for RCP4.5. As for the scenario RCP8.5, no clear European analogue city could be determined, indicating that the city might enter a novel climate. The nearest match suggests the cities of Florence and Prato in Tuscany. However, considering climate indices, the encompassing region of the French–Spanish city triangle Bordeaux–Toulouse–Bilbao is a better fit. The developed algorithm can be applied to any of the cities included in the dataset.

54 ENVIRONMENTAL SCIENCES↗

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. Here, this paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

17 WIND ENERGY↗

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Case Study of the Impact of AIRS Temperature Retrievals on Numerical Weather Prediction

Large errors in numerical weather prediction are often associated with explosive cyclogenesis. Most studes focus on the under-forecasting error, i.e. cases of rapidly developing cyclones which are poorly predicted in numerical models. However, the over-forecasting error (i.e., to predict an explosively developing cyclone which does not occur in reality) is a very common error that severely impacts the forecasting skill of all models and may also present economic costs if associated with operational forecasting. Unnecessary precautions taken by marine activities can result in severe economic loss. Moreover, frequent occurrence of over-forecasting can undermine the reliance on operational weather forecasting. Therefore, it is important to understand and reduce the prdctions of extreme weather associated with explosive cyclones which do not actually develop. In this study we choose a very prominent case of over-forecasting error in the northwestern Pacific. A 960 hPa cyclone develops in less than 24 hour in the 5-day forecast, with a deepening rate of about 30 hPa in one day. The cyclone is not versed in the analyses and is thus a case of severe over-forecasting. By assimilating AIRS data, the error is largely eliminated. By following the propagation of the anomaly that generates the spurious cyclone, it is found that a small mid-tropospheric geopotential height negative anomaly over the northern part of the Indian subcontinent in the initial conditions, propagates westward, is amplified by orography, and generates a very intense jet streak in the subtropical jet stream, with consequent explosive cyclogenesis over the Pacific. The AIRS assimilation eliminates this anomaly that may have been caused by erroneous upper-air data, and represents the jet stream more correctly. The energy associated with the jet is distributed over a much broader area and as a consequence a multiple, but much more moderate cyclogenesis is observed.

Reale, O.↗

Guest Editorial: Special Section on Sustainable Energy for Enhancing Grid Resiliency

Extreme weather threatens lives, disables communities, and devastates energy generation, transmission, and distribution systems. These extreme events are likely to become more frequent or more intense due to climate change. Energy networks have shown significant vulnerability during record hurricanes, deadly heat waves, destructive wildfires, and winter storms in the past few years. Because of the energy transition process, modern power grids will feature a high penetration level of the use of renewable resources. The adoption of renewable energy and the rise of omnidirectional power delivery mean that our energy network is more decentralized than ever. In this new environment, grid operation becomes more complex, and achieving resiliency is more difficult than in the past. Many utilities are seeking the latest technologies to improve energy security and responsiveness to severe events. On the one hand, sustainable energy resources can provide emergency power and assist grid restoration in disastrous events. On the other hand, their volatility, susceptibility, interdependency, and other unique features must be carefully considered when being used for grid resilience enhancement. This special section brings together 22 papers that range from innovative research advances tackling fundamental challenges of achieving more resilient grids to real-world demonstrations of leveraging sustainable energy resources to enhance grid resiliency. These papers can be categorized into three groups.

climate change↗

Trustworthy AI for Extreme Event Prediction and Understanding

Our transformational science question is: can we revolutionize both the prediction and understanding of extreme events through trustworthy AI? Our use-cases include extreme weather such as tornadoes and hail as well as water-based events including extreme precipitation, compound flooding, harmful algal blooms, and sea turtle cold stunnings and nest inundations.

54 ENVIRONMENTAL SCIENCES↗

Evolution of Scenario Generation Capabilities in the ExaSGD Project

High-fidelity renewable energy scenarios and rare-event high-impact contingencies are essential for modeling operations and infrastructure expansion of the next generation power grids using exascale computing resources. As the ExaSGD project progressed, our capabilities for generating scenarios for modeling wind power output at multiple wind farms significantly improved, evolving from the use of independent random perturbations to importance sampling-based techniques capturing representative spatial-temporal relations. Contingency generation also improved, evolving from standard N-1 techniques to probabilistic models of infrastructure damage resulting from extreme weather events. We review the developments in the realistic intermittent-energy scenario forecasting and extreme-event contingency creation as the ExaSGD project progressed and discuss the future work in these areas.

economic dispatch↗

Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems, e.g., emergency asset management. Included is an example case study: Hurricane Dolly damaging a synthetic 2000-bus test system during its landing in Southern Texas. We perform statistical analysis of damages and discuss topological effects on the example synthetic grid. Also, we include a cursory evaluation of impacts using simplified operational models. Finally, we discuss how our method can be extended to use even higher-fidelity meteorological data sets and suggest directions for future work.

contingencies↗

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

data-driven forecasting↗

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources: Preprint

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Included is an example case study: Hurricane Dolly damaging a synthetic 2000 bus test system during its landing in Southern Texas. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems including emergency asset management. We perform statistical analysis of damages and quantify topological effects on example synthetic grid. Also, we investigate loss-of-load events during two-day economic dispatch experiment. Finally, we point out various shortcomings of our method and suggest how it can be improved.

contingencies↗

Muon Flux Variance from Severe Atmospheric Conditions

We analyze data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the QuarkNet Research Group at Fermilab to study the variance in muon flux due to extreme weather conditions. We considered three high-intensity storms for each season: winter, spring, and summer. The most significant observation was during winter, with a ~125% increase in muon flux corresponding to a temperature drop of ~20K. Additionally, we are developing CosmicWatch muon detectors to be used in tests for the MMBC/MAMBA Bubble Chamber. A pair of these detectors will be employed to trigger on muons, providing extremely precise and inexpensive tracking granularity in the form of a muon telescope.

Aamir, Yusuf↗

Dynamic Restoration Strategy for Distribution System Resilience Enhancement

In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/dis-turbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

distributed energy resources (DERs)↗

Building hypotheses to understand the synergistic effects of heat and pollution exposure in a changing climate

The increasing intensity, frequency, and duration of extreme weather events due to climate change poses a broad range of health risks, and the synergistic effects of extreme temperature co-occurring with other hazardous exposures such as air pollution may result in higher risks of all-cause mortality and cardiovascular and respiratory morbidity than exposure to either event alone. There are a number of hypothesized mechanisms for this interaction effect, including increased susceptibility to heat effects on chronic conditions affected by air pollution exposure, exacerbation of pollution effects due to temperature-induced stress, and shared pathophysiological pathways (e.g., systemic inflammation), but specific physiological mechanisms are not well understood. In this editorial, the authors discuss this aspect of a recently published study examining ambient formaldehyde combined with high temperature exposure and respiratory disease admissions among children. Here, the observation that the health effects of pollutants such as formaldehyde are amplified following periods of high temperature can help inform risk warning systems even without knowledge of the underlying physiological mechanisms, but a deeper biological understanding of the interaction effects of heat and ambient air pollution may better inform interventions. This is even more important in view of increases in both extreme temperature and pollution events tied to climate change.

Chambliss, Sarah [University of Texas at Austin, T↗