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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Benefit of NASA's Atmosphere Observing System (AOS) Mission Lidar and Polarimeter Observations for Health and Air Quality Applications

The Atmosphere Observing System (AOS) seeks to explore fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. AOS will provide key information to enhance the communities’ ability to improve weather and air quality forecasting today, seasonal to sub-seasonal changes in the near future, and societal challenges resulting from climate change in the decades to come. A fundamental component of the AOS mission is ensuring that health and air quality applications are considered to the greatest extent possible in mission design. As a result, the Applications Impact Team (AIT) was implemented to address this objective. The overarching goal of the AIT is to help improve the capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data becomes available. To support these efforts, we leverage existing and near future mission applications activities and initiatives, such as the NASA CALIPSO, MAIA, TEMPO, and PACE missions to form a framework to enhance health and air quality applications for AOS. The unique synergy between lidar and polarimeter instruments onboard the AOS constellation, as well as diurnally varying observations of aerosol profiles, will provide new opportunities to engage health and air quality stakeholders for forecasting, monitoring, and warning of hazardous events (e.g., wildfire smoke, volcanic ash) that impact human health. Engaging with existing missions helps identify and understand data needs, gaps and opportunities for current and future stakeholders, determine what aerosol data products are of highest value and use, and helps connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS aerosol observations relevant for health and air quality applications, AIT activities and initiatives and how existing aerosol satellite missions and their applications activities can play a critical role in AOS applications development during mission design.

Melanie Follette-Cook↗
NASA NTRS2019

A Double Bootstrap Approach to Superposed Epoch Analysis to Evaluate Response Uncertainty

The association between climate variability and episodic events, such as the antecedent moisture conditions prior to wildfire or the cooling following volcanic eruptions, is commonly assessed using Superposed Epoch Analysis (SEA). In SEA the epochal response is typically calculated as the average climate conditions prior to and following all event years or their deviation from climatology. However, the magnitude and significance of the inferred climate association may be sensitive to the selection or omission of individual key years, potentially resulting in a biased assessment of the relationship between these events and climate. Here we describe and test a modified double-bootstrap SEA that generates multiple unique draws of the key years and evaluates the sign, magnitude, and significance of event-climate relationships within a probabilistic framework. This multiple resampling helps quantify multiple uncertainties inherent in conventional applications of SEA within dendrochronology and paleoclimatology. We demonstrate our modified SEA by evaluating the volcanic cooling signal in a Northern Hemisphere tree-ring temperature reconstruction and the link between drought and wildfire events in the western United States. Finally, we make our Matlab and R code available to be adapted for future SEA applications.

Dendroclimatology↗
DOE OSTI2025

Energy storage planning for enhanced resilience of power systems against wildfires and heatwaves

Extreme weather events pose significant risks to power grid stability due to their severe consequences and potential for widespread failures. Energy storage systems hold great potential for enhancing grid resilience against such events by providing reliable power during peak demand periods. However, accurately quantifying the size, location, and investment costs of new energy storage assets is a complex task, as energy storage planning decisions depend on the investment choices of other generation technologies and the integration of new transmission projects. Here, this paper presents a novel capacity expansion planning framework that simultaneously optimizes investments in energy storage, generation, and transmission, determining their optimal size, location, and type, while incorporating extreme weather events into long-term planning. More specifically, our stress-event-informed planning framework integrates the impact of heatwaves and wildfires into the planning process, identifying least-cost investment solutions that comply with policy goals and enhance grid resilience. The proposed framework employs machine-learning-based modeling to project heatwave-induced loads and performance-based risk assessment to evaluate wildfire-driven transmission line derates. Using industry-standard datasets to accurately represent the transmission topology of the Western Interconnection (WI) system, the proposed framework is applied to the WI 40-zone system, with investment decisions reported for the years 2030, 2035, and 2040. Simulation results reveal that with just a 10% increase in investment costs, resilience against extreme events can be significantly improved, with investment decisions heavily favoring energy storage, particularly 4-hour energy storage systems.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

The INSTEP Monitoring Network: Merging High-and-Low Cost Measurements to Characterize California Wildfires

Despite challenges with data quality and scope, low-cost sensor networks have skyrocketed in popularity over the last 15 years, making air quality data available on refined spatial scales. More recently, studies have leveraged both high and low-quality instruments to create stronger “hybrid” models, with most studies focusing on particulate matter. Low-cost measurements typically represent ground-level emissions only, providing context for human health issues from climate change-driven events such as wildfires. Since low-cost sensors’ capabilities are localized, daily events and microclimates tend to dominate the data rather than larger regional or atmospheric trends. Likewise, their low cost explains their high uncertainty. In contrast, some regulatory-grade instruments produce column measurements as well, providing reliable information on a broader scope. To bridge this gap while expanding into gas-phase measurements, we deployed 12 air quality sensor packages in California, USA during the 2022 wildfire season. These INSTEP (Inexpensive Network Sensor Technology Exploring Pollution) monitors measure carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2), and several hydrocarbons including methane (CH4) and formaldehyde (HCHO). Half of the monitors were co-located with remote sensing spectrometers: NASA Pandora and Total Column Carbon Observing Network (TCCON). The overlap in pollutants includes NO2, O3, and HCHO between the INSTEP monitors and the Pandora column measurements. TCCON covers column CO, CO2, and CH4, rounding out our comparison. Most of the monitors were distributed throughout the San Francisco Bay area, and an additional three were located within 100 km of Los Angeles. The sites ranged in geographic and population characteristics, including desert, mountainous, coastal, and urban locations. Since varying environmental conditions such as temperature and pressure are known to challenge sensor performance, we will apply newer sensor “calibration” techniques meant to combat this. We will normalize our sensor signals by z-scoring them prior to applying a single calibration model in the form of multivariate linear regression or an artificial neural network. While this technique has been validated for the hydrocarbon and ozone sensor types (metal oxide), it has not yet been tested on electrochemical and non-dispersive infrared sensors, which are also used in the INSTEP monitors. This will serve as a test to see if this normalization technique – or another – is most effective in accounting for environmental differences among sensors. Related data analysis efforts have found success with a variety of geospatial analysis techniques, including weighted network models in which high-quality instruments are given higher weights than their low-cost counterparts. Our preliminary analysis will focus on kriging, which uses a Gaussian algorithm to assign weights, providing estimated pollution levels at locations between monitors. Smoke trajectory and evolution will also be considered using both measurement types. We also aim to baseline subtract our emission estimates from each region to determine which portion of emissions are regional and local, further characterizing burn differences in northern and southern California fires. Future directions include using INSTEP jointly with TEMPO satellite data, and mobile deployments on aircraft and uncrewed aerial vehicles (UAV).

Low-cost sensors↗
NASA NTRS2018

NASA Provides the Capability to Deliver Near Real-Time JPSS Data to Users in Order to Monitor Time-Sensitive Applications Such as Wildfires, Floods, Volcanic Eruptions, Tropical Cyclones and Extreme Weather Events

NASA's Land, Atmosphere Near real-time Capability for EOS (Earth Observing System) (LANCE https://earthdata.nasa.gov/lance) serves near real time (NRT) data to monitor time sensitive applications such as monitoring wildfires, floods, volcanic eruptions, tropical cyclones and extreme weather events. It currently serves data and imagery from the Visible Infrared Imager Radiometer Suite (VIIRS) and Ozone Mapping and Profiler Suite (OMPS) S NPP (Suomi National Polar-orbiting Partnership) instruments and is in the process of integrating continuity data products from VIIRS and OMPS onboard the Joint Polar Satellite System (JPSS), via the JPSS data Hub, to continue to meet the needs of agencies, scientists and members of the general public. NASA's Earth Science Division (ESD) sponsored the EOSDIS development of LANCE in 2009 to provide a central point of access to high quality NRT data products and imagery for applications users. LANCE makes data available to the public within 3 hours of satellite observation and imagery within 4-5 hours of satellite observation. Full resolution browse imagery from LANCE are provided through the Global Imagery Browse Services (GIBS) which also fuels NASA's Worldview tool so that users can interactively browse near real time data. This data supports time critical applications and allows users to view current natural hazards and events and animate the imagery over time.

Near real time↗
NASA NTRS2008

A VARI-Based Relative Greenness from MODIS Data for Computing the Fire Potential Index

The Fire Potential Index (FPI) relies on relative greenness (RG) estimates from remote sensing data. The Normalized Difference Vegetation index (NDVI), derived from NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery is currently used to calculate RG operationally. Here we evaluated an alternate measure of RG using the Visible Atmospheric Resistant Index (VARI) derived from Moderate Resolution Imaging Spectrometer (MODIS) data. VARI was chosen because it has previously been shown to have the strongest relationship with Live Fuel Moisture (LFM) out of a wide selection of MODIS-derived indices in southern California shrublands. To compare MODIS-based NDVI-FPI and VARI-FPI, RG was calculated from a 6-year time series of MODIS composites and validated against in-situ observations of LFM as a surrogate for vegetation greenness. RG from both indices was then compared in terms of its performance for computing the FPI using historical wildfire data. Computed RG values were regressed against ground-sampled LFM at 14 sites within Los Angeles County. The results indicate the VARI-based RG consistently shows a stronger relationship with observed LFM than NDVI-based RG. With an average R2 of 0.727 compared to a value of only 0.622 for NDVI-RG, VARI-RG showed stronger relationships at 13 out of 14 sites. Based on these results, daily FPI maps were computed for the years 2001 through 2005 using both NDVI-RG and VARI-RG. These were then validated against 12,490 fire detections from the MODIS active fire product using logistic regression. Deviance of the logistic regression model was 408.8 for NDVI-FPI and 176.2 for VARI-FPI. The c-index was found to be 0.69 and 0.78, respectively. The results show that VARI-FP outperforms NDVI-FPI in distinguishing between fire and no-fire events for historical wildfire data in southern California for the given time period.

Schneider, P.↗
DOE OSTI2022

Climate change increases risk of extreme rainfall following wildfire in the western United States

Post-wildfire extreme rainfall events can have destructive impacts in the western United States. Using two climate model large ensembles, we assess the future risk of extreme fire weather events being followed by extreme rainfall in this region. By mid-21st century, in a high warming scenario (RCP8.5), we report large increases in the number of extreme fire weather events followed within 1 year by at least one extreme rainfall event. By 2100, the frequency of these compound events increases by 100% in California and 700% in the Pacific Northwest in the Community Earth System Model v1 Large Ensemble. We further project that more than 90% of extreme fire weather events in California, Colorado, and the Pacific Northwest will be followed by at least three spatially colocated extreme rainfall events within five years. Our results point to a future with substantially increased post-fire hydrologic risks across much of the western United States.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2025

Local and Regional Diurnal Variability of Aerosol Properties Retrieved by DSCOVR/EPIC UV Algorithm

The diurnal variability of aerosol optical depth retrieved from L1 point satellite radiance measurements closely matches surface observations. Seasonal variability of retrieved single scattering albedo is in good qualitative agreement with equivalent ground-based observations. EPIC aerosol products can be used in the qualitative and quantitative monitoring of extraordinary aerosol events resulting from wildfires and desert dust storms.

Aerosols, Absorption, Satellite retrieval↗
DOE OSTI2020

Resilient buildings for fire-adapted landscapes: EE and flexible loads integrated with solar and storage microgrids

Energy efficiency (EE) and flexible loads can be part of a resilient buildings packagewhen they are combined with solar and storage in a clean energy microgrid to reduce the carbonfootprint of buildings and enable resilience to extreme events. Recent large wildfires and an emerging understanding of the fire-adapted landscapes in the American West underscore the urgency of work towards scaling and commercializing these systems. For example, a recent power shutoff in Northern California (October 9-12, 2019) resulted in 738k customers disconnected at the peak of the outage and is emblematic of challenges to come. Our paper reports on insights from a clean energy microgrid deployment pilot that integrates a 50 kWAC PV array, a 109 kW / 174 kWh battery system, switchgear to safely isolate from the regional power system, and communicating controllers for HVAC and refrigeration. The project will be commissioned in May 2020 and is sited at a critical infrastructure site in rural Northern California – in this case a gasoline station with convenience store. Our experience and results shed light on capabilities of integrated microgrids to provide value to customers during blue sky conditions and resilience during black sky days with high fire risk, and what opportunities and barriers exist for scaling these integrated microgrid systems in the near term. We use a simulation model to estimate how EE and flexibility can extend the run time of solar and storage, improving the reliability of power at critical sites.

Alstone, Peter↗
DOE OSTI2022

Clean Indoor Air and Lower Environmental Impacts: Heat Recovery Ventilation in Cold Climates

Over half of Alaska homes are at risk of poor indoor air quality, which increases the risk of respiratory and other illnesses for people and animals. Heat recovery ventilators (HRVs) provide warm, fresh air to a building interior while minimizing energy use. They can be programmed to provide varying amounts of fresh air to ensure adequate ventilation for occupants and can be configured to filter air during wildfires or other events. In addition to improving the safety of indoor air and extending the life of a building, HRVs can improve surrounding outdoor air because they reduce the amount of fossil fuels needed to heat a building. Recent research by CCHRC has focused on lowering barriers to using HRVs in cold climates through laboratory evaluations and homeowner education. This poster will cover data on indoor air quality in Alaska, results of CCHRC research, and future research questions.

buildings↗
DOE OSTI2023

Hydrogen Energy Storage System at Borrego Springs Towards an H2 Enabled 100 Renewable Microgrid

San Diego Gas & Electric's Borrego Springs Microgrid is one of the largest microgrids in the USA, serving 2500 residential customers, 300 commercial and industrial customers, and has a peak demand of approximately 14 MW. This microgrid is located at the end of a long transmission line, and is subjected to extreme weather events like storms, wildfires, and flooding, which frequently cause grid outages. The microgrid currently relies on 3.65 MW of diesel-powered generators to provide grid-forming services during these outages, which results in greenhouse gas and criteria pollutant emissions. However, the microgrid has access to approximately 37 MW of installed solar generation and struggles with overgeneration and curtailment. 1.5 MW/4.5 MWh of grid-scale batteries have been installed to capture some the overgeneration and provide resiliency, but a longer-duration low-greenhouse gas emission energy storage solution is needed. In our project, the team will evaluate in the lab and demonstrate in the field a grid-forming fuel cell inverter that can provide grid-forming services while utilizing hydrogen's energy storage scalability. We will first utilize NREL's Renewable Energy Integration and Optimization (REopt) platform to perform analyses on future microgrid scenarios that use hydrogen assets to reduce or eliminate the need for diesel backup generators. We will then evaluate the grid-forming inverter through power hardware-in-the-loop and controller hardware-in-the-loop experiments at ARIES, and de-risk the field deployment and operation of hydrogen assets in the microgrid setting. Finally, this project will demonstrate the operation of the fuel cell inverter and updated microgrid controller by operating in grid-forming mode in the Borrego Springs microgrid.

Borrego Springs↗
DOE OSTI2024

Hydrogen Energy Storage System at Borrego Springs Towards an H2 Enabled 100% Renewable Microgrid

San Diego Gas & Electric's Borrego Springs Microgrid is one of the largest microgrids in the USA, serving 2500 residential customers, 300 commercial and industrial customers, and has a peak demand of approximately 14 MW. This microgrid is located at the end of a long transmission line, and is subjected to extreme weather events like storms, wildfires, and flooding, which frequently cause grid outages. The microgrid currently relies on 3.65 MW of diesel-powered generators to provide grid-forming services during these outages, which results in greenhouse gas and criteria pollutant emissions. However, the microgrid has access to approximately 37 MW of installed solar generation and struggles with overgeneration and curtailment. 1.5 MW/4.5 MWh of grid-scale batteries have been installed to capture some the overgeneration and provide resiliency, but a longer-duration low-greenhouse gas emission energy storage solution is needed. In our project, the team will evaluate in the lab and demonstrate in the field a grid-forming fuel cell inverter that can provide grid-forming services while utilizing hydrogen's energy storage scalability. We will first utilize NREL's Renewable Energy Integration and Optimization (REopt) platform to perform analyses on future microgrid scenarios that use hydrogen assets to reduce or eliminate the need for diesel backup generators. We will then evaluate the grid-forming inverter through power hardware-in-the-loop and controller hardware-in-the-loop experiments at ARIES, and de-risk the field deployment and operation of hydrogen assets in the microgrid setting. Finally, this project will demonstrate the operation of the fuel cell inverter and updated microgrid controller by operating in grid-forming mode in the Borrego Springs microgrid.

blackstart with fuel cells↗
DOE OSTI2026

A two-stage optical fusion framework for wildfire severity mapping across the conterminous United States

Accurate wildfire severity mapping (WSM) is essential for post-fire recovery planning, erosion risk assessment, ecosystem monitoring, and disaster risk reduction. Although Landsat and Sentinel optical imagery have been widely used for burn severity assessment, the added value of fusing multiple optical sensors has not been sufficiently quantified across diverse fire events, particularly since the launch of Landsat-9. This study evaluates whether multisensor optical fusion improves wildfire severity mapping relative to single-sensor baselines using Sentinel-2, Landsat-8, and Landsat-9 imagery across 40 wildfire events in the conterminous United States. We tested a two-stage fusion framework that combines feature-level fusion with pixel-level dimensionality reduction. First, feature-level fused datasets were created through early fusion by combining standardized post-fire bands from each sensor into a single predictor stack. Both raw reflectance bands and pairwise spectral transforms were retained to capture within- and cross-sensor spectral interactions. Second, Linear Discriminant Analysis was applied to both single-sensor and fused datasets to produce comparable low-dimensional feature spaces. Six machine-learning classifiers were then used to benchmark model performance with repeated spatially buffered train–test splits. Results show that Landsat-9 was the strongest single-sensor baseline. Among the fusion strategies, Sentinel-2 + Landsat-9 produced the most consistent improvement and reduced performance variability. Landscape-condition analysis further showed that this fusion was most beneficial in shrubland-dominated and high-terrain fires, where it achieved the highest overall mean accuracy and the fewest failures. In contrast, its benefits were less reliable in evergreen forests, mixed vegetation, and low- to moderate-elevation terrain. In operational settings, the Sentinel-2 + Landsat-9 configuration offers a practical solution for post-fire recovery planning, erosion-risk assessment, watershed management, and ecological monitoring when field observations are available and timely satellite-based information is needed.

Landsat↗
DOE OSTI2023

Wildfire Risk Evaluation Framework for Grid Operations and Planning

The United States (US) environmental protection agency's (EPA's) climate change indicators for wildfires show a long-term trend of increased annual wildfire activity, larger wildfire size, and more variable dynamics in wildfire behavior. This has caused more frequent preemptive public safety power shutoff (PSPS) events in the regions with recognized high wildfire risk. These preemptive power shutoffs attempt to prevent the ignition of a wildfire but it nonetheless renders the transmission line non-operational, which often sheds load of downstream communities. As wildfires and PSPS events become more frequent, it is crucial to find the communities most at risk of load shedding. To that end, this paper proposes the wildfire risk evaluation of the system (WiRES) framework, which is a performance-based framework that translates extreme weather-related and PSPS event probabilities into a cumulative probability of a non-operational transmission line. This study also provides a geospatial visualization tool that breaks down the entire western electrical coordinating council (WECC) region into 50 km grid cells which can be used to 1) filter out transmission lines with higher than a threshold outage probability, and 2) graphically discover the affected regions and their biophysical and socioeconomic metrics. %such as the social vulnerability index, population density, gross domestic product, etc. Lastly, an impact assessment study is conducted which connects the results of the proposed framework to python and powerworld-based contingency analysis to highlight the applicability of the framework.

Wildfire, Resilience, Extreme Events, Grid Reslien↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-Slt Silas Little- New Jersey

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Slt Silas Little- New Jersey. This is the FLUXNET version of the carbon flux data for the site US-Slt Silas Little- New Jersey produced by applying the standard ONEFlux (1F) software. Site Description - Wildfires, prescribed fires, insect defoliation events and windstorms are the common disturbances in the NJ Pinelands. The oak-dominated forest at Silas Little Experimental Forest was most recently defoliated by Gypsy moth (Lymantria dispar L.) in 2006 to 2008, with complete defoliation occuring in 2007. Following this multi-year defoliation event, oak mortality was significant, and resulted in the death of approximately 20 % of the overstory oaks, and a similar reduction in stand biomass. Previous disturbances have included windstorms and earlier Gypsy moth defoliation events in the 1990's. The last major wildfire to occur at and near the Experimental Forest was in 1963. Since then, a number of prescribed fires have been conducted in the vicinity of the Silas Little flux site.

Clark, Kenneth [USDA Forest Service]↗
DOE OSTI2022

Does Age, Residency, or Feeding Guild Coupled with a Drought Index Predict Avian Health during Fall Migration?

Birds are good indicators of environmental change and are often studied for responses to climate. Many studies focus on breeding birds, while fewer look at the migration period, which is a critical time for many birds. Birds are more susceptible to unusual climatic events during their migration due to the metabolic stress of long-distance movements. In the fall of 2020, an unusual cold weather event coupled with drought and wildfire smoke led to a large avian mortality event in New Mexico. Later analysis pointed to the mortality being largely due to starvation. This was the impetus for our research. We used 11 years of fall bird banding data from two locations, along with local drought indices, to determine what predicts avian health during the migration period. We used fat score data from over 15,000 individual birds to assess whether drought indices, age, diet, or residency influenced avian health using multiple logistic regression. We found that the probability of positive fat scores decreased as drought severity increased for younger, insectivorous, migratory birds. Insectivores had a higher probability of receiving a fat score greater than zero relative to local drought conditions, which is important, since many North American insectivores are in steep decline. Migratory birds showed a greater response than year-round residents, and older birds showed a lower but significant response compared to hatch-year birds. Our results suggest that migratory insectivores in the southwestern United States may be less resilient to drought-related climate change.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

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)↗