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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Developing a Reduced 240-Bus WECC Dynamic Model for Frequency Response Study of High Renewable Integration

The ongoing changes in the generation resource mix, driven by the rapid adoption of inverter-based resources (IBR) as well as the early retirement of synchronous generators, are bringing new challenges to the planning and operation of bulk electric power systems. Consequently, there is an increasing need to understand, design, and quantify the reliability service provision from IBRs by performing integrated scheduling and dynamic simulations. However, test systems that have consistent scheduling and dynamic models rarely exist largely because of the decoupled nature of the two simulations on a synchronous generator-dominated system. This paper develops the dynamic model of a reduced Western Electricity Coordinating Council (WECC) system. In conjunction with the existing scheduling model, it is suitable for integrating scheduling and dynamic simulations. The reduced 240-bus WECC model reflects the generation resource mix of the Western Interconnection as of 2018. Moreover, the developed dynamic model is validated against field frequency events measured by FNET/GridEye and preserves the dominant inter-area oscillation mode in WECC.

dynamic simulation↗
NASA NTRS2023

Simulation of the Solar Energetic Particle Event on 2020 May 29 Observed by Parker Solar Probe

This paper presents a stochastic three-dimensional focused transport simulation of solar energetic particles (SEPs) produced by a data-driven coronal mass ejection (CME) shock propagating through a data-driven model of coronal and heliospheric magnetic fields. The injection of SEPs at the CME shock is treated using diffusive shock acceleration of post-shock suprathermal solar wind ions. A time-backward stochastic simulation is employed to solve the transport equation to obtain the SEP time–intensity profile at any location, energy, and pitch angle. The model is applied to a SEP event on 2020 May 29, observed by STEREO-A close to ∼1 au and by Parker Solar Probe (PSP) when it was about 0.33 au away from the Sun. The SEP event was associated with a very slow CME with a plane-of-sky speed of 337 km s −1 at a height below 6 RS as reported in the SOHO/LASCO CME catalog. We compute the time profiles of particle flux at PSP and STEREO-A locations, and estimate both the spectral index of the proton energy spectrum for energies between ∼2 and 16 MeV and the equivalent path length of the magnetic field lines experienced by the first arriving SEPs. We find that the simulation results are well correlated with observations. The SEP event could be explained by the acceleration of particles by a weak CME shock in the low solar corona that is not magnetically connected to the observers.

Solar energetic particles↗
DOE OSTI2025

Harvest legacies and climate change interact to shape forest structure and biomass through time

Forest structural change driven by climate trends has been observed worldwide and is expected to increase in the future. Management of forest structure has been an important tool for mitigating the impacts of climate change but forest structure may shift independently of management goals as it interacts with climate change. Here, we investigated the long-term impacts of harvest-based management strategies on structure and resistance to climate-induced biomass loss using a process-based ecosystem model for a midwestern USA hardwood forest. We identified aboveground biomass loss events and compared the cumulative number of these events following a five-year period of active management under four management strategies and two climate change scenarios. Management legacy had the clearest impact on climate-driven biomass loss over the mid-term (∼25 years) with the shelterwood scenario experiencing no loss events during this period. However, by the end of the century legacy effects faded and climate change severity became the driver of differences as greater warming scenarios experienced twice the loss events, and end-of-century loss events were 3–10 times more frequent than mid-century events. We found that while structure was distinct among harvest scenarios through the mid-century, differences were negligible by the end-of-century; identical to when management correlated with loss event frequency. Here, we found that loss events were preceded by a drop in precipitation two years prior, while no specific forest structure preceded a loss event. However, the structures preceding a biomass loss event were distinct across different legacies, implying additional influences of past management.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Storm-driven Mixing and Potential Impact on the Arctic Ocean

Observations of the ocean, atmosphere, and ice made by Ice-Ocean Environmental Buoys (IOEBs) indicate that mixing events reaching the depth of the halocline have occurred in various regions in the Arctic Ocean. Our analysis suggests that these mixing events were mechanically forced by intense storms moving across the buoy sites. In this study, we analyzed these mixing events in the context of storm developments that occurred in the Beaufort Sea and in the general area just north of Fram Strait, two areas with quite different hydrographic structures. The Beaufort Sea is strongly influenced by inflow of Pacific water through Bering Strait, while the area north of Fram Strait is directly affected by the inflow of warm and salty North Atlantic water. Our analyses of the basin-wide evolution of the surface pressure and geostrophic wind fields indicate that the characteristics of the storms could be very different. The buoy-observed mixing occurred only in the spring and winter seasons when the stratification was relatively weak. This indicates the importance of stratification, although the mixing itself was mechanically driven. We also analyze the distribution of storms, both the long-term climatology as well as the patterns for each year in the last two decades. The frequency of storms is also shown to be correlated- (but not strongly) to Arctic Oscillation indices. This study indicates that the formation of new ice that leads to brine rejection is unlikely the mechanism that results in the type of mixing that could overturn the halocline. On the other hand, synoptic-scale storms can force mixing deep enough to the halocline and thermocline layer. Despite a very stable stratification associated with the Arctic halocline, the warm subsurface thermocline water is not always insulated from the mixed layer.

Yang, Jiayan↗
DOE OSTI2021

Behavior of an air-cooled thermosiphon during a non-air gas ingress event

Here, this study investigates the transient response of an air-cooled thermosiphon subjected to a sudden non-air gas ingress event. Results presented are from experimental testing conducted on a large-scale thermal hydraulic facility that examines heat removal performance of the Reactor Cavity Cooling System (RCCS) concept, a passive safety system for advanced nuclear reactors. Relying on buoyancy driven natural circulation flow, these systems feature a series of riser standpipes and coupled chimney network that provide a pathway for heat rejection to the atmosphere. In this work, the facility was modified to allow a transition of the draft intake from normal ambient air, to draft intake from an open-top volume containing high purity argon. Testing began by establishing normal operation and allowing the facility to reach steady-state thermal hydraulic flow conditions with natural air draft. The transitioned sequence was then initiated which simultaneously changed the inlet boundary from open air to a pathway from the quiescent argon volume. This event caused 1,200 cu-feet of the heavy gas, twice the internal volume of total facility flow path, to be drawn into the inlet plenum and ingress into the heated riser standpipes. With the upper chimney network still containing residual air, there was insufficient density difference to maintain buoyancy driven natural circulation. After a period of only 90 s past the ingress event, the facility experienced complete flow stagnation before entering an extended period of severely degraded system flow. Due to the cessation of bulk fluid movement and subsequent failure of its heat removal function, fluid and structural temperatures began to rise sharply. Re-circulation patterns developed within the multiple parallel riser standpipes, where hot gas near the outlets was observed to travel downward and re-enter the inlet of adjacent channels. After approximately 18-minutes, fluid temperatures and their associated density difference rose to a level sufficient to allow reestablishment of buoyancy driven system flow, and ultimately, recovery of facility operation to normal behavior.

42 ENGINEERING↗
NASA NTRS2011

Analysis of Voyager Observed High-Energy Electron Fluxes in the Heliosheath Using MHD Simulations

The Voyager spacecraft (V1 and V2) observed electrons of 6-14 MeV in the heliosheath which showed several incidences of flux variation relative to a background of gradually increasing flux with distance from the Sun. The increasing flux of background electrons is thought to result from inward radial diffusion. We compare the temporal electron flux variation with dynamical phenomena in the heliosheath that are obtained from our MHD simulations. Because our simulation is based on V2 observed plasma data before V2 crossed the termination shock, this analysis is effective up to late 2008, i.e., about a year after the V2-crossing, during which disturbances, driven prior to the crossing time, survived in the heliosheath. Several electron flux variations correspond to times directly associated with interplanetary shock events. One noteworthy example corresponds to various times associated with the March 2006 interplanetary shock, these being the collision with the termination shock, the passage past the V1 spacecraft, and the collision with the region near the heliopause, as identified by W.R. Webber et al. for proton/helium of 7-200 MeV. Our simulations indicate that all other electron flux variations, except one, correspond well to the times when a shock-driven magneto-sonic pulse and its reflection in the heliosheath either passed across V1/V2, or collided with the termination shock or with the plasma sheet near the heliopause. This result suggests that variation in the electron flux should be due to either direct or indirect effects of magnetosonic pulses in the heliosheath driven by interplanetary shocks

Washimi, Haruichi↗
DOE OSTI2021

Model for current drive induced crash cycles in W7-X

In the Wendelstein 7-X (W7-X) stellarator, the vacuum rotational transform, ι, has a flat radial profile and does not cross any major rational resonance. Nevertheless, during plasma operation the ι–profile can be strongly modified by electron cyclotron current drive in such a way that the resulting ι-profile passes through low-order rational values, and this can trigger magnetohydrodynamic (MHD) events. Indeed, W7-X plasmas are sometimes subject to repetitive collapses of core confinement, which can be observed regardless of the direction in which the EC current is driven. Even though the origin of these MHD instabilities is under investigation, the crashes may be connected to the formation of magnetic islands and magnetic reconnection. In the present work, we try to shed light on the dynamics of different events happening during the course of sawtooth cycles in W7-X by proposing a model that combines a slow current diffusion with a recipe for fast relaxation that conserves the corresponding helical flux (Kadomtsev 1975 Fiz. Plazmy 1 710–15). We also propose a simple model based on Taylor relaxation (Taylor 1974 Phys. Rev. Lett. 33 1139), (Taylor 1986 Rev. Mod. Phys. 58 741) to predict the nonlinear redistribution of plasma current caused by the largest of the observed events.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Summary Distributed acoustic sensing (DAS) has been used in the oil and gas industry as an advanced technology for surveillance and diagnostics. Operators use DAS to monitor hydraulic fracturing activities, examine well stimulation efficacy, and estimate complex fracture system geometries. Particularly, low-frequency DAS can detect geomechanical events such as fracture hits because hydraulic fractures propagate and create strain rate variations in the rock. Analysis of DAS data today is mostly done post-job and subject to interpretation methods. However, the continuous and dense data stream generated live by DAS poses the opportunity for more efficient and accurate real-time data-driven analysis. The objective of this study is to develop a machine learning-based workflow that can identify and locate fracture-hit events in simulated strain rate responses correlated with low-frequency DAS data. In this paper, “fracture hit” refers to a hydraulic fracture originating from a stimulated well intersecting an offset well. We start with building a single fracture propagation model to produce strain rate patterns observed at a hypothetical monitoring well. This model is used to generate two sets of strain rate responses with one set containing fracture-hit events. The labeled synthetic data are then used to train a custom convolutional neural network (CNN) model for identifying the presence of fracture-hit events. The same model is trained again for locating the event with the output layer of the model replaced with linear units. We achieved near-perfect predictions for both event classification and localization. These promising results prove the feasibility of using CNN for real-time event detection from fiber-optic sensing data. Additionally, we use edge detection techniques to recognize fracture-hit event patterns in strain rate images. The fracture-hit location can be identified using recognized pixels in the image. The accuracy of edge detection-based location identification is also plausible, but edge detection is dependent on the assumption of pattern shape and image quality, hence it is less robust compared to CNN models. This comparison further supports the need for CNN applications in image-based real-time fiber-optic sensing event detection.

Engineering↗
DOE OSTI2023

Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Abstract Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.

42 ENGINEERING↗
DOE OSTI2025

Integrated Metrics for County-Level Resilience Ranking Using Entropy and TOPSIS

In the face of atypical weather events, power infrastructure failures, and limited resources for resilience investment, energy decision-makers need data-driven metrics to allocate resilience investments and maximize the reduction of power outage impacts. For state-level planning, for instance, ranking the resilience of each county is key to ensuring effective distribution of resources. In such cases, resilience for each spatial unit is multifaceted and is captured by a set of indicators (i.e., metrics) that can be combined into an overall score that reduces the complexity of power outage dynamics to a single decision metric. However, weighting of these indicators is often addressed by simplifying assumptions (i.e., equal weights) or semi-subjective methods that rely on user-defined weights that can introduce biases (e.g., weighted average score). Within the disaster risk reduction and resilience engineering community, a recurring challenge in multicriteria decision-making is the objective weighting of indicators for composite indices. To address this issue, we have leveraged a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) combined with an entropy-based weighting approach to calculated the integrated scores. This method objectively determines the importance of each metric, better discerns between spatial units (i.e., counties), and offers a more reliable ranking of counties according to their relative resilience attributes. By improving methods for integrating resilience indicators, our approach helps planners and decision-makers prioritize resources more effectively for more efficient resilience investments.

Bhusal, Narayan [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

Achieving reutilization of scheduling software through abstraction and generalization

Reutilization of software is a difficult goal to achieve particularly in complex environments that require advanced software systems. The Request-Oriented Scheduling Engine (ROSE) was developed to create a reusable scheduling system for the diverse scheduling needs of the National Aeronautics and Space Administration (NASA). ROSE is a data-driven scheduler that accepts inputs such as user activities, available resources, timing contraints, and user-defined events, and then produces a conflict-free schedule. To support reutilization, ROSE is designed to be flexible, extensible, and portable. With these design features, applying ROSE to a new scheduling application does not require changing the core scheduling engine, even if the new application requires significantly larger or smaller data sets, customized scheduling algorithms, or software portability. This paper includes a ROSE scheduling system description emphasizing its general-purpose features, reutilization techniques, and tasks for which ROSE reuse provided a low-risk solution with significant cost savings and reduced software development time.

Wilkinson, George J.↗
NASA NTRS1996

Plasma Heating and Flow in an Auroral Arc

We report direct observations of the three-dimensional velocity distribution of selected topside ionospheric ion species in an auroral context between 500 and 550 km altitude. We find heating transverse to the local magnetic field in the core plasma, with significant heating of 0(+), He(+), and H(+), as well as tail heating events that occur independently of the core heating. The 0(+) velocity distribution departs from bi-Maxwellian, at one point exhibiting an apparent ring-like shape. However, these observations are shown to be aliased within the auroral arc by temporal variations that arc not well-resolved by the core plasma instrument. The dc electric field measurements reveal superthermal plasma drifts that are consistent with passage of the payload through a series of vortex structures or a larger scale circularly polarized hydromagnetic wave structure within the auroral arc. The dc electric field also shows that impulsive solitary structures, with a frequency spectrum in the ion cyclotron frequency range, occur in close correlation with the tail heating events. The drift and core heating observations lend support to the idea that core ion heating is driven at low altitudes by rapid convective motions imposed by the magnetosphere. Plasma wave emissions at ion frequencies and parallel heating of the low-energy electron plasma are observed in conjunction with this auroral form; however, the conditions are much more complex than those typically invoked in previous theoretical treatments of superthermal frictional heating. The observed ion heating within the arc clearly exceeds that expected from frictional heating for the light ion species H(+) and He(+), and the core distributions also contain hot transverse tails, indicating an anomalous transverse heat source.

Moore, T. E.↗
NASA NTRS2002

Implementation of Adaptive Digital Controllers on Programmable Logic Devices

Much has been made of the capabilities of Field Programmable Gate Arrays (FPGA's) in the hardware implementation of fast digital signal processing functions. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used Proportional-Integral-Derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM- based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a Digital Signal Processor (DSP) device or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using DSP devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, Pulse Width Modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacemap. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive-control algorithm approaches. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Gwaltney, David A.↗
NASA NTRS2009

Observation and Interpretation of Energetic Neutral Hydrogen Atoms from the December 5, 2006 Solar Flare

We discuss observations of energetic neutral hydrogen atoms (ENAs) from a solar flare/coronal mass ejection event reported by Mewaldt et al. (2009). The observations were made during the 5 December 2006 X9 solar flare, located at E79, by the Low Energy Telescopes (LETs) on STEREO A and B. Prior to the arrival of the main solar energetic particle (SEP) event at Earth, both LETs observed a sudden burst of 1.6 to 15 MeV particles arriving from the Sun. The derived solar emission profile, arrival directions, and energy spectrum all show that the <5 MeV particles were due to energetic neutral hydrogen atoms produced by either flare or shock-accelerated protons. RHESSI measurements of the 2.2-MeV gamma-ray line provide an estimate of the number of interacting flare-accelerated protons in this event, which leads to an improved estimate of ENA production by flare-accelerated protons. CME-driven shock acceleration is also considered. Taking into account ENA losses, we conclude that the observed ENAs must have been produced in the high corona at heliocentric distances .2 solar radii.

Barghouty, A. F.↗
NASA NTRS2020

Hiatus of wetland methane emissions associated with recent La Niña episodes in the Asian monsoon region

The variability of methane emissions from wetlands in the tropics and northern temperate regions can explain more than 70% of the interannual variation in global wetland methane emissions, which are largely driven by climate variability. We use climate reanalysis, remote sensing wetland area dataset and simulations from 11 land models contributing to Global Methane Budget to investigate the interannual variation and anomalies of wetland methane emissions in the Asian Monsoon region. Methane emissions in this region steadily increased over 2000–2012. However, abnormally low methane emissions were found in equatorial fully humid (Af), warm temperate winter dry (Cw), and warm temperate fully humid (Cf) Asian Monsoon climate sub-regions in 2008, 2009 and 2011, respectively. These spatially-shifting low emissions occurred simultaneously with observed wetland area shrinkage due to abnormally low precipitation. Interannual variability of wetland methane emissions in Asian Monsoon region are primarily driven by South Asian Monsoon system. However, the abnormally low emissions are related to strong La Niña events, and its accompanying effect of weakened East Asian Monsoon system and eastward Western Pacific subtropical high, which drives the shifting pattern of rainfall, and thus the spatial pattern of methane emission anomalies.

wetlands↗
NASA NTRSDate not supplied

Ionospheric Response to Electron Precipitation Driven by Whistler-Mode Chorus Waves Measured by Arase Satellite and Simulated by STET and SPEAH-RIT

The origin of pulsating auroras has long been an open question. Arase satellite observation reported the direct evidence that precipitated electrons in pulsating aurora are driven by whistler-mode chorus wave activity. Our study presents the theoretical analysis of this observational event based on SuperThermal Electron Transport (STET) code that simulates the highly dynamic environment of measured wave intensities and particle fluxes. Specifically, the STET code simulated results confirm the delicate loss-cone observation results of this mission and reveal the broader energy range of precipitated electron fluxes that were not measurable by Arase satellite. These highly dynamic precipitating electron fluxes were applied to a newly developed code, the Superthermal Proton, Electron and Atomic Hydrogen tRansport in the Ionosphere and Thermosphere (SPEAH-RIT), to simulate ionospheric response of this phenomena, in particular, the enhancement of electron density, plasma temperatures, and ionospheric electric conductance at the footprints of Arase satellite.

George V. Khazanov↗
DOE OSTI2023

An Inventory of AI-ready Benchmark Data for US Fires, Heatwaves, and Droughts

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

Lin, Xinming↗