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DOE OSTI2024

Analysis of Near-Field Magnetic Responses on ZrTe 5 through Cryogenic Magneto-THz Nano-Imaging

One manifestation of light-Weyl fermion interaction is the emergence of chiral magnetic effects under magnetic fields. Probing real space magnetic responses at terahertz (THz) scales is challenging but highly desired, as the local responses are less affected by the topologically trivial inhomogeneity that is ubiquitous in spatially averaged measurements. Here, we implement a cryogenic THz microscopy instrument under a magnetic field environment—a task only recently achieved. We explore the technical approach of this system and characterize the magnetic field’s influence on our AFM operation by statistical noise analysis. We find evidence for local near-field spatial variations in the topological semimetal ZrTe 5 up to a 5-Tesla magnetic field and obtain near-field THz spectra to discuss their implications for future studies on the chiral magnetic effect.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗

DOE OSTI2021

Strain fields in twisted bilayer graphene

Van der Waals heteroepitaxy allows deterministic control over lattice mismatch or azimuthal orientation between atomic layers to produce long-wavelength superlattices. The resulting electronic phases depend critically on the superlattice periodicity and localized structural deformations that introduce disorder and strain. In this study we used Bragg interferometry to capture atomic displacement fields in twisted bilayer graphene with twist angles < 2°. Nanoscale spatial fluctuations in twist angle and uniaxial heterostrain were statistically evaluated, revealing the prevalence of short-range disorder in moiré heterostructures. By quantitatively mapping strain tensor fields, we uncovered two regimes of structural relaxation and disentangled the electronic contributions of constituent rotation modes. Further, we found that applied heterostrain accumulates anisotropically in saddle-point regions, generating distinctive striped strain phases. Our results establish the reconstruction mechanics underpinning the twist-angle-dependent electronic behaviour of twisted bilayer graphene and provide a framework for directly visualizing structural relaxation, disorder and strain in moiré materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

The need for multicomponent dust attenuation in modeling nebular emission: Constraints from SDSS-IV MaNGA

A fundamental assumption adopted in nearly every extragalactic study that analyzes optical emission lines is that the attenuation of different emission lines can be described by a single attenuation curve, scaled by a single reddening parameter, usually E(B - V). Here we show this assumption fails in many cases with important implications for derived results. We developed a new method to measure the differential nebular attenuation among three kinds of transitions: the Balmer lines of hydrogen; high-ionization transitions (> 13.6 eV) including [Ne III], [O III], and [S III]; and low-ionization transitions (≲13.6 eV) including [O II], [N II], and [S II]. This method bins the observed data in a multidimensional space spanned by attenuation-insensitive line ratios. Within each small bin, the variations in nebular parameters such as the metallicity and ionization parameter are negligible compared to the variation in the nebular attenuation. This allowed us to measure the nebular attenuation using both forbidden lines and Balmer lines. We applied this method to a sample of 2.4 million star-forming (SF) spaxels from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. We found that the attenuation of high ionization lines and Balmer lines can be well described by a single Fitzpatrick (1999, PASP, 111, 63) extinction curve with R V = 3.1. However, no single attenuation curve can simultaneously account for these transitions and the derived attenuation of low-ionization lines. This strongly suggests that different lines have different effective attenuations, likely because spectroscopy at hundreds of parsecs to kiloparsecs of resolution mixes multiple physical regions that exhibit different intrinsic line ratios and different levels of attenuation. As a result, the assumption that different lines follow the same attenuation curve breaks down. Using a single attenuation curve determined by Balmer lines to correct attenuation-sensitive forbidden line ratios could bias the nebular parameters derived by 0.06–0.25 dex at A V = 1, depending on the details of the dust attenuation model. Observations of a statistically large sample of H II regions with high spatial resolutions and large spectral coverage are vital for improved modeling and deriving accurate corrections for this effect.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Evaluating Offshore Infrastructure Integrity

Drilling in the offshore environment involves a complex network of infrastructure including pipelines, platforms, rigs, subsea installations, ports, and terminals. Government and industry partners have developed this network over many decades and it remains a critical part of the United States (U.S.) energy portfolio. Many of the major components of this system have been designed with a 20- to 30-year lifespan, yet consistent and growing energy demands support the need to extend the design life of existing infrastructure or repurpose it for secondary needs (i.e. enhanced oil recovery, carbon storage, and new wells). As a result, a growing portion of the offshore infrastructure in the U.S. is approaching or has exceeded its original design life. A critical step in ensuring the continued safe and effective operation of offshore infrastructure is developing a comprehensive understanding of the state of offshore infrastructure and the factors that effect it. The purpose of this project is to assess the current state of existing infrastructure and identify the factors involved in infrastructure degradation through the development and application of big data analytics, machine learning, and advanced spatio-temporal analysis. The project leverages existing data at NETL and combines it with new information on offshore oil and gas structures and the ambient offshore environment in an effort to identify patterns associated with infrastructure integrity. Building on the identified trends and patterns, this project incorporates exploratory analytics and spatial analysis tools in conjunction with machine learning and statistical models to characterize the condition of existing platforms in the offshore environment and predict their risk of failure.

02 PETROLEUM↗

DOE OSTI2025

Dynamics and lipid membrane coupling of the RAS-RAF complex revealed via multiscale simulations

To gain molecular and mechanistic insights into initiation of the RAS-RAF signaling cascade, we developed and used a combination of multiscale simulation and experimental approaches. The influence and impact of the membrane on RAS and RAF proteins is a factor we are just beginning to understand and appreciate in more detail. Molecular simulation is an ideal methodology to further study this complicated relationship between the membrane and associated proteins. Our previous work using Multiscale Machine-learned Modeling Infrastructure investigated different lipid compositions solely around the KRAS4b protein and the interplay between protein behavior and these membrane environments. Multiscale Machine-learned Modeling Infrastructure uses machine learning to couple adjacent simulation scales and has been efficiently scaled across some of the world’s largest high-performance computers. Recently, we have expanded this multiresolution framework to include the all-atom simulation scale and to incorporate the RAF RBDCRD domains. Here, we present the overall analysis results from this new simulation campaign comprising a mixture of RAS and RAF RBDCRD proteins. Approximately 35,000 coarse-grained and 10,000 all-atom molecular dynamics simulations were completed, sampled from a variety of protein/lipid composition configurations that were generated from a micron-scale continuum simulation containing hundreds of copies of the proteins. Our studies suggest that orientations of the RAS-RBDCRD complex on the membrane occupy distinct configurational states, and the spatial patterns of lipid arrangements around these different protein states are unique to each state. The extent and size of lipid “fingerprints” imposed on the membrane by the RAS-RBDCRD protein complex are significantly larger than observed for just the RAS protein on its own. These protein complexes strongly associate, but we do not observe statistically significant preferred protein-protein orientations. These observations indicate that spatial colocalization of RAS-RBDCRD proteins in the same vicinity may be assisted by specific membrane environments, acting to increase the probability of signaling complex formation.

Carpenter, Timothy S. [Lawrence Livermore National↗

DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗

DOE OSTI2022

FilDReaMS: II. Application to the analysis of the relative orientations between filaments and the magnetic field in four Herschel fields

Context. Both simulations and observations of the interstellar medium show that the study of the relative orientations between filamentary structures and the magnetic field can bring new insight into the role played by magnetic fields in the formation and evolution of filaments and in the process of star formation. Aims. We provide a first application of FilDReaMS, the new method presented in the companion paper to detect and analyze filaments in a given image. The method relies on a template that has the shape of a rectangular bar with variable width. Our goal is to investigate the relative orientations between the detected filaments and the magnetic field. Methods. We apply FilDReaMS to a small sample of four Herschel fields (G210, G300, G82, G202) characterized by different Galactic environments and different evolutionary stages. First, we look for the most prevalent bar widths, and we examine the networks formed by filaments of different bar widths as well as their hierarchical organization. Second, we compare the filament orientations to the magnetic field orientation inferred from Planck polarization data and, for the first time, we study the statistics of the relative orientation angle as functions of both spatial scale and H2 column density. Results. We find preferential relative orientations in the four Herschel fields: small filaments with low column densities tend to be slightly more parallel than perpendicular to the magnetic field; in contrast, large filaments, which all have higher column densities, are oriented nearly perpendicular (or, in the case of G202, more nearly parallel) to the magnetic field. In the two nearby fields (G210 and G300), we observe a transition from mostly parallel to mostly perpendicular relative orientations at an H 2 column density ≃ 1.1 × 10 21 cm -2 and 1.4 × 10 21 cm -2 , respectively, consistent with the results of previous studies. Conclusions. Our results confirm the existence of a coupling between magnetic fields at cloud scales and filaments at smaller scale. They also illustrate the potential of combining Herschel and Planck observations, and they call for further statistical analyses with our dedicated method.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R 2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Generating high-resolution total canopy SIF emission from TROPOMI data: Algorithm and application

Solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in modeling global terrestrial gross primary production (GPP). Canopy total SIF emissions (SIF total ) are mechanistically linked to the plant photosynthesis, and can be estimated from satellite observed SIF (SIF obs ) through radiative transfer modeling. However, the current satellite SIF obs and thus SIF total are available only at coarse spatial resolutions from several kilometers to tens of kilometers, inhibiting the application at fine spatial scales. Here, in this work, we proposed an algorithm to generate both global high-resolution SIF total (HSIF total ) and high-resolution SIF obs (HSIF obs ) at 1 km from low-resolution SIF obs (LSIF obs ) from the TROPOspheric Monitoring Instrument (TROPOMI), which has a spatial resolution at nadir of 3.5 km by 5.6–7 km. Our statistical method is based on the law of energy conservation and uses satellite derived fraction of absorbed photosynthetically active radiation, fluorescence efficiency, and the escape probability of fluorescence. We evaluated the accuracy of our HSIF total using the Orbiting Carbon Observatory-2 SIF (R 2 = 0.78). We found that the spatial resolution had clear effects on the relationship between HSIF total and GPP. We also compared HSIF total to 8-day averaged tower GPP from 135 flux sites and found that they were better correlated when HSIF total was averaged over a 1-km radius around the tower than when averaged over a larger radius. Our study provided a unique high-resolution HSIF total product, which will advance the estimation of GPP by extrapolating site-level relationships to the global scale.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data

When constructing an accurate, continental, in-situ-based, kilometer-scale, long-term record of the precipitation field and its spatiotemporal changes remains a significant challenge. Here, we determine the extreme-value behavior of the NEXRAD Stage IV radar-based quantitative precipitation estimate. We find that the climatology of 5-year daily return values in the contiguous United States East of the Rocky Mountains shows only slight variability on spatial scales smaller than (Formula presented.) ~100 km. In light of this finding, we test whether rain-gauge-only daily precipitation data sets can produce accurate extreme-value behavior at spatial scales finer than the spacing between gauges. We find that the 5-year daily return values are accurate at locations far from rain gauges only if the interpolation between gauges is carried out appropriately for extremes. Precipitation statistics derived from in-situ rain gauge data are therefore of sufficient spatial resolution to faithfully capture daily extremes over much of the eastern United States.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Three-Dimensional Imaging Lidar for Characterizing Particle Fields and Organisms in the Mesopelagic Zone

The ocean’s mesopelagic zone is largely uncharacterized despite its vital role in sustaining ocean ecosystems. The composition, cycling, and fate of particle fields in the mesopelagic lacks an integrative multi-scale understanding of organism migration patterns, distribution, and diversity. This problem is addressed by combining complementary technologies with overlapping size spectra, including profiler mounted optical scattering sensors, profiler, and ship mounted acoustic devices, and a custom Unobtrusive Multi-Static Lidar Imager (UMSLI). This unique sensor suite can observe distributions of particles including organisms over a six order of magnitude dynamic size range, from microns to meters. Overlapping size ranges between different methods allows for cross-validation. This work focuses on the lidar imaging measurements and optical backscattering and attenuation, covering a combined particle size range of 0.1 mm to several cm. Particles at the small end of this range are sized using an existing backscattering time series inversion method after Briggs et al. (2013). Larger particles are resolved with UMSLI over an expanding volume using three-dimensional photo-realistic laser serial imaging. UMSLI’s image rectifying ability over time allows for derivation of particle concentration, size, and spatial distribution. Technical details on the development and post-processing methods for the novel UMSLI system are provided. Image resolved particle size distributions (PSDs) revealed a size shift from smaller to larger particles (>0.5 mm) as indicated by flatter slopes from dawn (slope = 2.6) to dusk (slope = 3.0). PSD trends are supported by an optical backscatter and transmissometer time series inversion analysis. Size shifts in the particle field are largely attributed to aggregation effects. Images support evidence of temporal variation between dusk and dawn stations through statistical analysis of particle concentrations for particle sizes 0.50–5.41 mm. Spatial analysis of the particle field revealed a dominantly uniform distributed marine snow background. The importance and potential of integrated approaches to studying particle and organism dynamics in ocean environments are discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Significant DBSCAN+: Statistically Robust Density-based Clustering

Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.

Computer Science↗

DOE OSTI2024

Fractional Statistics

The quantum-mechanical description of assemblies of particles whose motion is confined to two (or one) spatial dimensions offers many possibilities that are distinct from bosons and fermions. We call such particles anyons. The simplest anyons are parameterized by an angular phase parameter θ. θ = 0, π correspond to bosons and fermions, respectively; at intermediate values, we say that we have fractional statistics. In two dimensions, θ describes the phase acquired by the wave function as two anyons wind around one another counterclockwise. It generates a shift in the allowed values for the relative angular momentum. Composites of localized electric charge and magnetic flux associated with an abelian U(1) gauge group realize this behavior. More complex charge-flux constructions can involve nonabelian and product groups acting on a spectrum of allowed charges and fluxes, giving rise to nonabelian and mutual statistics. Interchanges of nonabelian anyons implement unitary transformations of the wave function within an emergent space of internal states. Anyons of all kinds are described by quantum field theories that include Chern–Simons terms. The crossings of one-dimensional anyons on a ring are unidirectional, such that a fractional phase θ acquired upon interchange gives rise to fractional shifts in the relative momenta between the anyons. The quasiparticle excitations of fractional quantum Hall states have long been predicted to include anyons. Recently, the anyon behavior predicted for quasiparticles in the ν = 1/3 fractional quantum Hall state has been observed in both scattering and interferometric experiments. Excitations within designed systems, notably including superconducting circuits, can exhibit anyon behavior. Such systems are being developed for possible use in quantum information processing.

Physics↗

DOE OSTI2023

Tensor-to-scalar ratio forecasts for extended LiteBIRD frequency configurations

LiteBIRD is a planned JAXA-led cosmic microwave background (CMB) B-mode satellite experiment aiming for launch in the late 2020s, with a primary goal of detecting the imprint of primordial inflationary gravitational waves. Its current baseline focal-plane configuration includes 15 frequency bands between 40 and 402 GHz, fulfilling the mission requirements to detect the amplitude of gravitational waves with the total uncertainty on the tensor-to-scalar ratio, δr, down to δr < 0.001. A key aspect of this performance is accurate astrophysical component separation, and the ability to remove polarized thermal dust emission is particularly important. In this paper we note that the CMB frequency spectrum falls off nearly exponentially above 300 GHz relative to the thermal dust spectral energy distribution, and a relatively minor high frequency extension can therefore result in even lower uncertainties and better model reconstructions. Specifically, we compared the baseline design with five extended configurations, while varying the underlying dust modeling, in each of which the High-Frequency Telescope (HFT) frequency range was shifted logarithmically toward higher frequencies, with an upper cutoff ranging between 400 and 600 GHz. In each case, we measured the tensor-to-scalar ratio r uncertainty and bias using both parametric and minimum-variance component-separation algorithms. When the thermal dust sky model includes a spatially varying spectral index and temperature, we find that the statistical uncertainty on r after foreground cleaning may be reduced by as much as 30–50% by extending the upper limit of the frequency range from 400 to 600 GHz, with most of the improvement already gained at 500 GHz. We also note that a broader frequency range leads to higher residuals when fitting an incorrect dust model, but also it is easier to discriminate between models through higher χ 2 sensitivity. Even in the case in which the fitting procedure does not correspond to the underlying dust model in the sky, and when the highest frequency data cannot be modeled with sufficient fidelity and must be excluded from the analysis, the uncertainty on r increases by only about 5% for a 500 GHz configuration compared to the baseline.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Quantitative assessment of fitting errors associated with streak camera noise in Thomson scattering data analysis

Thomson scattering measurements in high energy density experiments are often recorded using optical streak cameras. In the low-signal regime, noise introduced by the streak camera can become an important and sometimes the dominant source of measurement uncertainty. In this paper, we present a formal method of accounting for the presence of streak camera noise in our measurements. We present a phenomenological description of the noise generation mechanisms and present a statistical model that may be used to construct the covariance matrix associated with a given measurement. This model is benchmarked against simulations of streak camera images. We demonstrate how this covariance may then be used to weight fitting of the data and provide quantitative assessments of the uncertainty in the fitting parameters determined by the best fit to the data and build confidence in the ability to make statistically significant measurements in the low-signal regime, where spatial correlations in the noise become apparent. These methods will have general applicability to other measurements made using optical streak cameras.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗

DOE OSTI2021

Analysis of Weather and Climate Extremes Impact on Power System Outage

This paper provides statistical analysis of the characteristics of power system outages to gain a better understanding of the impacts of the increasing severe weather conditions on the outages. 10-year historical power system outage data from the Bonneville Power Administration (BPA) were gathered together with co-located weather attributes and recorded extreme weather events in the service area, which are paired in comparable spatial and temporal scales, with a focus on each outage transmission line. Statistical frequency analysis and cross-tabular evaluation are performed to investigate the occurring frequency and duration of outages associated with extreme weather in this area of study. The study reveals that the weather-related outages can be mainly attributed to hail and thunderstorm events which correspond to up to 60% out of all failures in several transmission line types.

Ren, Huiying↗