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Satellite-Based Ranking of the World’s Hottest and Coldest Cities Reveals Inequitable Distribution of Temperature Extremes
The identification of the world’s hottest and coldest cities fascinates both the public and the scientific community. However, the ranking of city temperatures, especially from the perspective of human discomfort, has been difficult. Here we estimated the monthly mean maximum and minimum 1-km resolution urban temperatures of 13,135 cities worldwide (2003–19) from the thermal discomfort perspective by combining in situ measurements, satellite-based land surface temperatures, fine-resolution intracity data, and reanalysis data. Manama, Bahrain, was identified as the hottest city (48.18° ± 1.31°C) and Yakutsk, Russia (–42.96° ± 0.72°C), as the coldest city. The global city temperatures followed a power-law pattern, characterized by cities with <0.3 million inhabitants covering 80% of the top 20% global cities with extreme temperatures. Our study reveals an inequitable pattern of global city temperature extremes and highlights the urgency of developing appropriate strategies to reduce climate change risks in small- and medium-sized cities with low development levels.
Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS
High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.
Object-Based Evaluation of Dynamical and Statistical Downscaled Precipitation Products over CONUS
High-resolution precipitation data, generated through dynamical downscaling (DD) or statistical downscaling (SD) of global climate model output, provide critical information for regional climate assessment and adaptation planning. Most downscaling development and validation have focused on accurate gridscale precipitation construction and ignored the spatial structure of precipitation across model grids and at the event scale. However, many applications, e.g., hydrologic modeling and the analysis using the downscaled precipitation, require a reasonable representation of the spatial structure of precipitation within watersheds. Therefore, a set of standard metrics to evaluate the representation of the spatial structure of individual storms across diverse downscaled precipitation products is desired. To address this need, we conducted an object-based evaluation of precipitation in decades-long DD and SD products over the contiguous United States (CONUS). Specifically, we evaluate their ability to reproduce various features of precipitation objects in the observations: total volume, precipitation area, peak intensity, and spatial structure. Multiple metrics (bias, Perkins score, and nonparametric statistical tests) are used to quantify model performance. Our evaluation reveals notable variations in performance among individual products across different climate zones and seasons, as well as between extreme and nonextreme events. In general, most DD products exhibit balanced performance across the four precipitation object features, while SD products vary more significantly in their performance across products. Based on this comprehensive evaluation, we provide guidance on choosing downscaled products for specific regions, seasons, and precipitation object features. These findings and recommendations can inform precipitation-relevant modeling and analysis over CONUS, guide future downscaling technique developments, and provide actionable information for climate impact assessment and adaptation.
Utah FORGE: Area Terrain Slope Data
This archive contains a terrain slope image, in units of degrees, of the Utah FORGE area near Roosevelt Hot springs. The data was derived from 0.5 m resolution LiDAR DEM data and is in a GeoTiff format. It was processed using ArcGIS.
Locating Operational Events of the Cooling Tower of a Nuclear Reactor with a Very Local Seismic Network
Geolocation of emergent seismic signals is challenging at close distances. Here, we used three-component data from a seismic network and a targeted experiment at a research nuclear reactor to locate seismic sources. Utilizing known events collected during the targeted experiment, we were able to infer source locations with seismic amplitudes and polarization characteristics of the data. Although the resolution of the source location is not perfect, the seismic amplitudes and polarization analysis offer useful constraints. For the known events, the source region inferred with our analysis includes the true source locations. Synthetic tests indicate the resolution is largely due to limited data coverage and measurement uncertainties because the synthetic tests show similar results compared with the field data. We identified the source of the unknown event through spectrum cross correlation between the signals from the known events and an unknown event. Our findings were confirmed by operational staff at the facility. When the propagation medium properties (i.e., seismic velocity and quality factor for attenuation) are known, our analysis can be applied to continuous data from a seismic array to infer both source amplitude and location. If the medium properties are not known, a targeted experiment can be conducted to estimate them.
Changes in When and Where People are Spending Time in Response to COVID-19
The COVID-19 pandemic has resulted in a significant change in driving behavior as people respond to the new environment. However, existing methods for analyzing driver behavior such as travel surveys and travel demand models are not suited for incorporating abrupt environmental disruptions. To address this, we analyze a set of high-resolution trip data and introduce two new metrics for quantifying driving behavioral shifts as a function of time, allowing us to compare the time periods before and after pandemic began. We apply these metrics to the Denver, Colorado metropolitan statistical area (MSA) to demonstrate the utility of the metrics. Then, we present a case study for comparing two distinct MSAs, Louisville, Kentucky; and Des Moines, Iowa which exhibit significant differences in the makeup of their labor markets. The results indicate that although the regions of study exhibit certain unique driving behavioral shifts, emerging trends can be seen when comparing between seemingly distinct regions. For instance, drivers in all three MSAs are generally shown to have spent more time at residential locations and less time in workplaces in the time period after the pandemic started. In addition, workplaces that may be incompatible with remote working, such as hospitals and certain retail locations, generally retained much of their pre-pandemic travel activity.
Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors
With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.
Machine Learning for Rapid Biomarker Discovery via Image-Omic Fusion
In this project we proposed to accelerate biomarker discovery and improve disease prediction by combining image and omic data using a single, unified modelling approach. We hypothesized that much richer information could be derived from both the image and omic data when they are analyzed together. Using imaging mass spectrometry (IMS) data collected from mice we fused image and omic data to increase the resolution of the IMS data. Using human data provided to us by Oregon Health and Science University, we developed an initial modeling framework that can increase the accuracy of both imaging and MS datasets.
A Study of the Effects of the Digital Noise Floor on Flight Data Measured using a Uniform Resolution Analog to Digital Converter.
Abstract not provided.
Super-Resolution Processing of FDTR Measurement Data for Defect Detection
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Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review
The goal of the circular economy (CE) is to transition from today's take-make-waste linear pattern of production and consumption to a circular system in which the societal value of products, materials, and resources is maximized over time. Yet circularity in and of itself does not ensure social, economic, and environmental performance (i.e., sustainability). Sustainability of CE strategies needs to be measured against their linear counterparts to identify and avoid strategies that increase circularity yet lead to unintended externalities. The state of the practice in quantitatively comparing sustainability impacts of circular to linear systems is one of experimentation with various extant methods developed in other fields and now applied here. While the proliferation of circularity metrics has received considerable attention, to-date, there is no critical review of the methods and combinations of methods that underlie those metrics and that specifically quantify sustainability impacts of circular strategies. Our critical review herein analyzes identified methods according to six criteria: temporal resolution, scope, data requirements, data granularity, capacity for measuring material efficiency potentials, and sustainability completeness. Results suggest that the industrial ecology and complex systems science fields could prove complementary when assessing the sustainability of the transition to a CE. Both fields include quantitative methods differing primarily with regard to their inclusion of temporal aspects and material efficiency potentials. Moreover, operations research methods such as multiple-criteria decision-making (MCDM) may alleviate the common contradictions which often exist between circularity metrics. This review concludes by suggesting guidelines for selecting quantitative methods most appropriate to a particular research question and making the argument that while there are a variety of existing methods, additional research is needed to combine existing methods and develop a more holistic approach for assessing sustainability impacts of CE strategies.
Geographic Shift and Environment Change of U.S. Tornado Activities in a Warming Climate
Even with ever-increasing societal interest in tornado activities engendering catastrophes of loss of life and property damage, the long-term change in the geographic location and environment of tornado activity centers over the last six decades (1954–2018), and its relationship with climate warming in the U.S., is still unknown or not robustly proved scientifically. Utilizing discriminant analysis, we show a statistically significant geographic shift of U.S. tornado activity center (i.e., Tornado Alley) under warming conditions, and we identify five major areas of tornado activity in the new Tornado Alley that were not identified previously. By contrasting warm versus cold years, we demonstrate that the shift of relative warm centers is coupled with the shifts in low pressure and tornado activity centers. The warm and moist air carried by low-level flow from the Gulf of Mexico combined with upward motion acts to fuel convection over the tornado activity centers. Employing composite analyses using high resolution reanalysis data, we further demonstrate that high tornado activities in the U.S. are associated with stronger cyclonic circulation and baroclinicity than low tornado activities, and the high tornado activities are coupled with stronger low-level wind shear, stronger upward motion, and higher convective available potential energy (CAPE) than low tornado activities. The composite differences between high-event and low-event years of tornado activity are identified for the first time in terms of wind shear, upward motion, CAPE, cyclonic circulation and baroclinicity, although some of these environmental variables favorable for tornado development have been discussed in previous studies.
Surface Electronic Structure of Cr Doped Bi 2 Se 3 Single Crystals
Here, by using angle-resolved photoemission spectroscopy, we showed that Bi 2-x Cr x Se 3 single crystals have a distinctly well-defined band structure with a large bulk band gap and undistorted topological surface states. These spectral features are unlike their thin film forms in which a large nonmagnetic gap with a distorted band structure was reported. We further provide laser-based high resolution photoemission data which reveal a Dirac point gap even in the pristine sample. The gap becomes more pronounced with Cr doping into the bulk of Bi 2 Se 3 . These observations show that the Dirac point can be modified by the magnetic impurities as well as the light source.
Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA
The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.
THE ORBITAL AND PHYSICAL PARAMETERS, AND THE DISTANCE OF THE ECLIPSING BINARY SYSTEM OGLE-LMC-ECL-25658 IN THE LARGE MAGELLANIC CLOUD
We present an analysis of a new detached eclipsing binary, OGLE-LMC-ECL-25658, in the Large Magellanic Cloud (LMC). The system consists of two late G-type giant stars on an eccentric orbit with an orbital period of ∼200 days. The system shows total eclipses and the components have similar temperatures, making it ideal for a precise distance determination. Using multi-color photometric and high resolution spectroscopic data, we have performed an analysis of light and radial velocity curves simultaneously using the Wilson–Devinney code. We derived orbital and physical parameters of the binary with a high precision of <1%. The masses and surface metallicities of the components are virtually the same and equal to 2.23±0.02 M{sub ⊙} and [Fe/H] = −0.63±0.10 dex. However, their radii and rates of rotation show a distinct trace of differential stellar evolution. The distance to the system was calculated using an infrared calibration between V-band surface brightness and (V–K) color, leading to a distance modulus of (m−M) = 18.452±0.023 (statistical) ± 0.046 (systematic). Because OGLE-LMC-ECL-25658 is located relatively far from the LMC barycenter, we applied a geometrical correction for its position in the LMC disk using the van der Marel et al. model of the LMC. The resulting barycenter distance to the galaxy is d{sub LMC} = 50.30±0.53 (stat.) kpc, and is in perfect agreement with the earlier result of Pietrzyński et al.
An Explanation of Remarkable Emission-line Profiles in Post-flare Coronal Rain
We study broad redshifted emission in chromospheric and transition region lines that appears to correspond to a form of post-flare coronal rain. Profiles of Mg ii, C ii, and Si iv lines were obtained using IRIS before, during, and after the X2.1 flare of 2015 March 11 (SOL2015-03-11T16:22). We analyze the profiles of the five transitions of Mg ii (the 3p−3s h and k transitions, and three lines belonging to the 3d−3p transitions). We use analytical methods to understand the unusual profiles, together with higher-resolution observational data of similar phenomena observed by Jing et al. The peculiar line ratios indicate anisotropic emission from the strands that have cross-strand line center optical depths (k line) of between 1 and 10. The lines are broadened by unresolved Alfvénic motions whose energy exceeds the radiation losses in the Mg ii lines by an order of magnitude. The decay of the line widths is accompanied by a decay in the brightness, suggesting a causal connection. If the plasma is ≲99% ionized, ion–neutral collisions can account for the dissipation; otherwise, a dynamical process seems necessary. Our work implies that the motions are initiated during the impulsive phase, to be dissipated as radiation over a period of an hour, predominantly by strong chromospheric lines. The coronal “rain” we observe is far more turbulent than most earlier reports have indicated, with implications for plasma heating mechanisms.