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Social media and digital platforms have become essential tools for the new generation of youth activists. However, these tools subject youth to both new (and old) forms of surveillance and control. Drawing on in-depth interviews and social media walkthroughs with 61 youth activists, I examine hybrid tactics that these youth employ to resist surveillance and other forms of digitally mediated control as they participate in politics and social movements. I show that even in democracies like the United States and Canada, for individuals along intersecting axes of marginalization (e.g. race, gender), public political acts do not capture the full range of young people's political repertoires. Young people, especially those from marginalized groups, adopt hidden, under-the-radar tactics in response to pressures of social, state, and corporate surveillance. I develop the concept of "digital infrapolitics" referring to the ways in which digital politics and activism go below the radar under surveillance and control. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

Objectives: We propose the origami plot, which maintains the original functionality of a radar chart and avoids potential misuse of its connected regions, with newly added features to better assist multicriteria decision-making., Study Design and Setting: Built upon a radar chart, the origami plot adds additional auxiliary axes and points such that the area of the connected region of all dots is invariant to the ordering of axes. As such, it enables ranking different individuals by the overall performance for multicriteria decision-making while maintaining the intuitive visual appeal of the radar chart. We develop extensions of the origami plot, including the weighted origami plot, which allows reweighting of each attribute to define the overall performance, and the pairwise origami plot, which highlights comparisons between two individuals., Results: We illustrate the different versions of origami plots using the hospital compare database developed by the Centers for Medicare & Medicaid Services (CMS). The plot shows individual hospital's performance on mortality, readmission, complication, and infection, as well as patient experience and timely and effective care, as well as their overall performance across these metrics. The weighted origami plot allows weighing the attributes differently when some are more important than others. We illustrate the potential use of the pairwise origami plot in electronic health records (EHR) system to monitor five clinical measures (body mass index [BMI]), fasting glucose level, blood pressure, triglycerides, and low-density lipoprotein ([LDL] cholesterol) of a patient across multiple hospital visits., Conclusion: The origami plot is a useful visualization tool to assist multicriteria decision making. It improves radar charts by avoiding potential misuse of the connected regions. It has several new features and allows flexible customization., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates GPROF V7 and multiple configurations of the GPROF-NN retrieval against ground-based radar mea- surements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPMMicrowave Imager (GMI) are validated over several years and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable precipitation estimates over CONUS. During all four assessed years, annual mean precipitation is within 8% of gauge-corrected radar measurements. Although biases of up to 25% are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reproduces the principal precipitation characteristics of each region. The effective resolution of GPROF V7 is found to be 51km over CONUS and 18km over the tropical Pacific. GPROF V7 produces robust precipitation estimates also for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPROF precipi- tation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error by about twenty percent for GPROF GMI while the effective resolution is improved to 31km over land and 15km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar/radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used in the training of the retrievals for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai'i in the United States. Unfortunately, fine temporal (subdaily) and spatial (<1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide "ground truth." There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O'ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures. Significance Statement: The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O'ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai'i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting. [ABSTRACT FROM AUTHOR]

Supercell storms are commonly responsible for severe hail, which is the costliest severe storm hazard in the United States and elsewhere. Radar observations of such storms are common and have been leveraged to estimate hail size and severe hail occurrence. However, many established relationships between radar-observed storm characteristics and severe hail occurrence have been found using data from few storms and in isolation from other radar metrics. This study leverages a 10-yr record of polarimetric Doppler radar observations in the United States to evaluate and compare radar observations of thousands of severe hail–producing supercells based on their maximum hail size. In agreement with prior studies, it is found that increasing hail size relates to increasing volume of high (≥50 dBZ) radar reflectivity, increasing midaltitude mesocyclone rotation (azimuthal shear), increasing storm-top divergence, and decreased differential reflectivity and copolar correlation coefficient at low levels (mostly below the environmental 0°C level). New insights include increasing vertical alignment of the storm mesocyclone with increasing hail size and a Doppler velocity spectrum width minimum aloft near storm center that increases in area with increasing hail size and is argued to indicate increasing updraft width. To complement the extensive radar analysis, near-storm environments from reanalyses are compared and indicate that the greatest environmental differences exist in the middle troposphere (within the hail growth region), especially the wind speed perpendicular to storm motion. Recommendations are given for future improvements to radar-based hail-size estimation. [ABSTRACT FROM AUTHOR]

The amount of energy available in a system constrains large-scale patterns of abundance. Here, we test the role of temperature and net primary productivity as drivers of flying insect abundance using a novel continental-scale data source: weather surveillance radar. We use the United States NEXRAD weather radar network to generate a near-daily dataset of insect flight activity across a gradient of temperature and productivity. Insect flight activity was positively correlated with mean annual temperature, explaining 38% of variation across sites. By contrast, net primary productivity did not explain additional variation. Grassland, forest and arid-xeric shrubland biomes differed in their insect flight activity, with the greatest abundance in subtropical and temperate grasslands. The relationship between insect flight abundance and temperature varied across biome types. In arid-xeric shrublands and in forest biomes the temperature–abundance relationship was indirectly (through net primary productivity) or directly (in the form of precipitation) mediated by water availability. These results suggest that temperature constraints on metabolism, development, or flight activity shape macroecological patterns in ectotherm abundance. Assessing the drivers of continental-scale patterns in insect abundance and their variation across biomes is particularly important to predict insect community response to warming conditions. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

As wind energy rapidly expands worldwide, information to minimize impacts of this development on biodiversity is urgently needed. Here we demonstrate how data collected by weather radar networks can inform placement and operation of wind facilities to reduce collisions and minimize habitat‐related impacts on nocturnally migrating birds. We found over a third of nocturnal migrants flew through altitudes within the rotor‐swept zone surrounding the North American Great Lakes, a continentally important migration corridor. Migrating birds concentrated in terrestrial stopover habitats within 20‐km from shorelines, a distance well beyond the current guidelines for construction of new land‐based facilities, and their distributions varied seasonally and at local and regional scales, creating predictable opportunities to minimize impacts from wind energy development and operation. Networked radar data are available across the United States and other countries and broad application of this approach could provide information critical to bird‐friendly expansion of this globally important energy source. [ABSTRACT FROM AUTHOR]

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 ~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. Plain Language Summary: Accurate measurement of the amount of precipitation that falls within a given region and time period is crucial for environmental modeling, climate change research, and resource and risk management. For all of those applications, it is desirable to understand not only how much precipitation falls on average, but also how much precipitation falls during an extreme event, such as a severe storm. Using data from weather radar, we show that certain statistical properties of extreme rainfall are highly correlated on spatial scales up to 100 km over the eastern United States. This means that rain gauge networks, which have typical intergauge spacings of roughly 30 km over the eastern United States, are dense enough to accurately measure these statistical properties. However, it is imperative to interpolate between the rain gauge measurements in a way that explicitly captures extremes if the application of interest requires capturing extremes accurately. Our research represents a step toward constructing an accurate, continental‐scale, long‐term, high‐resolution precipitation data set. Key Points: The 5‐years return values of extreme rainfall vary only minimally on spatial scales smaller than 100 kmThe gauge‐only Mountain Mapper algorithm underestimates 5‐year return values far from the locations of rain gaugesThe Risser et al. (2019) algorithm represents these statistics more accurately than Mountain Mapper over the majority of contiguous United States [ABSTRACT FROM AUTHOR]

An improved understanding of common differences between tornadic and nontornadic supercells is sought using a large set of observations from the operational NEXRAD WSR-88D polarimetric radar network in the contiguous United States. In particular, data from 478 nontornadic and 294 tornadic supercells during a 7-yr period (2011–17) are used to produce probability-matched composite means of microphysical and kinematic variables. Means, which are centered on echo-top maxima and in a horizontal coordinate system rotated such that storm motion points in the positive x dimension, are created in altitude relative to ground level at times of peak echo-top altitude and peak midlevel rotation for nontornadic supercells and times at and prior to the first tornado in tornadic supercells. Robust differences between supercell types are found, with consistent characteristics at and preceding tornadogenesis in tornadic storms. In particular, the mesocyclone is found to be vertically aligned in tornadic supercells and misaligned in nontornadic supercells. Microphysical differences found include a low-level radar reflectivity hook echo aligned with and ~10 km right of storm center in tornadic supercells and displaced 5–10 km down-motion in nontornadic supercells, a low-to-midlevel differential radar reflectivity dipole that is oriented more parallel to storm motion in tornadic supercells and more perpendicular in nontornadic supercells, and a separation between enhanced differential radar reflectivity and specific differential phase (with unique displacement-relative correlation coefficient reductions) at low levels that is more perpendicular to storm motion in tornadic supercells and more parallel in nontornadic supercells. [ABSTRACT FROM AUTHOR]

A new dual-polarization (DP) radar synthetic quantitative precipitation estimation (QPE) product was developed using a combination of specific attenuation A, specific differential phase KDP, and reflectivity Z to calculate the precipitation rate R. Specific attenuation has advantages of being insensitive to systematic biases in Z and differential reflectivity ZDR due to partial beam blockage, attenuation, and calibration while more linearly related to R than other radar variables. However, the R(A) relationship is not applicable in areas containing ice. Therefore, the new DP QPE applies R(A) in areas where radar is observing pure rain, R(KDP) in regions potentially containing hail, and R(Z) elsewhere. Further, an evaporation correction was applied to minimize false light precipitation related to virga. The new DP QPE was evaluated in real time over the conterminous United States and showed significant improvements over previous radarQPE techniques that were based solely on R(Z) relationships. The improvements included reduced dry biases in heavy to extreme precipitation during the warm season. The new DP QPE also reduced errors and spatial discontinuities in regions impacted by partial beam blockage. Further, the new DP QPE reduced wet bias for scattered light precipitation in the southwest and north central United States where there is significant boundary layer evaporation. [ABSTRACT FROM AUTHOR]

As part of a suite of quality control methods applied to Canadian and American weather radar data before their assimilation into a numerical weather prediction model, the combination of thresholded depolarization ratio and a speckle filter was applied to American data with the purpose of identifying and removing non-precipitation echoes. This polarimetric quality control replaces a set of image-analysis-based methods used in a previous study and based on reflectivity information only. The old and new quality-controlled results were objectively assessed using meteorological aerodrome report (METAR)-based precipitation occurrence observations and a set of five common contingency table skill scores with all available Next Generation Weather Radar (NEXRAD) Level II data from the contiguous United States for August 2016. The new quality control yields consistently improved skill scores, indicating higher quality radar data for downstream application. The process whereby the radar data are quality controlled and assessed comprises a framework with the ability to monitor the impacts of quality control to radar data quality over time. In turn, this allows for the introduction of changes to data acquisition and processing with the ability to monitor the impacts on data quality: a scientific evidence-based quality assurance process as part of change management. [ABSTRACT FROM AUTHOR]

Small industrial sources collectively release large amounts of pollution, including particulate matter (PM) that contributes to air quality problems in the United States and elsewhere. We study one such type of industrial facility, concrete batch plants, and analyze PM emissions and siting patterns of 131 plants located in Harris County, Texas. We find that concrete batch plants in Harris County are collectively a major pollution source, contributing between 38 and 111 tons of primary PM 2.5 emissions (between 26%-76% of PM 2.5 from the median Texas oil refinery) and between 109 and 493 tons of primary PM 10 emissions (between 64%-290% of PM 10 from the median refinery). Estimates from an integrated assessment model suggest that health damages from the PM 2.5 emissions alone amount to $29 million annually, reflecting two additional premature deaths per year. We further find that concrete batch plants in Harris County are disproportionately located in census tracts with more low-income, Hispanic, and Black populations, thereby raising important environmental justice questions. On the basis of these findings, we argue that small pollution sources require more air quality monitoring and emissions reporting and that regulatory agencies should consider cumulative environmental and health impacts of these sources as part of the permitting process.

In this paper, we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this paper, we report the results of a multi-institutional effort to develop advanced BTD algorithms for a real-world GPR BTD system. The effort involved five institutions with substantial experience with the development of GPR-based BTD algorithms. In this paper, we report the technical details of the advanced algorithms submitted by each institution, representing their latest technical advances, and many state-of-the-art GPR-based BTD algorithms. We also report the results of evaluating the algorithms from each institution on the large experimental data set used for development. The experimental data set comprised 120 000 m2 of GPR data using surface area, from 13 different lanes across two U.S. test sites. The data were collected using a vehicle-mounted GPR system, the variants of which have supplied data for numerous publications. Using these results, we identify the most successful and common processing strategies among the submitted algorithms, and make recommendations for GPR-based BTD algorithm design. [ABSTRACT FROM AUTHOR]

After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation. [ABSTRACT FROM AUTHOR]

Abstract The increasing knowledge in the capabilities of satellite imagery to hazard applications is especially useful in emergency situations where timing and ability to cover large areas are of the essence. For optical imagery, cloud coverage can corrupt an image rendering it unusable for intended emergency analyses. This study proposes the use of Synthetic Aperture Radar (SAR) imagery for burn severity analysis for western United States sites, as an alternative to its optical based counterpart, differenced normalized burn ratio (dNBR). Unlike optical sensors, the radar sensor is an active sensor that is able to penetrate clouds and smoke, an attribute that is crucial in emergency situations where immediate burn severity data are needed to assess the vulnerability of fire affected areas to post-fire hazards. Using C5 decision tree algorithm we developed a SAR-based metric that attempts to classify burn severities of fire affected locations in the western USA. We then compared the performance of this developed metric to that obtained by the existing dNBR metric, to determine if there is any merit to its adoption as an alternative for the western USA landscape. The results showed the SAR approach to produce higher validation metrics in comparison to the dNBR. It had an overall accuracy and kappa of 60% and 0.35, respectively, in comparison to the 35% and 0.1 of the dNBR approach. This shows an improved ability to quickly obtain burn severity data and make better informed decisions in emergency situations. [ABSTRACT FROM AUTHOR]

Aim: Measuring avian migration can prove challenging given the spatial scope and the diversity of species involved. No one monitoring technique provides all the pertinent measures needed to capture this macroscale phenomenon – emphasizing the need for data integration. Migration phenology is a key metric characterizing large‐scale migration dynamics and has been successfully quantified using weather surveillance radar (WSR) data and community science observations. Separately, both platforms have their limitations and measure different aspects of bird migration. We sought to make a formal comparison of the migration phenology estimates derived from WSR and eBird data – of which we predict a positive correlation. Location: Contiguous United States. Time period: 2002–2018. Major taxa studied: Migratory birds. Methods: We estimated spring and autumn migration phenology at 143 WSR stations aggregated over a 17‐year period (2002–2018), which we contrast with eBird‐based estimates of spring and autumn migration phenology for 293 nocturnally migrating bird species at the 143 WSR stations. We compared phenology metrics derived from all species and WSR stations combined, for species in three taxonomic orders (Anseriformes, Charadriiformes and Passeriformes), and for WSR stations in three North American migration flyways (western, central and eastern). Results: We found positive correlations between WSR and eBird‐based estimates of migration phenology and differences in the strength of correlations among taxonomic orders and migration flyways. The correlations were stronger during spring migration, for Passeriformes, and generally for WSR stations in the eastern flyway. Autumn migration showed weaker correlation, and in Anseriformes correlations were weakest overall. Lastly, eBird‐based estimates slightly preceded those derived from WSR in the spring, but trailed WSR in the autumn, suggesting that the two data sources measure different components of migration phenology. Main conclusions We highlight the complementarity of these two approaches, but also reveal strong taxonomic and geographic differences in the relationships between the platforms. [ABSTRACT FROM AUTHOR]

Upright convection can impact extratropical cyclone (ETC) precipitation and dynamics differently than isentropic ascent, but how often it occurs and how much it contributes to total precipitation within an ETC have not been systematically documented in previous literature. Herein, convection in ETCs is analyzed using ground‐based radar observations over the Eastern United States. Convection occupies 1%–5% of the precipitating region and constitutes 1%–15% of total cyclone precipitation. Furthermore, the location of convective activity occurs preferentially in the warm sector early in a cyclone's life cycle, and then as the storm evolves, more convection occurs along and behind the cold front. The precipitation rates in regions with convection are more likely to be stronger than those in other precipitating regions of the cyclone. However, the cyclones with the largest area‐average precipitation rates include only a small contribution from convection. Plain Language Summary: This research is motivated by the potential influence of convection on extratropical cyclone circulation. We utilize a newly derived dataset of upright convection and a cyclone‐tracking framework to quantify convective activity in extratropical cyclones over the United States, east of the Rocky Mountains. We find that upright convection only occupies a small portion of the precipitating region, and the relative contribution to total precipitation has relatively small variability as the cyclone evolves. We find that the location of convective activity occurs preferentially in the warm sector early in a cyclone's life cycle, and as the storm evolves, more convection occurs along and behind the cold front. We find that precipitation rates in regions with convection are more likely to be stronger than those in other precipitating regions of the cyclone. However, the cyclones with the largest area‐average precipitation rates include only a small contribution from convection. Key Points: Convection occurs in 1%–5% of precipitating regions of extratropical cyclones over eastern United States and contributes 1%–15% of total precipitationThe majority of upright convection occurs ahead of the cold front and disperses into other regions as the cyclone agesWhile convection produces larger rain rates, cyclones with greater total precipitation do not have a large convective contribution [ABSTRACT FROM AUTHOR]

Weather surveillance radars are increasingly used for monitoring the movements and abundances of animals in the airspace. However, analysis of weather radar data remains a specialised task that can be technically challenging. Major hurdles are the difficulty of accessing and visualising radar data on a software platform familiar to ecologists and biologists, processing the low‐level data into products that are biologically meaningful, and summarizing these results in standardized measures. To overcome these hurdles, we developed the open source R package bioRad, which provides a toolbox for accessing, visualizing and analyzing weather radar data for biological studies. It provides functionality to access low‐level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics. The package aims to standardize methods for extracting and reporting biological signals from weather radars. Here we describe a roadmap for analyzing weather radar data using bioRad. We also define weather radar equivalents for familiar measures used in the field of migration ecology, such as migration traffic rates, and recommend several good practices for reporting these measures. The bioRad package integrates with low‐level data from both the European radar network (OPERA) and the radar network of the United States (NEXRAD). bioRad aims to make weather radar studies in ecology easier and more reproducible, allowing for better inter‐comparability of studies. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Weather radars and gauge observations are the primary observations to determine the coverage and magnitude of precipitation; however, radar and gauge networks have significant coverage gaps, which can underrepresent or even miss the occurrence of precipitation. This is especially noticeable in mountainous regions and in shallow precipitation regimes. The following study presents a methodology to improve spatial representations of precipitation by seamlessly blending multiple precipitation sources within the Multi-Radar Multi-Sensor (MRMS) system. A high spatiotemporal resolution multisensor merged quantitative precipitation estimation (QPE) product (MSQPE) is generated by using gauge-corrected radar QPE as a primary precipitation source with a combination of hourly gauge observations, monthly precipitation climatologies, numerical weather prediction short-term precipitation forecasts, and satellite observations to use in areas of insufficient radar coverage. The merging of the precipitation sources is dependent upon radar coverage based on an updated MRMS radar quality index, surface and atmospheric conditions, topography, gauge locations, and precipitation values. Evaluations of the MSQPE product over the western United States resulted in improved statistical measures over its individual input precipitation sources, particularly the locally gauge-corrected radar QPE. TheMSQPE scheme demonstrated its ability to sufficiently fill in areas where radar alone failed to detect precipitation due to significant beam blockage or poor coverage while minimizing the generation of false precipitation and underestimation biases that resulted from radar overshooting precipitation. [ABSTRACT FROM AUTHOR]

The brown marmorated stink bug, Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), is a polyphagous invasive insect and currently one of the most threatening agricultural pests in the USA and globally. Nymphs are highly mobile, moving among host plants, and causing significant damage. Thus, understanding dispersal biology for all life stages is critical for the development of reliable monitoring and management programs. Here, we evaluated the influence of harmonic radar as a tool to study dispersal ecology of nymphal H. halys; we measured the impact of glues and tag attachment on survivorship and mobility in the laboratory and validated in the field that tagged and released nymphs could be tracked on baited and unbaited host and non‐host plants using harmonic radar. In the laboratory, four glues were evaluated for attaching harmonic radar tags securely to nymphs, and survivorship with attached tags was measured. There were no significant differences in survivorship or vertical and horizontal movement among nymphs with tags affixed with the glue treatments compared with the untagged control. Based on numerically greater survivorship of nymphs with tags affixed with Loctite glass glue, a field validation study of tagged nymphs released in host (apple tree) and non‐host (mowed grass) with or without H. halys pheromonal stimuli present revealed that nymphs could be successfully relocated using harmonic radar after 48 h. Among treatments, 83% of nymphs remained in baited and unbaited apple trees, 50% of nymphs remained in baited mowed grass plots, and in unbaited mowed grass plots, 17% of fifth instars, and 0% of fourth instars were retained. The absence of negative effects on mobility, survivorship, and field tracking validates that harmonic radar can be used to study dispersal ecology of nymphal H. halys. [ABSTRACT FROM AUTHOR]

This research quantifies the spatiotemporal statistics of composite radar reflectivity in the vicinity of severe thunderstorm reports. By using over 20 years (1996–2017) of data and 500,000 severe thunderstorm reports, this study presents the most comprehensive analysis of the mesoscale presentation of radar reflectivity composites during severe weather events to date. We first present probability matched mean composites of approximately 5,000 radar images centred on tornado reports that contain one of three types of manually‐labelled convective storm modes—namely, (a) quasi‐linear convective system (QLCS); (b) cellular; or (c) tropical system. Next, we generate composites for tornado report data stratified by EF‐scale and for four temporal periods during which notable severe weather events took place. The data are then stratified by hazard, region, season, and time of day. The results show marked spatiotemporal and intra‐hazard variability in radar presentation. In general, cellular convection is favoured in the Great Plains of the United States, whereas QLCS convection is favoured in the Southeast United States. Night and cool‐season subsets showed a preference for QLCS convection, whereas day and warm‐season subsets showed a preference for cellular convection. These results agree well with the existing literature and suggest that the data extraction and organization approach is sound. Because of this, these data will be useful for future image classification studies in climate and atmospheric sciences—particularly those involving storm mode classification. [ABSTRACT FROM AUTHOR]

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) dataset blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS dataset uses the WSR-88D archive starting in 1998–2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1 km × 1 km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe-storm-related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5 km × 0.5 km. The initial period of record for the dataset is 1998–2011. The dataset underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall dataset, but which caused large data errors in some cases. This dataset has applications toward radar-based climatologies, postevent analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented. [ABSTRACT FROM AUTHOR]

Monitoring small, mobile organisms is crucial for science and conservation, but is technically challenging. Migratory birds are prime examples, often undertaking nocturnal movements of thousands of kilometres over inaccessible and inhospitable geography. Acoustic technology could facilitate widespread monitoring of nocturnal bird migration with minimal human effort. Acoustics complements existing monitoring methods by providing information about individual behaviour and species identities, something generally not possible with tools such as radar. However, the need for expert humans to review audio and identify vocalizations is a challenge to application and development of acoustic technologies.Here, we describe an automated acoustic monitoring pipeline that combines acoustic sensors with machine listening software (BirdVoxDetect). We monitor 4 months of autumn migration in the northeastern United States with five acoustic sensors, extracting nightly estimates of nocturnal calling activity of 14 migratory species with distinctive flight calls. We examine the ability of acoustics to inform two important facets of bird migration: (1) the quantity of migrating birds aloft and (2) the migration timing of individual species. We validate these data with contemporaneous observations from Doppler radars and a large community of citizen scientists, from which we derive independent measures of migration passage and timing.Together, acoustic and weather data produced accurate estimates of the number of actively migrating birds detected with radar. A model combining acoustic data, weather and seasonal timing explained 75% of variation in radar‐derived migration intensity. This model outperformed models that lacked acoustic data. Including acoustics in the model decreased prediction error by 33%. A model with only acoustic information outperformed a model comprising weather and date (57% vs. 48% variation explained, respectively).Acoustics also successfully measured migration phenology: species‐specific timing estimated by acoustic sensors explained 71% of variation in timing derived from citizen science observations.Synthesis and applications. Our results demonstrate that cost‐effective acoustic sensors can monitor bird migration at species resolution at the landscape scale and should be an integral part of management toolkits. Acoustic monitoring presents distinct advantages over radar and human observation, especially in inaccessible and inhospitable locations, and requires significantly less expense. Managers should consider using acoustic tools for monitoring avian movements and identifying and understanding dangerous situations for birds. These recommendations apply to a variety of conservation and policy applications, including mitigating the impacts of light pollution, siting energy infrastructure (e.g. wind turbines) and reducing collisions with structures and aircraft. [ABSTRACT FROM AUTHOR]

Large-span, post-tensioned (PT) beams play a crucial role in maximizing the benefits of post-tensioning techniques. Bonded and unbonded systems are prevalent, with the latter being more widespread in the United States. While bonded systems are advantageous for creating long spans when multiple tendons are grouped in ducts, limited studies in the literature exist on their demolition. With a case study, this paper addresses the unique challenge of demolishing large-span-bonded, post-tensioned beams that occurs due to a building's functional change. Emphasizing insights for engineers, it explores the use of cutting and dismantling methods, thereby considering the presence of prestressed cables. The demolition process is distinctive due to the presence of numerous prestressed cables along the beams, necessitating a specialized and cautious cutting approach. This is accomplished through the use of a drilling technique that selectively distresses the tendons, ensuring they are not all affected simultaneously. An intriguing observation discussed in this paper pertains to the occurrence of horizontal cracks accompanied by loud sounds following the drilling process, thereby offering insights from the design perspective of PT systems. This paper details an innovative method for safely demolishing large-span, bonded PT beams using ground-penetrating radar and computer models to navigate structural complexities and ensure nearby structures' safety. [ABSTRACT FROM AUTHOR]

Accurate and timely inland waterbody extent and location data are foundational information to support a variety of hydrological applications and water resources management. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a promising tool for delineating inland water due to distinct surface reflectivity characteristics over dry versus wet land which are observable by CYGNSS's eight microsatellites with passive bistatic radars that acquire reflected L-band signals from the Global Positioning System (GPS) (i.e., signals of opportunity). This study conducts a baseline 1-km comparison of water masks for the contiguous United States between latitudes of 24°N-37°N for 2019 using three Earth observation systems: CYGNSS (i.e., our baseline water mask data), the Moderate Resolution Imaging Spectroradiometer (MODIS) (i.e., land water mask data), and the Landsat Global Surface Water product (i.e., Pekel data). Spatial performance of the 1-km comparison water mask was assessed using confusion matrix statistics and optical high-resolution commercial satellite imagery. When a mosaic of binary thresholds for 8 sub-basins for CYGNSS data were employed, confusion matrix statistics were improved such as up to a 34% increase in F1-score. Further, a performance metric of ratio of inland water to catchment area showed that inland water area estimates from CYGNSS, MODIS, and Landsat were within 2.3% of each other regardless of the sub-basin observed. Overall, this study provides valuable insight into the spatial similarities and discrepancies of inland water masks derived from optical (visible) versus radar (Global Navigation Satellite System Reflectometry, GNSS-R) based satellite Earth observations. [ABSTRACT FROM AUTHOR]

As of July 2020, the United States’ Identification Friend or Foe Identifier (IFF) stopped operating the 40-year-old secrecy algorithm Mode-4. Following its termination, Mode 5, which provides improved security performance, jamming response, and GPS location information, has been applied. Herein, we present the function design of the IFF system and the procedures, contents, and test results related to the AIMS PO (Air traffic control radar Beacon system, identification friend or foe, Mark XII/XIIA, systems program office) certification test for the IFF system within the CSR (coastal surveillance radar)-II system. The AIMS certification test, including the stability and interface verification of the CSR-II system with a Mode 5 function, was performed, and the certification letter was received from the US AIMS PO. We present the procedure and practical knowledge of the CSR-II mode 5 platform certification test for reducing business risk when developing a weapon system using Mode 5 and present an excellent test case for platform certification acquisition. [ABSTRACT FROM AUTHOR]

Solar monitoring is a method in which solar interferences, recorded during operational scanning of a radar, are used to monitor antenna pointing, identify signal processor issues, track receiver chain stability, and check the balance between horizontal and vertical polarization receive channels. The method is used by EUMETNET to monitor more than 100 radars in 20 European countries and it has been adopted by many national weather services across the world. NEXRAD is a network of 160 similar S-band weather radars (WSR-88Ds), which makes it most suitable for assessing the capability of the solar monitoring method on a continental scale. The NEXRAD level-II data contain radial-by-radial noise power estimates. An increase in this estimate is observed when the antenna points close to the sun. Our decoding software extracts these noise power estimates for the horizontal and vertical receive channels (converted to solar flux units) and other relevant metadata, including azimuth, elevation, observation time, and radar location. Here we present results of analyzing one year of solar monitoring data generated by 142 radars from the contiguous United States. We show monitoring results, geographical maps, and statistical outcomes on antenna pointing, solar fluxes, and differential reflectivity biases. We also assess the quality of the radars by defining a figure of merit, which is calculated from the solar monitoring results. The results demonstrate that the solar method provides great benefit for routine monitoring and harmonization of national and transnational operational radar networks. [ABSTRACT FROM AUTHOR]

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography. [ABSTRACT FROM PUBLISHER]

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]

The article discusses the concept of radar which can be used for the detection of distant objects invisible to the human eye. Topics discussed include shortening of the phrase Radio Detection and Ranging into radar by the U.S. Navy in 1940, the use of the telemobiloscope, predecessor of radar, for detecting ships in the fog, and the further development of radar during the World War II.

The northeast U.S. extratropical cyclone of 8-9 February 2013 produced blizzard conditions and more than 0.6-0.9 m (2-3 ft) of snow from Long Island through eastern New England. A surprising aspect of this blizzard was the development and rapid weakening of a snowband to the northwest of the cyclone center with radar ref lectivity factor exceeding 55 dB Z. Because the radar reflectivity within snowbands in winter storms rarely exceeds 40 dB Z, this event warranted further investigation. The high radar reflectivity was due to mixed-phase microphysics in the snowband, characterized by high differential reflectivity ( ZDR > 2 dB) and low correlation coefficient (CC < 0.9), as measured by the operational dual-polarization radar in Upton, New York (KOKX). Consistent with these radar observations, heavy snow and ice pellets (both sleet and graupel) were observed. Later, as the reflectivity decreased to less than 40 dB Z, surface observations indicated a transition to primarily high-intensity dry snow, consistent with lower-tropospheric cold advection. Therefore, the rapid decrease of the 50+ dB Z reflectivity resulted from the transition from higher-density, mixed-phase precipitation to lower-density, dry-snow crystals and aggregates. This case study indicates the value that dual-polarization radar can have in an operational forecast environment for determining the variability of frozen precipitation (e.g., ice pellets, dry snow aggregates) on relatively small spatial scales. [ABSTRACT FROM AUTHOR]

The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR during April-September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern United States during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE. HRRR tends to overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds and large objects at high-reflectivity thresholds. HRRR tends to either underforecast objects in the southern and central plains or has a correct frequency bias there, whereas it overforecasts objects across the southern and eastern United States. Attribute comparisons reveal the inability of the HRRR to fully resolve convective-scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts. Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke skill score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length. SIGNIFICANCE STATEMENT: Improving weather forecast models requires determining where the model does well and where it does not. Gridpoint-based methods for assessing model forecasts have known shortfalls when applied to high-resolution models that can forecast individual thunderstorms. We present an object-based verification procedure that focuses on identifying actual meteorological features such as thunderstorms instead of gridpoint-by-gridpoint comparison between forecasts and verifying truth. This article reveals some of the information ascertained from this assessment and illustrates the enhancement of information obtained from object-based verification to gridpoint-based assessment. [ABSTRACT FROM AUTHOR]

Since the legalization of homeschooling in 1972, litigation by homeschooling adversaries has shaped the legal frameworks regulating its practice. Adjudicating between complementary theoretical claims, we examine whether homeschooling litigation trends-specifically the ability of either side to set the agenda by getting on court dockets-reflect concerns over contentious educational practices and policies or broader cultural and political dynamics. We employ a unique longitudinal dataset on precedent-setting state and federal cases (1972-2007). Analyses show that litigation trends remained largely unaffected by changing educational dynamics, except via racialized status competition dynamics. Instead, litigation trends primarily reflect political and cultural factors, including the liberalization of public attitudes on socially contentious issues. Most importantly, mobilization and countermobilization dynamics have driven the ability of homeschooling rivals to use the courts for agenda-setting purposes. Our results highlight how political and cultural contexts shape movements' ability to set policy agendas through the courts. [ABSTRACT FROM AUTHOR]

Abstract In this paired study, we apply a recently developed high-resolution bathymetry estimation algorithm ("cBathy") to X-band marine radar observations at two nearshore field sites. The algorithm exploits observations of the spatial structure of wave phase to attain wavenumber estimates, inverts the linear water wave dispersion relation for depth, and then applies a Kalman filter to objectively update the bathymetry estimates. Previously, performance has only been tested using optical video observations. In this first of two papers, performance of the algorithm using X-band radar image time series is tested at two disparate barred beach environments: Duck, NC, USA, and Benson Beach, WA, USA. Each of the test beaches is either co-located with (Duck, NC) or geographically close to (Benson Beach, WA) those utilized in the original algorithm verification. Concurrent echosounder surveys are used as ground truth. The bulk performance of the radar-derived bathymetry estimate at Duck, NC, achieves 0.49 m root-mean-square error (RMSE) with 0.26 m bias deep. This compares well with the bulk performance of the concurrent estimate derived using optical video (0.44 m RMSE and 0.23 m bias deep). The radar-derived bathymetry estimate performance at Benson Beach, is similar (0.35 m RMSE and 0.11 m bias shallow), and is comparable to that of an optical video derived estimate at a similar Pacific Northwest beach (0.56 m RMSE and 0.41 m bias shallow). At both beaches, significantly higher performance is achieved at locations deeper than 2 m (offshore of the surfzone) than at locations shallower than 2 m (surfzone), where errors are often a large fraction of the total depth. Lastly, several weeks of observations are utilized to assess the sensitivity of algorithm quality control to environmental conditions. Thresholds based on the shoreward component of wind stress and offshore wave steepness are identified and shown to impact the areal coverage of radar-derived bathymetric estimates. Overall, these results demonstrate the viability of marine radar observations as input to the cBathy algorithm and delineate some environmental constraints on algorithm performance. In the companion paper, the algorithm is extended to areas where tidal currents are important, including an ebb tidal shoal and an estuary mouth. Highlights • Depth estimation algorithm, cBathy, is applied to X-band radar image time series. • Performance is assessed at two beaches (North Carolina and Washington, USA). • Accuracy via radar is comparable to video-based performance (reported previously). • Frequency of updates increases with wave height and when wind blows toward shore. [ABSTRACT FROM AUTHOR]

Straight-line winds are arguably the most challenging element considered by operational forecasters when issuing severe thunderstorm warnings. Determining the potential maximum surface wind gust prior to an observed, measured gust is very difficult. This work builds upon prior research that quantified a relationship between the observed outflow boundary speed and corresponding measured wind gusts. Whereas this prior study was limited to a 30-case dataset over eastern Colorado, the current study comprises 943 cases across the contiguous United States and encompasses all times of day, seasons, and regions while representing various convective modes and associated near-storm environments. The wind gust ratios (WGRs), or the ratio between a measured wind gust and the associated outflow boundary speed, had a nationwide median of 1.44, mean of 1.68, 25th percentile of 1.19, and 75th percentile of 1.91. WGRs varied considerably by region, season, time of day, convective mode, near-storm environment, and outflow boundary speed. WGRs tended to be higher in the plains, Intermountain West, and southern coastal regions, lower in the cool season and during the morning and overnight, and lower in linear convective modes relative to supercell and disorganized modes. Environments with stronger mean winds and low- to midlevel shear vector magnitudes tended to have lower WGRs, whereas those with steeper low-level lapse rates and other thermodynamic characteristics favorable for momentum transfer and evaporative cooling tended to have higher WGRs. As outflow boundary speed increases, WGRs—and their variability—decrease. Applying these findings may help operational meteorologists to provide more accurate severe thunderstorm warnings. Significance Statement: Forecasting thunderstorm peak wind gusts is a challenge for operational forecasters, especially in comparison with forecasting hail. This study improves upon a method from 1988 to forecast these wind gusts, which uses the ratio of peak thunderstorm wind gust divided by thunderstorm speed. Herein, we expanded the sample from 30 to 943 cases and from eastern Colorado to across the contiguous United States, with a focus on airports where winds are especially impactful to aviation. We found considerable variability in typical ratios of wind gusts to thunderstorm motion speed based upon the time of day, season, region, and mode and environment of the thunderstorms, with an average ratio of 1.68. The results herein may help to improve the accuracy of severe thunderstorm warnings, allowing the public to more readily prepare for the associated hazards. [ABSTRACT FROM AUTHOR]

The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next generation radar (NEXRAD) network over the U.S. to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014-2016) of EAMv1 simulations of 3D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central United States, which suggests that the model does not capture the mesoscale convective systems that produce much of precipitation in that region. The shape of the model estimated histogram of subgrid scale reflectivity is improved by correcting the microphysical assumptions in COSP. The model severely underestimates radar reflectivity at upper levels - the simulated echo top height is about 4 km lower than in observations - and this result is not changed by tuning any single physics parameter. [ABSTRACT FROM AUTHOR]

Dust is an important and complex constituent of the atmospheric system, having significant impacts on the environment, climate, air quality, and human health. Although dust events are common across many regions of the United States, their impacts are not often prioritized in air quality mitigation strategies. We argue that there are at least three factors that result in underestimation of the social and environmental impact of dust events, making them receive less attention. These include (a) sparse monitoring stations with irregular spatial distribution in dust‐influenced regions, (b) inconsistency with dust sampling methods, and (c) sampling frequency and schedules, which can lead to missed dust events or underestimation of dust particle concentrations. Without addressing these three factors, it is challenging to characterize and understand the full air quality impacts of dust events in the United States. This paper highlights the need for additional monitoring to measure these events so that we can more fully evaluate and understand their impacts, as they are predicted to increase with climate change. Plain Language Summary: Dust is an important and complex component of the atmospheric system, having significant impacts on the environment, climate, air quality, and human health. Yet, dust events are underestimated and therefore do not receive the level of attention necessary to fully understand their impacts. This is due in part to the fact that monitoring stations are sparse in dust‐influenced regions; there is a lack of consistency in dust definitions and sampling methods, and there is a lack of continuous monitoring, which results in missed dust events or underestimations of their particle concentration. This commentary highlights the need for additional monitoring designed to measure dust so that we can more fully understand its impacts, which are particularly important as dust is expected to increase with climate change. Key Points: Different factors result in an underestimation of the social and environmental impact of dust events, making them receive less attentionThere is a lack of consistency in dust definitions and sampling methodsSampling frequency and schedules of dust events can lead to missed dust events or underestimation of dust particle concentrations [ABSTRACT FROM AUTHOR]

• Precipitation annual maxima estimated from NEXRAD are evaluated over the CONUS. • Accuracy of radar-based estimates varies with magnitude and duration. • Uncertainty in radar-based IDF curves exhibits climate dependence. The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002–2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-h) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24 h). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes. [ABSTRACT FROM AUTHOR]

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No-precipitation reflectivity observations from Doppler radar contain valuable information on areas lacking precipitation; however, they are often ignored in four-dimensional variational (4DVar) radar data assimilation (DA). This study incorporated a neighborhood-based scheme to assimilate no-precipitation observations as a mechanism to suppress spurious convection. The impact of the scheme on convective forecasting using 4DVar was evaluated by comparing the performance of experiments with and without assimilation (ExpCTL) using eight diverse storm cases that occurred over the central United States during summer 2016. Three no-precipitation assimilation experiments with different neighborhood radiuses of 10 (ExpR10), 30 (ExpR30), and 50 km (ExpR50) were conducted to examine the sensitivity of the scheme to neighborhood size. Results indicated that all the no-precipitation assimilation experiments significantly improved quantitative precipitation forecast skill with large reduction of the bias and the false alarm ratio from ExpCTL, as well as improving representation of the intensity and coverage of the precipitation. The horizontal wind, temperature, and water vapor were also improved, especially the latter. The scheme was found sensitive to neighborhood size and greater benefit was found in ExpR30 in comparison with ExpR10 and ExpR50. Analysis revealed that ExpR30 reduced low-level cooling and mid-level warming corresponding to decreased water vapor in areas of overpredicted and false precipitation, and it was more effective in conserving the total water content balance during cycled radar DA. The findings of this study could provide reference information for assimilation of no-precipitation observations into the Weather Research and Forecasting model using 4DVar, which would be valuable for severe weather prediction. • A neighborhood-based scheme was incorporated into 4DVar to assimilate no-precipitation observations from Doppler Radar. • The scheme significantly reduced spurious convection, and a moderate neighborhood size resulted in the best results. • The no-precipitation assimilation helps conserve balance of water content, which led to improved precipitation forecast. [ABSTRACT FROM AUTHOR]

Drawing on in‐depth interviews with 50 Latin American immigrants in Dallas, Texas, this article uncovers systematic distinctions in how immigrants holding different legal statuses perceive the threat of deportation. Undocumented immigrants recognize the precarity of their legal status, but they sometimes feel that their existence off the radar of the US immigration regime promotes their long‐term presence in the country. Meanwhile, documented immigrants perceive stability in their legal status, but they sometimes view their existence on the radar of the US immigration regime as disadvantageous to their long‐term presence in the country. The article offers the concept of system embeddedness—individuals' perceived legibility to institutions that maintain formal records—as a mechanism through which perceived visibility to the US immigration regime entails feelings of risk, and perceived invisibility feelings of safety. In this way, the punitive character of the US immigration regime can overwhelm its integrative functions, chilling immigrants out of opportunities for material and social well‐being through legalization and legal status. More broadly, system embeddedness illuminates how perceived visibility to a record‐keeping body that combines punitive and integrative goals represents a mechanism of legal stratification for subordinated populations—even absent prior punitive experiences with other social control institutions that might otherwise be thought to trigger their system avoidance. [ABSTRACT FROM AUTHOR]

Motor vehicle crashes remain a leading cause of accidental death in the United States, and weather is frequently cited as a contributing factor in fatal crashes. Previous studies have investigated the link between these crashes and precipitation typically using station-based observations that, while providing a good estimate of the prevailing conditions on a given day or hour, often fail to capture the conditions present at the actual time and location of a crash. Using a multiyear, high-resolution radar reanalysis and information on 125,012 fatal crashes spanning the entire continental United States over a 6-yr period, we find that the overall risk of a fatal crash increases by approximately 34% during active precipitation. The risk is significant in all regions of the continental United States, and it is highest during the morning rush hour and during the winter months. [ABSTRACT FROM AUTHOR]

Background: Pulmonary vein isolation is insufficient to treat all patients with persistent atrial fibrillation (AF), and effective adjunctive ablation strategies are needed. Ablation of AF drivers holds promise, but current technologies to identify drivers are limited by spatial resolution. In a single-arm, first-in-human, investigator-initiated Food and Drug Administration Investigational Device Exemption study, we used a novel system for real-time, high-resolution identification of AF drivers in persistent AF., Methods: Patients with persistent or long-standing persistent AF underwent ablation using the RADAR (Real-Time Electrogram Analysis for Drivers of Atrial Fibrillation) system in conjunction with a standard electroanatomical mapping system. After pulmonary vein isolation, electrogram and spatial information was streamed to the RADAR system and analyzed to identify driver domains to target for ablation., Results: Across 4 centers, 64 subjects were enrolled: 73% male, age, 64.7±9.5 years; body mass index, 31.7±6.0 kg/m 2 ; left atrium size, 54±10 mm, with persistent/long-standing persistent AF in 53 (83%)/11 (17%), prior AF ablation (re-do group) in 26 (41%). After 12.6±0.8 months follow-up, 68% remained AF-free off all antiarrhythmics; 74% remained AF-free and 66% remained AF/atrial tachycardia/atrial flutter-free on or off AADs (antiarrhythmic drugs). AF terminated with ablation in 35 patients (55%) overall and in 23/38 (61%) of de novo ablation patients. For patients with AF termination during ablation, 82% remained AF-free and 74% AF/atrial tachycardia/atrial flutter-free during follow-up on or off AADs. Patients undergoing first-time ablation generally had higher rates of freedom from AF than the re-do group., Conclusions: This novel technology for panoramic mapping of AF drivers showed promising results in a persistent/long-standing persistent AF population. These data provide the scientific basis for a randomized trial., Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT03263702; IDE#G170049.

In a prior study, GOES-R Geostationary Lightning Mapper (GLM) flash extent density (FED) data were assimilated using ensemble Kalman filter into a convection-allowing model for a mesoscale convective system (MCS) and a supercell storm. The FED observation operator based on a linear relation with column graupel mass was tuned by multiplying a factor to avoid large FED forecast bias. In this study, new observation operators are developed by fitting a third-order polynomial to GLM FED observations and the corresponding FED forecasts of graupel mass of the MCS and/or supercell cases. The new operators are used to assimilate the FED data for both cases, in three sets of experiments called MCSFit, SupercellFit, and CombinedFit, and their performances are compared with the prior results using the linear operator and with a reference simulation assimilating no FED data. The new nonlinear operators reduce the frequency biases (root-mean-square innovations) in the 0–4-h forecasts of the FED (radar reflectivity) relative to the results using the linear operator for both storm cases. The operator obtained by fitting data from the same case performs slightly better than fitting to data from the other case, while the operator obtained by fitting forecasts of both cases produce intermediate but still very similar results, and the latter is considered more general. In practice, a more general operator can be developed by fitting data from more cases. Significance Statement: Prior studies found that assimilation of satellite lightning observation can benefit storm forecasts for up to 4 h. A linear lightning observation operator originally developed for assimilating pseudo-satellite lightning observations was tuned earlier through sensitivity experiments when assimilating real lightning data. However, the linear relation does not fit the model and observational data well and significant bias can exist. This study develops new lightning observation operators by fitting a high-order polynomial to satellite lightning observations and model-predicted quantities that directly relate to lightning. The new operator was found to reduce the frequency biases and root-mean-square innovations for lightning and radar reflectivity forecasts, respectively, up to several hours relative to the linear operator. The methodology can be applied to larger data samples to obtain a more general operator for use in operational data assimilation systems. [ABSTRACT FROM AUTHOR]

Data gathered by electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) were used to produce a three-dimensional image of subsurface soft-sediment deformation structures developed on the modern Lake Powell delta at Hite, Utah. ERT and GPR lines were run orthogonal across the crater. ERT resolved a low-resistivity layer up to 2 m thick in the area near the vents within the crater. This low-resistivity layer thins toward the margins representing clays ejected from the vents. Below and adjacent to this layer is a high-resistivity layer that reflects delta top and pro-delta sands. The deepest zone recognized in the ERT profiles consists of a low-resistivity layer, clay deposits that accumulated during the maximum lake high stand. This clay zone is connected to the vent within the crater by a conduit that changes diameter vertically. GPR profiles recognized the presence of collapse features restricted to the proximity of the vent. The geometry of the model is consistent with those proposed for marine pockmarks that can be generated seismically or aseismically with the exception of subaerial exposure after the dome stage development. [ABSTRACT FROM AUTHOR]

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models. [ABSTRACT FROM AUTHOR]

Documentation of international trade in non-CITES species is generally poor. As a result, illegal trade in nationally-protected non-CITES species is often hard to detect. To show that such illicit trade nevertheless occurs, this study has analysed import and export records of the Giant Blue-tongued Skink Tiliqua gigas for the European Union (EU) and the United States (US) between 1999 and 2015. EU trade data for T. gigas was obtained from the UNEP-WCMCM CITES Trade Database. Trade data for the US was extracted from the US Fish and Wildlife Service (USFWS) Law Enforcement Management Information System (LEMIS). We observed a sudden shift in source codes (from wild-caught to captive-bred) coinciding with legislative changes, which has been indicative of laundering practices in the past. In the US, no less than 45% of all imported T. gigas was declared as wild-caught, constituting a direct violation of Indonesia's national laws and the US Lacey Act. The keeping of trade records is subjected to the willingness of individual countries, yet unwillingness to collect such data can severely threaten species when over-exploitation or laundering practices remain undetected. A CITES Appendix III-listing, obligating participating countries to maintain trade records, would facilitate improved detection and monitoring of illegal trade in nationally-protected species. [ABSTRACT FROM AUTHOR]