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Publicly accessible weather radar data have significant capabilities for meteorological measurements and predictions and, further, have the potential to measure nonmeteorological events that include smoke, ash, and debris plumes as well as explosions. The ability to identify and track nonmeteorological events can be of assistance in emergency response, hazard mitigation, and related activities in locations where radar coverage both exists and is recorded and accessible to the user. In this study, events from multiple locations in the United States that are reported in news outlets are assessed using a manual inspection process of Level 2 weather radar data to identify anthropogenic and nonbiological returns. Explosive events are also identified, and a large high-altitude debris cloud from the intentional destruction of the SpaceX Starship is tracked across a wide area. Finally, future efforts using a machine learning model are discussed as a means of automating the process and potentially enabling near-real-time nonmeteorological event identification in the same areas where the data are accessible. Using weather radar data can be a valuable new tool for Department of Defense systems to aid in military awareness, and for interagency emergency response and forensic mission experts to consider national weather service data in their mission profiles. Radar data can be effective in detecting several common types of emergencies and inform and aid response personnel.

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]

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 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]

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]

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]

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]

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.

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]

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]

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]

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 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]

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]

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]

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]

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]

When university academics implement changes in learning, such as introducing blended learning, it is conventional practice to examine and evaluate the impact of the resulting curriculum reform. Judging the worth and impact of an educational development is a complex task involving subtle differences in learning. Qualitative methods to explore these deep processes in learning include using interviews, observations and open-ended questionnaires targeting all stakeholders in the process, such as students, teachers, administration and technical staff. These evaluation tasks generate a mass of raw data that many faculty members in higher education are unaccustomed to analyzing. This article provides a framework using radar charts adapted from the field of organizational development. A modified six-zone radar chart was used to assess the extent of blended learning in order to compare changes in the learning environment. Data collection included interviews, classroom observations and electronic records generated during educational delivery over a 1-year period of time. A purposeful sample of online course data was collected by three participating universities in South Africa, Australia, and the United States. (Contains 2 figures and 2 tables.)

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]

Radiation protection recommendations advanced by the International Commission on Radiological Protection and National Council on Radiation Protection and Measurements, and many times adopted into regulations by the United States Nuclear Regulatory Commission, need to be based on scientifically justified assumptions and conclusions. The linear no-threshold model assigns risk to every radiation exposure above zero dose and is the current basis for setting radiation protection standards worldwide. This hypothesis is vigorously challenged by many individuals but just as vigorously defended in spite of the uncertainties surrounding health effects at low dose levels. It is clear that at radiation doses below 100 mSv, the effects, if any, are so low as to be unobservable and perhaps, therefore, unknowable. However, the linear no-threshold hypothesis is used routinely to formulate regulatory dose limits for workers and the general public and to derive stochastic radiogenic risk estimates at low doses. This note will show that while the linear no-threshold hypothesis may play a legitimate role in setting radiation protection standards and operating policies, such as establishing dose limits or as part of an operational "as low as is reasonably achievable" (ALARA) policy, it is inappropriate for use in estimating possible cancer risks associated with low-level radiation exposures. It will also demonstrate that the raising, not lowering, of current regulatory dose limits is more solidly supported by the actual observed data on radiation dose and effects. The authors submit that the misuse of the linear no-threshold model for predicting radiation effects in exposed individuals and populations should be discontinued.

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]

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.

As part of our continental defense system, the United States Air Force has operated a radar system, known generally by the label PAVE PAWS, off of Cape Cod, MA since 1978. Some populated areas in the vicinity of the system are subject to a low level of background radiofrequency radiation from the system, and local citizens' groups have expressed concern that this radiofrequency radiation may affect their health. These concerns have been fueled by presentations and letters by Dr. R. A. Albanese, an applied mathematician at the Air Force Research Laboratory, who has proposed standards by which that PAVE PAWS radiofrequency radiation which is incident on populations should be judged. I discuss those standards that are sufficiently well defined to be subject to analysis and show that they are not based on sound quantitative reasoning.

High-frequency (HF) surface wave radar has been identified to be a gap-filling technology for Maritime Domain Awareness. Present SeaSonde HF radars have been designed to map surface currents but are able to track surface vessels in a dual-use mode. Rutgers and CODAR Ocean Sensors, Ltd., have collaborated on the development of vessel detection and tracking capabilities from compact HF radars, demonstrating that ships can be detected and tracked by multistatic HF radar in a multiship environment while simultaneously mapping ocean currents. Furthermore, the same vessel is seen simultaneously by the radar based on different processing parameters, mitigating the need to preselect a fixed set and thereby improving detection performance. [ABSTRACT FROM AUTHOR]

A specific example of radar navigation versus the new cooperative surveillance system, known as Automatic Dependent Surveillance-Broadcast (ADS-B), illustrates the shift in air transportation from the current National Airspace System (NAS) to the Next Generation Air Transportation System (NextGen). Drawing from system-of-systems concepts provides insights into exploring the shift. An agent-based model simulates this specific transition from radar navigation to ADS-B navigation and the benefits thereof, specifically in terms of fuel efficiency, as one of many benefits due to NextGen. The results of the simulation indicate a significant fuel savings when ADS-B is used. Furthermore, the modeling approach appears promising for other studies of NextGen. [ABSTRACT FROM AUTHOR]

There is now a large installed base of high-frequency (HF) coastal ocean radars in the United States able to measure surface currents on an operational basis. However, these instruments also have the potential to provide estimates of the spatially variable surface wind field over distances ranging from 10 to 200 km offshore. This study investigates the ability of direction-finding HF radars to recover spatial maps of wind speed and direction from the dominant first-order region radar returns using empirical models. Observations of radar backscatter from the Martha's Vineyard Coastal Observatory HF radar system were compared to wind observations from an offshore tower, finding significant correlations between wind speed and the backscatter power for a range of angles between the wind and radar loop directions. Models for the directional spreading of wind waves were analyzed in comparison to data-based results, finding potentially significant differences between the model and data-based spreading relationships. Using empirical fits, radar-based estimates of wind speed and direction at the location of the in situ wind sensor had error rates of 2 m/s and 60°, which decreased with hourly averaging. Attempts to extrapolate the results to the larger domain illustrated that spatially dependent transfer functions for wind speed and direction appear possible for large coastal ocean domains based on a small number of temporary, or potentially mobile, in situ wind sensors. [ABSTRACT FROM AUTHOR]

Airborne cloud radar and cloud microphysical data, collected in summertime stratus clouds off the Oregon coast, are analyzed to obtain an empirical threshold radar reflectivity above which drizzle can be expected in warm marine stratus. Such clouds, which are prevalent over the eastern subtropical oceans, have a profound impact on global climate.This study finds that the threshold reflectivity for drizzle is a strong function of height within the cloud layer. Both radar reflectivities and calculated reflectivities (based on measured droplet spectra) indicate that the threshold is most crisply defined in the lower half of the stratus cloud deck. The use of the threshold reflectivity profile to flag drizzle is illustrated by examining the dependency of radar reflectivity on cloud water content. Such dependency is non-existent in marine stratus in general, but a clear relationship emerges when drizzle cases are excluded. [Copyright &y& Elsevier]

Quantitative real-time observations of a tsunami have been limited to deep-water, pressure-sensor observations of changes in the sea surface elevation and observations of sea level fluctuations at the coast, which are essentially point measurements. Constrained by these data, models have been used for predictions and warning of the arrival of a tsunami, but to date no system exists for local detection of an actual incoming wave with a significant warning capability. Networks of coastal high frequency (HF)-radars are now routinely observing surface currents in many countries. We report here on an empirical method for the detection of the initial arrival of a tsunami, and demonstrate its use with results from data measured by fourteen HF radar sites in Japan and USA following the magnitude 9.0 earthquake off Sendai, Japan, on 11 March 2011. The distance offshore at which the tsunami can be detected, and hence the warning time provided, depends on the bathymetry: the wider the shallow continental shelf, the greater this time. We compare arrival times at the radars with those measured by neighboring tide gauges. Arrival times measured by the radars preceded those at neighboring tide gauges by an average of 19 min (Japan) and 15 min (USA) The initial water-height increase due to the tsunami as measured by the tide gauges was moderate, ranging from 0.3 to 2 m. Thus it appears possible to detect even moderate tsunamis using this method. Larger tsunamis could obviously be detected further from the coast. We find that tsunami arrival within the radar coverage area can be announced 8 min (i.e., twice the radar spectral time resolution) after its first appearance. This can provide advance warning of the tsunami approach to the coastline locations. [ABSTRACT FROM AUTHOR]

The article focuses on the use of radar imaging in monitoring the oil spill in the Gulf of Mexico. It notes that radar being a remote sensing instrument has the capability to image the surface at night and below cloud cover. It mentions that the National Aeronautics and Space Administration (NASA) positioned the uninhabited aerial vehicle synthetic aperture radar (UAVSAR) to monitor the coastline for scientific studies.

A national high-frequency radar network has been created over the past 20 years or so that provides hourly 2-0 ocean surface current velocity fields in near real time from a few kilometers offshore out to approximately 200 km. This preoperational network is made up of more than 100 radars from 30 different institutions. The Integrated Ocean Observing System efforts have supported the standards-based ingest and delivery of these velocity fields to a number of applications such as coastal search and rescue, oil spill response, water quality monitoring, and safe and efficient marine navigation. Thus, regardless of the operating institution or location of the radar systems, emergency response managers, and other users, can rely on a common source and means of obtaining and using the data. Details of the history, the physics, and the application of high-frequency radar are discussed with successes of the integrated network highlighted. [ABSTRACT FROM AUTHOR]

Abstract: Forest canopy height is essential information for many forest management activities and is a critical parameter in models of ecosystem processes. Several methods are available to measure canopy height from single-tree to regional and global scales, but the methods vary widely in their sensitivities, leading to different height estimates even for identical stands. We compare four technologies for estimating canopy height in pine and hardwood forests of the Piedmont region of North Carolina, USA: (1) digital elevation data from the global Shuttle Radar Topography Mission (SRTM) C-band radar interferometry, (2) X- and P-band radar interferometry from the recently developed airborne Geographic Synthetic Aperture Radar (GeoSAR) sensor, (3) small footprint lidar measurements (in pine only), and (4) field measurements acquired by in situ forest mensuration. Differences between measurements were smaller in pine than in hardwood forests, with biases ranging from 5.13 to 12.17m in pine (1.60–13.77m for lidar) compared to 6.60–15.28m in hardwoods and RMSE from 8.40 to 14.21m in pine (4.73–14.92m for lidar) compared to 9.54–16.84 in hardwood. GeoSAR measurements of canopy height were among the most comparable measurements overall and showed potential for successful calibration, with R 2 =0.87 in pine canopies and R 2 =0.38 in hardwood canopies from simple linear regression. An improved calibration based on differential canopy penetration is presented and applied to SRTM measurements, resulting in canopy height estimates in pine forests with RMSE and standard error <4.00m. Each of the remotely sensed methods studied produces reasonable and consistent depictions of canopy height that can be compared with data of similar provenance, but due to differences in underlying sensitivities between the methods, comparisons between measurements from various sources require cross-calibration and will be most useful at broad scales. [Copyright &y& Elsevier]

Normal near surface radio-frequency (RF) propagation in the littorals across the land–sea boundary is rare due to the land–sea temperature difference, coastline shape, ground cover, urban density, coastal topography, and soil moisture content. The resulting frequent existence of coastal non-standard vertical profiles of refractivity and the resulting RF propagation has a profound impact on the performance of Navy ship-borne radars operating within 100 nm of the shore. In addition, these non-standard RF propagation conditions are spatio-temporally inhomogeneous. These spatial and time dependent propagation conditions and the resulting radar engineering implications cannot be revealed by a single vertical profile of refractivity taken near the ship borne radar. The results from single profile analysis techniques provide no spatiotemporal information and may lead to overly conservative radar design. Mesoscale numerical weather prediction (NWP) is a rapidly maturing technology with a strong operational Navy history that can provide a vertical profile of refractivity every 1 km in the battle space and every hour, up to 48 h, in the future. The Sensor Division at NSWCDD has applied mesoscale NWP for the last 2 years to better understand the performance of prototype radar in realistic four-dimensional (4D) coastal environments. Modern RF parabolic equation models are designed to model specific radar designs and to employ 3D refractivity fields from mesoscale NWP models. This allows for a radar design to be tested in realistic littoral non-standard atmospheres produced by stable internal boundary layers, sea breeze events, and the more rare sub-refractive events. Mesoscale NWP is currently qualitative for this purpose, but a research and development program focused on sea testing of prototype radars is described with the purpose of developing a more quantitative mesoscale NWP technology to support radar acquisition, testing, and operations. [ABSTRACT FROM AUTHOR]

The next generation radar suite will include a radar that has sensitivity far greater than shipboard radars in use today. Most likely the radar will be a multi-faced, solid state, phased array system with each array consisting of thousands of individual radiating elements powered by transmit/receive (T/R) modules. It will likely need to be large, and the demands it makes on ship services will be unprecedented. Its electrical power demands could be larger than the total ship's load for present-day ships. Array size could significantly impact topside design. This paper examines top-level radar system design choices and illustrates the trends in the ship impact of those choices. Radar design options considered are aperture size and shape, coolant operating temperature, number of array faces, and power system architecture. These design options have an effect on the radar system's weight, footprint, power demand, and cooling load. The effects on ship design can be significant. The ship impact analyses consider a radar system that includes ship-provided equipment such as electrical power distribution equipment and chill water plants. An analysis of radar systems, all with the same performance, shows that radar system power demand can change by as much as four megawatts for a notional surface combatant depending upon the radar designer's configuration choices. Radar system weight can vary by more than 100 metric tons. There is a cubic relationship between T/R module power and array face area for a constant sensitivity radar system. This relationship is examined to show that there is often a choice of T/R module power that minimizes radar ship impact. The exact choice of T/R module power depends upon the customer's preferences, the manufacturer's capabilities, and choice of vendors. This paper does not convey any official US government position or US Navy endorsement of any particular radar architecture or design approach. [ABSTRACT FROM AUTHOR]

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]

This article discusses the deficiencies of the European missile defense system proposed by U.S. President George W. Bush to protect the U.S. and Europe from missile threats from the Middle East, particularly Iran. The system would consist of a large X-band radar called the European mid-course radar deployed in the Czech Republic; a forward-based X-band radar would be deployed in an unnamed location near Iran; and a launch site with 10 interceptor missiles would be located in Poland. This system could easily be rendered obsolete through the use of jammers and countermeasures such as lightweight balloons.