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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]

The United States is releasing more individuals from prisons than ever before. However, returning citizens face numerous challenges transitioning back into community life and many communities remain ill-equipped in successfully supporting them. This study focuses on transportation as in integral component to the success of returning citizens. Rapid and rigorous qualitative data analysis (RADaR technique) is applied to interviews (n = 15) with individuals having returned to their community about the impact of transportation on their reentry. Analysis resulted in three primary themes; interviewees reinforced existing knowledge of the issues with transportation disadvantage, transportation is an extension of freedom, and transportation is directly connected to reentry success. These findings suggest a tiered structure of transportation freedom for returning citizens. This model of transportation will aid future researchers and policymakers in considering how to better resource returning citizens to maximize their success. [ABSTRACT FROM AUTHOR]

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor's channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems. Significance Statement: To understand how the accuracy of satellite precipitation depends on weather conditions, we compare the satellite estimates of precipitation against ground radars in the United States, using cloud regimes as a proxy for different recurring atmospheric systems. Consistent with previous studies, we found that errors in the satellite precipitation vary under different regimes. Satellite precipitation is, reassuringly, more accurate for storm systems that produce intense precipitation. However, in systems that produce weak or isolated precipitation, the errors are larger due to retrieval limitations. These findings highlight the important role of atmospheric states on the accuracy of satellite precipitation and the potential of cloud regimes for categorizing the global atmosphere. [ABSTRACT FROM AUTHOR]

Two years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts. Key Points: Several satellite‐based precipitation products are compared in relation with orographic enhancementSatellite‐based precipitation BIAS shows a seasonal patternPrecipitation BIAS is associated with the surface temperatures and total precipitable water [ABSTRACT FROM AUTHOR]

Near‐term ecological forecasting has the potential to mitigate negative impacts of human modifications on wildlife by directing efficient action through relevant and timely predictions. We used the U.S. avian migration system to highlight ecological forecasting applications for aeroconservation. We used millions of observations from 143 weather surveillance radars to construct and evaluate a migration forecasting system for nocturnal bird migration over the contiguous United States. We identified the number of nights of mitigation required to reduce the risk of aerial hazards to 50% of avian migrants passing a given area in spring and autumn based on dynamic forecasts of migration activity. We also investigated an alternative approach, that is, employing a fixed conservation strategy based on time windows that historically capture 50% of migratory passage. In practice, during both spring and autumn, dynamic forecasts required fewer action nights compared with fixed window selection at all locations (spring: mean of 7.3 more alert days; fall: mean of 12.8 more alert days). This pattern resulted in part from the pulsed nature of bird migration captured in the radar data, where the majority (54.3%) of birds move on 10% of a migration season's nights. Our results highlight the benefits of near‐term ecological forecasting and the potential advantages of dynamic mitigation strategies over static ones, especially in the face of increasing risks to migrating birds from light pollution, wind energy infrastructure, and collisions with structures. [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]

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]

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]

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]

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 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]

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]

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]

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]

Urban areas affect terrestrial ecological processes and local weather, but we know little about their effect on aerial ecological processes.Here, we identify urban from non‐urban areas based on the intensity of artificial light at night (ALAN) in the landscape, and, along with weather covariates, evaluate the effect of urbanization on flight altitudes of nocturnally migrating birds.Birds are attracted to ALAN; hence, we predicted that altitudes would be lower over urban than over non‐urban areas. However, other factors associated with urbanization may also affect flight altitudes. For example, surface temperature and terrain roughness are higher in urban areas, increasing air turbulence and height of the boundary layer, and affecting local winds.We used data from nine weather surveillance radars in the eastern United States to estimate altitudes at five quantiles of the vertical distribution of birds migrating at night over urban and non‐urban areas during five consecutive spring and autumn migration seasons. We fit Generalized Linear Mixed Models by season for each of the five quantiles of bird flight altitude and their differences between urban and non‐urban areas.After controlling for other environmental variables and contrary to our prediction, we found that birds generally fly higher over urban areas compared to rural areas in spring, and marginally higher at the mid‐layers of the vertical distribution in autumn. We also identified a small interaction effect between urbanization and crosswind speed, and between urbanization and surface air temperature, on flight altitudes. We also found that the difference in flight altitudes of nocturnally migrating birds between urban and non‐urban areas varied among radars and seasons, but was consistently higher over urban areas throughout the years sampled.Our results suggest that the effects of urbanization on wildlife extend into the aerosphere and are complex, stressing the need of understanding the influence of anthropogenic factors on airspace habitat. [ABSTRACT FROM AUTHOR]

Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote sensing networks in the United States have provided unprecedented spatial and temporal sampling to study such storms. These networks help forecasters subjectively identify storms capable of producing severe weather at the ground; however, uncertainties remain in how to objectively identify severe thunderstorms using the same data. Here, three large-area datasets (geostationary satellite, ground-based radar, and ground-based lightning detection) are used over 28 recent events in an attempt to objectively discriminate between severe and nonsevere storms, with an additional focus on severe storms that produce tornadoes. Among these datasets, radar observations, specifically those at mid- and upper levels (altitudes at and above 4 km), are shown to provide the greatest objective discrimination. Physical and kinematic storm characteristics from all analyzed datasets imply that significantly severe [≥2-in. (5.08 cm) hail and/or ≥65-kt (33.4 m s-1) straight-line winds] and tornadic storms have stronger upward motion and rotation than nonsevere and less severe storms. In addition, these metrics are greatest in tornadic storms during the time in which tornadoes occur. [ABSTRACT FROM AUTHOR]

Narrow regions of intense, banded snowfall present hazardous travel conditions due to rapid onset, high precipitation rates, and lowered visibility. Despite their importance, there are few verification studies of snowbands in operational forecast models. The objective of this study is to evaluate the ability of the High-Resolution Rapid Refresh (HRRR) model to predict snowbands in the United States east of the Rocky Mountains. An automated band-detection algorithm was applied to a 3-yr period of simulated and observed radar reflectivity to compare snowband climatologies. This algorithm uses the distributions of reflectivities in contiguous precipitation regions to determine a band intensity threshold. The predictability of snowbands on a case-by-case basis was also evaluated using an object-oriented approach. The distribution of HRRR forecast banding resembles that of the observations, but with a significant positive frequency bias. This may partially be due to underrepresentation of observed bands in our verification dataset due to limited radar coverage in portions of the central United States. On a case-by-case basis, traditional skill metrics indicate limited predictability, but allowing for small timing discrepancies dramatically improves scores. Object-oriented verification yields mixed results, with 30% of forecasts receiving a score indicative of a well-predicted event. However, 69% of cases have at least one forecast lead demonstrating skill, suggesting the HRRR is successful in depicting environments conducive to band formation. These results suggest adopting a probabilistic, ensemble approach, and indicate that the deterministic HRRR is best suited for the identification of regions of elevated snowband risk and not precise timing or location information. [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]

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]

• 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]

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]

Highlights • Evaluates utility of scatterometer data for estimation of urban built-up volume. • Dense Sampling Method-processed scatterometer data are compared to airborne lidar. • Analysis conducted for nine urban areas in the United States. • Strong, linear correlations observed between radar- and lidar-derived datasets. • Results promote three-dimensional approach to land use/land cover analyses. Abstract Accurately mapping urban infrastructure and extent is a high priority for resource management and service allocation as well as for addressing environmental, socioeconomic, and geopolitical concerns. Most available data products only document surficial (two-dimensional) land use and land cover (LULC), yet a substantial component of urban growth occurs in the vertical dimension. Light detection and ranging (lidar) data offer the potential for monitoring three-dimensional (3D) change, but the extreme lack of systematic lidar coverage worldwide inflicts considerable gaps in both spatial and temporal coverage. Satellite scatterometer (radar) data may serve as an alternative data source for characterizing urban growth and development in both the horizontal and vertical directions. The accuracy of these radar-based datasets for estimating building volumes remains to be validated quantitatively. For nine U.S. cities, we test whether scatterometer data can be used to estimate 3D urban built-up volume. We found strong, linear correlations between the lidar-derived and radar-derived building volume estimates for all cities with r2 values as high as 0.98 when using spatial trend analysis. Given the high expense that limits lidar data acquisition to small areas at sporadic points in time, satellite scatterometer data provide a breakthrough method for monitoring both vertical growth and horizontal expansion of cities across the world with a continuous decadal time scale. [ABSTRACT FROM AUTHOR]

The factors affecting the inland and orographic enhancement of lake-effect precipitation are poorly understood, yet critical for operational forecasting. Here we use nine cool seasons (16 November-15 April) of radar data from the Montague/Ft. Drum, New York (KTYX), WSR-88D, the North American Regional Reanalysis (NARR), and observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine variations in lake-effect precipitation enhancement east of Lake Ontario and over the Tug Hill Plateau (hereafter Tug Hill). Key factors affecting the inland and orographic enhancement in this region include the strength of the incident boundary layer flow, the intensity of the lake-induced convective available potential energy (LCAPE), and the mode of the lake-effect system. Stronger flow favors higher precipitation rates, a precipitation maximumdisplaced farther downwind, and greater inland and orographic enhancement. The effects of LCAPE depend upon the strength of the flow. During periods of weak flow, higher LCAPE favors lower precipitation rates, a maximum closer to the shoreline, and lesser inland and orographic enhancement. During periods of strong flow, higher LCAPE favors higher precipitation rates, a maximum displaced farther downwind, and greater inland and orographic enhancement. Banded (nonbanded) modes favor higher (lower) precipitation rates, lesser (greater) inland and orographic enhancement, and a maximum closer to the shoreline (over Tug Hill). These results, for both manually measured and radar-estimated precipitation, are robust when many lake-effect events are considered, but substantial variability exists during individual events. [ABSTRACT FROM AUTHOR]

The island of Kaua'i, Hawaii, USA, holds a large breeding populations of the endangered Hawaiian Petrel (Pterodroma sandwichensis) and a majority of the world population of the threatened Newell's Shearwater (Puffinus newelli). We evaluated island-wide population trends of both species. For Newell's Shearwaters, we considered radar counts at 13 sites between 1993 and 2013 and annual island-wide tallies of fledglings retrieved after being grounded by light attraction in 1979-2015 (Save Our Shearwaters [SOS] program}. For Hawaiian Petrels, we considered radar counts alone. Radar data indicated a 78% decline overall in numbers of Hawaiian Petrels (at an average rate of ~6% per year) and a 94% decline overall in numbers of Newell's Shearwaters (at an average rate of ~13% per year) during the survey period. Most (92%) radar sites showed significant declines of Newell's Shearwaters across the entire survey period, as did 62% of sites for Hawaiian Petrels. The SOS recovery effort collected 30,522 Newell's Shearwater fledglings between 1979 and 2015. When we compared this dataset in pre- and post-Hurricane Iniki (September 1992) periods, we found a significant downward trend after Hurricane Iniki, similar to the trend seen in the radar data. The large-scale declines found in this study are not surprising, considering the significant threats facing both species on Kaua'i, which include powerline collisions, light attraction, introduced predators, and habitat modification--threats which were potentially exacerbated after Hurricane Iniki. Improved conservation initiatives and an increased understanding of the various threats facing the 2 species are key to reversing these declines. [ABSTRACT FROM AUTHOR]

We analyzed 4-years of Advanced Scatterometer (ASCAT) incidence angle normalized C-band backscatter, Next-Generation Radar (NEXRAD) precipitation and United States Department of Agriculture (USDA) crop yield data over the contiguous United States (U.S.). Large negative anomalies in backscatter are evident over the U.S. during the 2011 and 2012 growing seasons, coincident with two instances of severe drought. The backscatter anomalies were correlated with diminished growing season precipitation and with reduced crop yields reflecting where drought-induced impacts on above-ground biomass production occurred. During periods of acute drought, differences in diurnal backscatter were reduced and in some cases reversed reflecting diminished nocturnal leave rehydration. The results indicate that the C-band, VV-polarized ASCAT backscatter data is sensitive to interannual variability in agricultural productivity rather than soil moisture, and can offer an important geophysical monitoring tool capable of identifying and assessing drought-related vegetation responses independent of and complementary to satellite optical/near-infrared remote sensing data. This technique is flexible and adaptable, providing a capability to address region-specific needs. The twice-daily, weather independent and near-real time global monitoring capabilities of ASCAT and similar microwave instruments (e.g. RapidSCAT, Sentinel-1) can help improve drought preparedness and mitigation planning allowing improved water resource management for agriculture on the local as well as the state level. [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 article focuses on the need to improve the efficacy of wide area surveillance (WAS) in order for the U.S. armed forces to get consistent and most powerful information for enabling field operations and policy decisions. It seeks to improve the condition in advancing surveillance tradecraft by defining principles. It looks primarily on motion intelligence systems like wide area motion imagery and ground-scanning moving target indicator radars.

The societal impacts of flash floods are more significant than any other weather-related hazard. They are often manifested in the form of damage to infrastructure, flooding of roadways and bridges, creating deadly hazards to motorists and inundation of crops and pasture. Some of these hazards can be anticipated and thus mitigated given effective warning systems. This study describes the tools proposed over recent decades in the USA to predict flash flooding and evaluates them using a common observational data set. Design recommendations for flash-flood forecasting systems are provided, taking into account today's availability of high-resolution rainfall data at scales commensurate with flash flooding, their archives, spatial data sets to describe physiographic properties, and ever-increasing computational resources.Editor D. Koutsoyiannis; Guest editor R.J. MooreCitation Gourley, J.J., Flamig, Z.L., Hong, Y., and Howard, K.W., 2014. Evaluation of past, present and future tools for radar-based flash-flood prediction in the USA. Hydrological Sciences Journal, 59 (7), 1377–1389. http://dx.doi.org/10.1080/02626667.2014.919391 [ABSTRACT FROM AUTHOR]

A novel method has been proposed for validating satellite radar snowfall retrievals using surface station observations over the western United States mountainous region, where the mean snowfall rate at a station depends on its elevation. First, all station data within a 1° × 1° grid are used to develop a snowfall rate versus elevation relation. This relation is then used to compute snowfall rate in other locations within the 1° × 1° grid, as if surface observations were available everywhere in the grid. Grid mean snowfall rates are then derived, which should be more representative to the mean snowfall rate of the grid than using data at any one station or from a simple mean of all stations in the grid. Comparison of the so-derived grid mean snowfall rates with CloudSat retrievals shows that the CloudSat product underestimates snowfall by about 65% when averaged over all the 768 grids in the western United States mountainous regions. The bias does not seem to have clear dependency on elevation but strongly depends on snowfall rate. As an application of the method, we further estimated the snowfall to precipitation ratio using both ground and satellite measured data. It is found that the rates of increase with elevation of the snowfall to precipitation ratio are quite similar when calculating from ground and satellite data, being about 25% per kilometer elevation up or approximately 4% per every degree Cuisses of temperature drop. [ABSTRACT FROM AUTHOR]

Abstract: Ground-based interferometric radar (GBIR) monitoring was conducted on a slow-moving, translational failure landslide in Granby, Grand County, Colorado, USA. Radar monitoring was completed over two separate surveys in 2011 using a tripod mounted real aperture sensor. The purpose of this work is to evaluate GBIR as a temporally dense monitoring technique for monitoring landslide displacement and compare the monitoring results to ongoing GPS based surveying methods to verify measured displacements. We discuss the strengths and limitations of GBIR displacement monitoring with a variety of available sensors, and place this monitoring platform, sensor, and workflow into context of previous slope stability monitoring with GBIR. For both surveys, displacement time series were created through a small temporal baseline stacking to reduce noise and maintain high temporal resolution. The results of the displacement time series were compared to average displacement rates derived from GPS based surveying. An overall verification of radar and GPS derived displacement rates was achieved, and identifies important differences relating to the precision and uncertainty of the two techniques. This work demonstrates GBIR monitoring capability of establishing high temporal resolution on tracking variable rates of landslide movements. Spatial modeling of total observed displacements was completed for both surveys verifying a conceptual model of uniform translational landslide movement, providing greater confidence for mitigation planning. [Copyright &y& Elsevier]

Abstract: A substantial number of wind energy projects have been stalled or abandoned in the United States of America (US) due to concerns over the effects of wind turbines on radar installations. Between 2008 and 2010, military, airspace, or meteorological radar concerns in the US contributed to the delay or abandonment of an estimated 20,000 MW of wind energy capacity. These delays are a likely major factor influencing the current US Administration''s failure to double non-hydro renewable generation from 2008 to 2011; a target stated by the US President in a joint session address to Congress in February 2009. The delays are also a threat to the US Department of Energy''s target to produce 20% of electricity from wind energy by 2030 – unless radar-related barriers are mitigated. This work includes interviews with two senior representatives, from the US Department of Defence (DOD) and the American Wind Energy Association (AWEA), discussing the nature of concerns pertaining to the effects of wind turbines on radar and military/aviation in the US alongside approaches that have been trialled that aim to resolve such concerns. This research finds that the Energy Siting Clearing House, established within the DOD to review delayed wind farm projects, has brought much needed coordination to the approval process. A key challenge for any review body, however, will be to deliver an objective outcome that is not overturned by alternative political agendas. Integral to the success of any approach will be a sufficient capacity and mandate to facilitate the technical and non-technical cross-disciplinary and interagency research generating a balance between military, airspace, meteorological, and wind energy industry/political objectives. [Copyright &y& Elsevier]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

Abstract: The hazards attributed to supercell thunderstorms are primarily considered to be tornadoes, large hail, and damaging winds, but flooding from heavy and extreme rainfall is not as often considered with these storms. As a result there has been little research on the role that supercells play in the production of heavy and extreme rainfall events. In order to assess the contribution of supercells to the climatology of short-duration precipitation extremes the present study uses the Warning Decision Support System — Integrated Information to objectively identify supercell thunderstorms from mosaicked radar data during the year 2009 and associate them with high-resolution, accurate multisensor precipitation estimates at time scales of one hour. Supercells are found to be more likely than non-supercells to produce extreme and heavy precipitation, and comparisons are also made between storm types according to month. [Copyright &y& Elsevier]

Total lightning detection systems have been in development since the mid-1980s and deployed in several areas around the world. Previous studies on total lightning found intra- and intercloud lightning (IC) activity tends to fluctuate significantly during the lifetime of thunderstorms and have indicated that lightning jumps or rapid changes in lightning flash rates are closely linked to changes in the vertical integrated liquid (VIL) reading on the National Weather Service's Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. This study examines the total lightning and its relationship to WSR-88D signatures used operationally to determine thunderstorm severity to highlight the potential benefit of a combined forecast approach. Lightning and thunderstorm data from the Dallas-Fort Worth, Texas, and Tucson, Arizona, areas from 2006 to 2009, were used to relate total lightning behavior and radar interrogation techniques. The results indicate that lightning jumps can be classified into severe wind, hail, or mixed-type jumps based on the behavior of various radar-based parameters. In 25 of 34 hail-type jumps and in 18 of 20 wind-type jumps, a characteristic change in cloud-to-ground (CG) versus IC lightning flash rates occurred prior to the report of severe weather. For hail-type jumps, IC flash rates increased, while CG flash rates were steady or decreased. For wind-type jumps, CG flash rates increased, while IC flash rates either increased (12 of 18) or were steady or decreased (6 of 18). Although not every lightning jump resulted in a severe weather report, the characteristic behavior in flash rates adds information to radar-based approaches for nowcasting the severe weather type. [ABSTRACT FROM AUTHOR]

For the first time ever, convection-resolving forecasts at 1 km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1 km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4 km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1 km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours. [ABSTRACT FROM AUTHOR]

This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z- R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions. [ABSTRACT FROM AUTHOR]

On 2 April 2010, a developing quasi-linear convective system (QLCS) moved rapidly northeastward through central Oklahoma spawning at least three intense, mesoscale vortices. At least two of these vortices caused damage rated as category 0 to 1 on the enhanced Fujita scale (EF0-EF1) in and near the town of Rush Springs. Two radar networks--the National Weather Service Weather Surveillance Radar-1988 Doppler network (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network--collected high spatial and temporal resolution data of the event. This study is an in-depth polarimetric analysis of mesovortices within a QLCS. In this case study, the storm development and evolution of the QLCS mesovortices are examined. Significant findings include the following: 1) The damage in Rush Springs was caused by a combination of the fast translation speed and the embedded circulations associated with QLCS vortices. The vortices' relative winds nearly negated the storm motion to the left of the vortex, but doubled the ground-relative wind to the right of the vortex. 2) A significant differential reflectivity (ZDR) arc developed along the forward flank of the first vortex. The ZDR arc propagated northeastward along the QLCS with the development of each new vortex. 3) A minimum in the copolar correlation coefficient (phv) in the center of the strongest vortex was observed, indicating the likely existence of a polarimetric tornado debris signature (TDS). A secondary Phv minimum also was found just to the right of the vortex center, possibly associated with lofted debris from straight-line winds. [ABSTRACT FROM AUTHOR]

The article presents a study which uses radar and satellite land-cover data to determine the habitats with which birds are associated during migration stopover habitat in the southwestern U.S. The data has been used to study geographical patterns in densities of migrant passerines and quantifying its associations between migrants and their stopover habitats. Results of the study show that it is too simplistic to consider the arid West as inhospitable landscape which has small habitat resources.

Two satellite infrared-based overshooting convective cloud-top (OT) detection methods have recently been described in the literature: 1) the 11- μm infrared window channel texture (IRW texture) method, which uses IRW channel brightness temperature (BT) spatial gradients and thresholds, and 2) the water vapor minus IRW BT difference (WV-IRW BTD). While both methods show good performance in published case study examples, it is important to quantitatively validate these methods relative to overshooting top events across the globe. Unfortunately, no overshooting top database currently exists that could be used in such study. This study examines National Aeronautics and Space Administration CloudSat Cloud Profiling Radar data to develop an OT detection validation database that is used to evaluate the IRW-texture and WV-IRW BTD OT detection methods. CloudSat data were manually examined over a 1.5-yr period to identify cases in which the cloud top penetrates above the tropopause height defined by a numerical weather prediction model and the surrounding cirrus anvil cloud top, producing 111 confirmed overshooting top events. When applied to Moderate Resolution Imaging Spectroradiometer (MODIS)-based Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager proxy data, the IRW-texture (WV-IRW BTD) method offered a 76% (96%) probability of OT detection (POD) and 16% (81%) false-alarm ratio. Case study examples show that WV-IRW BTD > 0 K identifies much of the deep convective cloud top, while the IRW-texture method focuses only on regions with a spatial scale near that of commonly observed OTs. The POD decreases by 20% when IRW-texture is applied to current geostationary imager data, highlighting the importance of imager spatial resolution for observing and detecting OT regions. [ABSTRACT FROM AUTHOR]

Sensitivity of 0-12-h warm-season precipitation forecasts to atmospheric initial conditions, including those from different large-scale model analyses and from rapid cycled (RC) three-dimensional variational data assimilations (3DVAR) with and without radar data, is investigated for a 6-day period during the International H2O Project. Neighborhood-based precipitation verification is used to compare forecasts made with the Advanced Research core of the Weather Research and Forecasting Model (ARW-WRF). Three significant convective episodes are examined by comparing the precipitation patterns and locations from different forecast experiments. From two of these three case studies, causes for the success and failure of the RC data assimilation in improving forecast skill are shown. Results indicate that the use of higher-resolution analysis in the initialization, rapid update cycling via WRF 3DVAR data assimilation, and the additional assimilation of radar observations each play a role in shortening the period of the initial precipitation spinup as well as in placing storms closer to observations, thus improving precipitation forecast skill by up to 8-9 h. Impacts of data assimilation differ for forecasts initialized at 0000 and 1200 UTC. The case studies show that the pattern and location of the forecasted precipitation were noticeably improved with radar data assimilation for the two late afternoon cases that featured lines of convection driven by surface-based cold pools. In contrast, the RC 3DVAR, both with and without radar data, had negative impacts on convective forecasts for a case of morning elevated convection associated with a midlatitude short-wave trough. [ABSTRACT FROM AUTHOR]

Little is known about air traffic controllers' preferred maneuvers to resolve conflicts between pairs of aircraft, despite the topic's importance of to the planned NextGen system. We analyzed 256 cases of conflict resolution maneuvering from radar track data from 5 U.S. air traffic control centers. Our results show the influences of conflict geometries on controllers' maneuver choices. [ABSTRACT FROM AUTHOR]

The current network of weather surveillance radars within the United States readily detects flying birds and has proven to be a useful remote-sensing tool for ornithological study. Radar reflectivity measures serve as an index to bird density and have been used to quantitatively map landbird distributions during migratory stopover by sampling birds aloft at the onset of nocturnal migratory flights. Our objective was to further develop and validate a similar approach for mapping wintering waterfowl distributions using weather surveillance radar observations at the onset of evening flights. We evaluated data from the Sacramento, CA radar (KDAX) during winters 1998-1999 and 1999-2000. We determined an optimal sampling time by evaluating the accuracy and precision of radar observations at different times during the onset of evening flight relative to observed diurnal distributions of radio-marked birds on the ground. The mean time of evening flight initiation occurred 23 min after sunset with the strongest correlations between reflectivity and waterfowl density on the ground occurring almost immediately after flight initiation. Radar measures became more spatially homogeneous as evening flight progressed because birds dispersed from their departure locations. Radars effectively detected birds to a mean maximum range of 83 km during the first 20 min of evening flight. Using a sun elevation angle of -5° (28 min after sunset) as our optimal sampling time, we validated our approach using KDAX data and additional data from the Beale Air Force Base, CA (KBBX) radar during winter 1998-1999. Bias-adjusted radar reflectivity of waterfowl aloft was positively related to the observed diurnal density of radio-marked waterfowl locations on the ground. Thus, weather radars provide accurate measures of relative wintering waterfowl density that can be used to comprehensively map their distributions over large spatial extents. [ABSTRACT FROM AUTHOR]

This paper presents a case study of an EF3 tornado that adversely impacted Greene and Pitt Counties in eastern North Carolina on 16 April 2011. This was one of the most damaging and longest-lived of the multiple tornados that occurred across central and eastern North Carolina that day, the most extensive outbreak in North Carolina since 1984. This event occurred during the month (April 2011) with the largest number of tornadoes on record in the United States. The focus of this case study was to examine the relationship between the mesocyclone evolution and the location and intensity of surface damage associated with the EF3 tornado. Results indicated that the initial contraction and spin up of the mesocyclone circulation preceded EF3 damage by about 20 minutes. At the time of mesocyclone intensification, the damage swath and tornado were situated much closer to the mesocyclone center than in the formative and dissipating stages. The weakened mesocyclone passed directly over a meteorological station at East Carolina University's West Research Campus, providing a rare opportunity for surface measurements associated with a weakening tornadic mesocyclone. [ABSTRACT FROM AUTHOR]