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This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

X‐band marine radar observations and a hindcast simulation from a 3D hydrostatic model are used to provide an overview of Connecticut River (USA) ebb plume front expansion into the strong tidal crossflow of eastern Long Island Sound. The model performance is evaluated against in situ and remote sensing observations and demonstrates dominant control of the front by semidiurnal tides. The recurring frontal evolution is classified into three dynamical stages of arrest, propagation, and advection. A conceptual model that follows this progressing balance between outflow buoyancy and crossflow momentum qualitatively reproduces frontal evolution in both the radar observations and the hindcast. The majority of the residual, intertidal variability of front timing and geometry is explained by co‐varying tidal amplitude, freshwater discharge, and wind stress using a multi‐linear regression analysis of the radar observation record. Intrinsic front speeds in the modeled frontal propagation are compared with the analytical model of Benjamin (1968, https://doi.org/10.1017/s0022112068000133), with better agreement achieved after accounting for ambient near‐surface shear associated with wind forcing. Plain Language Summary: The fresh buoyant water that exits the Connecticut River mouth (USA) during each ebb tide expands as a plume, and floats above the denser waters of Long Island Sound. Currents in the Sound flow back and forth along the coast each tide, pushing the plume along the shore first to the east and then to the west. The location of the plume boundary (front) goes through a similar cycle almost every tide, initially being held stationary by eastward tidal currents, then expanding freely around slack tide, and subsequently being aided as it moves to the west by the ambient flow. Small changes to the arrival time and shape of the front are linked to changes in river discharge, tidal strength, and winds. Realistic simulations using a type of numerical model that aids coastal decision making are able to reproduce the tidal progression of front movement that was observed in 6 weeks of marine radar images of the water surface. Front movement in the numerical model also fits a simple analytical theory to a degree not previously seen on such a large scale, despite the complicating effects of wind. Key Points: Buoyant ebb plume front kinematics in a tidal cross‐flow are characterized by stages of arrest, propagation, and advectionWind, tide, and discharge variations explain modifications to the dominant semidiurnal control of the Connecticut River plume frontFront propagation in a hydrostatic numerical model reproduces both radar remote sensing observations and gravity current theory [ABSTRACT FROM AUTHOR]

More than two billion birds migrate through the Gulf of Mexico each spring en route to breeding grounds in the USA and Canada. This region has a long history of complex natural and anthropogenic environments as the northern Gulf coast provides the first possible stopover habitats for migrants making nonstop trans‐Gulf crossings during spring migration. However, intense anthropogenic activity in the region, which is expanding rapidly at present, makes migrants vulnerable to a multitude of obstacles and increasingly fragments and alters these habitats. Understanding the timing of migrants' overwater arrivals has biological value for expanding our understanding of migration ecology relative to decision‐making for nonstop flights, and is imperative for advancing conservation of this critical region through the identification of key times in which to direct conservation actions (e.g. temporary halting of wind turbines, reduction of light pollution). We explored 10 years of weather surveillance radar data from five sites along the northern Gulf of Mexico coast to quantify the daily timing and intensity of arriving trans‐Gulf migrants. On a daily scale, we found that migrant intensity peaked an average of nine hours after local sunrise, occurring earliest at easternmost sites. On a seasonal level, the greatest number of arrivals occurred between late April and early May, with peak intensity occurring latest at westernmost sites. Overall intensity of migration across all 10 years of data was greatest at the westernmost sites and decreased moving farther to the east. These findings emphasize the differential spatial and temporal patterns of use of the Gulf of Mexico region by migrating birds, information that is essential for improving our understanding of the ecology of trans‐Gulf migration and for supporting data‐driven approaches to conservation actions for the migratory birds passing through this critical region. [ABSTRACT FROM AUTHOR]

Waterfowl migrations are large‐scale events that involve millions of birds moving over broad geographic extents, which make them difficult to quantify and study. Historically, wildlife managers have relied mostly on field surveys, such as visual counts from the ground or air that sample at small spatial or temporal extents, or both. Combining field surveys with remote sensing data comprehensively collected over large spatial extents at high temporal frequency may improve the study of migrating waterfowl distributions. We tested the strength of the relationship between broad‐scale weather surveillance radar data and fine‐scale field surveys of waterfowl abundance at wetlands within the Rainwater Basin of Nebraska, USA, from 2017–2019. Radar reflectivity of waterfowl at the peak exodus of evening flights was positively correlated with diurnal waterfowl count data, although there was unexplained variation in the relationship. The association was also very similar across various time scales ranging from daily to monthly averages of waterfowl abundance. We suggest that human‐based ground surveys can calibrate and leverage more comprehensive remote sensing data to get a broad understanding of waterfowl distributions during migration. Several confounding factors, such as spatial displacement between radar and survey observation, individual variation in orientation and body size, and identification of avian species sampled by radar, remain on the path to improving radar‐based waterfowl estimates. [ABSTRACT FROM AUTHOR]

Clouds cover substantial parts of the Earth's surface and they are one of the most essential components of the global climate system impacting the Earth's radiation balance as well as the water cycle redistributing water around the globe as precipitation. Therefore, continuous observation of clouds is of primary interest in climate and hydrological studies. This work documents the first efforts in Italy in remote sensing clouds and precipitation using a combination of K- and W-band (24 and 94 GHz, respectively) radar profilers. Such a dual-frequency radar configuration has not been widely used yet, but it could catch on in the near future given its lower initial cost and ease of deployment for commercially available systems at 24 GHz, with respect to more established configurations. A field campaign running at the Casale Calore observatory at the University of L'Aquila, Italy, nestled in the Apennine mountain range is described. The campaign features are preceded by a review of the literature and the underpinning theoretical background that might help newcomers, especially in the Italian community, to approach cloud and precipitation remote sensing. This activity takes place in interesting time for radar sensing clouds and precipitation, stimulated both by the launch of the ESA/JAXA EarthCARE satellite missions scheduled in 2024, which will have on-board, among other instruments, a W-band Doppler cloud radar and the proposal of new missions using cloud radars currently undergoing their feasibility studies (e.g., WIVERN and AOS in Europe and Canada, and U.S., respectively). [ABSTRACT FROM AUTHOR]

Observations made by weather radars play a central role in many aspects of meteorological research and forecasting. These applications commonly require that radar data be supplied on a Cartesian grid, necessitating a coordinate transformation and interpolation from the radar's native spherical geometry using a process known as gridding. In this study, we introduce a variational gridding method and, through a series of theoretical and real data experiments, show that it outperforms existing methods in terms of data resolution, noise filtering, spatial continuity, and more. Known problems with existing gridding methods (Cressman weighted average and nearest neighbor/linear interpolation) are also underscored, suggesting the potential for substantial improvements in many applications involving gridded radar data, including operational forecasting, hydrological retrievals, and three-dimensional wind retrievals. [ABSTRACT FROM AUTHOR]

Measurements provided by Next Generation Weather Radar and operational thunderstorm monitoring instruments at the Kennedy Space Center and the Air Force Eastern Range have been examined to determine the initial electrical development of 13 isolated, air mass thunderstorms. The same instruments were used to examine the surface potential gradient prior to and following 13 long, horizontal discharges that propagated into the area. The motivation and primary objective for this work was to evaluate the safety of the existing lightning‐related launch constraints associated with surface potential gradient and precipitation radar measurements. The onset of cloud electrification as seen by a large‐area surface electric field mill network was detected 3.7–14.6 min before the first lightning discharge, with lead‐times that depended on proximity to the storm. In 11 of 13 cases, the first detectable field change was a positive excursion in potential gradient close to the storm, likely indicating initial development of lower positive charge. Lead‐times for the radar‐derived cloud tops (0 dBZ) reaching −20°C were longer than those for early electrification in all but two cases. Surface potential gradients above +500 V/m or below −100 V/m "warning thresholds" were exceeded before the first lightning flash in all cases. Radar reflectivities >35 dBZ above the −10°C level provided 3–14 min of lead time for lightning. Potential gradients just prior to and near long horizontal discharges exceeded 3 kV/m at most sites and were typically positive. Measurements at a single field mill site would not have provided adequate warning. Key Points: Elevated surface potential gradients precede lightning in both the early and late stages of thunderstormsElectrification in cumulus clouds can begin before the 0 dBZ radar cloud top reaches the −20°C level and within 4–5 min after it reaches the −10°C levelInitial electrification often indicated the presence of a localized lower positive charge region that was sometimes enhanced by early intracloud discharges [ABSTRACT FROM AUTHOR]

The accuracy of weather radar-measured products, such as reflectivity, plays a crucial factor in obtaining good quality, derived remote sensing products, such as hydrometeor classification. A slight mismatch of a few decibels in reflectivity may cause the hydrometeor classification to deviate from the actual truth. In order to obtain accurate remote-sensing measurements, calibration of weather radars should be carried out at regular intervals of time. The dual-frequency, dual-polarization, Doppler radar (D3R) is a well-established tool for measuring light rain and snow. There are various methods which are used for the calibration of weather radars, such as suspending a metallic sphere from a weather balloon or using corner reflectors on top of a tower or structure. In this work, we have shown the potential of using a UAV to suspend a calibration sphere, which is then used for calibrating the D3R radar. In this work, the advantages along with the practical aspects to be considered for calibration using UAV are discussed in detail. From the calibration results, it was observed that an offset of 2.2 dB was present in the Ku H-Pol reflectivity, Ku V-Pol was well calibrated and offsets within 2 dB were observed in the Ka H-Pol and V-Pol reflectivities. [ABSTRACT FROM AUTHOR]

A novel polarimetric radar algorithm for melting-layer (ML) detection and determination of its height has been developed and tested for a large number of cold-season weather events. The algorithm uses radial profiles of the cross-correlation coefficient (ρhv or CC) at the lowest elevation angles (<5°–6°). The effects of beam broadening on the spatial distribution of CC have been taken into account via theoretical simulations of the radial profiles of CC assuming intrinsic vertical profiles of polarimetric radar variables within the ML with varying heights and depths of the ML. The model radial profiles of CC and their key parameters are stored in lookup tables and compared with the measured CC profiles. The matching of the model and measured CC radial profiles allows the algorithm to determine the "true" heights of the top and bottom of the ML, Ht and Hb, at distances up to 150 km from the radar. Integrating the CC information from all available antenna elevations makes it possible to produce accurate maps of Ht and Hb over large areas of radar coverage as opposed to the previous ML detection methods including the existing algorithm implemented on the U.S. network of the WSR-88Ds. The initial version of the algorithm has been implemented in C++ and tested for a multitude of cold-season weather events characterized by a low ML with different degrees of spatial nonuniformity including cases with sharp frontal boundaries and rain–snow transitions. The new ML detection algorithm (MLDA) exhibits robust performance, demonstrating spatial and temporal continuity, and showing general consistency of the ML designations matching those obtained from the regional model and the quasi-vertical profiles (QVP) methodology output. [ABSTRACT FROM AUTHOR]

The S-band WSR-88D is sensitive enough to observe biological scatterers like birds and insects. However, their nonspherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (insect) features by coherently averaging dual-polarization measurements from multiple radar scans containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual-polarization inputs and then different combinations of the remaining features are added. The performance of all models for both methods are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration, and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual-polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the U.S. Next Generation Weather Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing hydrometeor classification algorithm (HCA), where the HCA would first separate biological from nonbiological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual-polarization variables at each range gate. [ABSTRACT FROM AUTHOR]

The direction-finding high-frequency (HF) radar that employs a crossed-loop/monopole antenna as receiving has been widely applied in sea surface dynamics remote sensing due to its compact footprint and remarkable performance. Nevertheless, the direction-of-arrival (DOA) estimation of this system remains challenging in the case of coherent signals, which imposes a great threat to the radar measurement accuracy but has rarely been concerned in the past. In this article, a novel method combining the covariance matrix reconstruction process and subspace estimation technology is proposed to deal with this problem. By exploiting the purely real property of the array manifold, the rank loss of the covariance matrix due to the coherence of signals is mitigated. Both the theoretical derivation and numerical analysis prove that the proposed method is valid in estimating two coherent signals, and the DOA estimation accuracy is mainly related to the signal-to-noise ratio (SNR) and angle separation of the signals. Finally, field experiment data are also used to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The Royal Netherlands Meteorological Institute (KNMI) operates two operational dual-polarization C-band weather radars providing 2D radar rainfall products. Attenuation can result in severe underestimation of rainfall amounts, particularly in convective situations that are known to have high impact on society. To improve the radar-based precipitation estimates, two attenuation correction methods are evaluated and compared: 1) modified Kraemer (MK) method, i.e., Hitschfeld–Bordan where parameters of the power-law Zh–kh relation are adjusted such that reflectivities in the entire dataset do not exceed 59 dBZh and attenuation correction is limited to 10 dB; and 2) using two-way path-integrated attenuation computed from the dual-polarization moment specific differential phase Kdp (Kdp method). In both cases the open-source Python library wradlib is employed for the actual attenuation correction. A radar voxel only contributes to the computed path-integrated attenuation if its height is below the forecasted freezing-level height from the numerical weather prediction model HARMONIE-AROME. The methods are effective in improving hourly and daily quantitative precipitation estimation (QPE), where the Kdp method performs best. The verification against rain gauge data shows that the underestimation diminishes from 55% to 37% for hourly rainfall for the Kdp method when the gauge indicates more than 10 mm of rain in that hour. The improvement for the MK method is less pronounced, with a resulting underestimation of 40%. The stability of the MK method holds a promise for application to data archives from single-polarization radars. [ABSTRACT FROM AUTHOR]

Monitoring avian migration within subarctic regions of the globe poses logistical challenges. Populations in these regions often encounter the most rapid effects of changing climates, and these seasonally productive areas are especially important in supporting bird populations—emphasizing the need for monitoring tools and strategies. To this end, we leverage the untapped potential of weather surveillance radar data to quantify active migration through the airspaces of Alaska. We use over 400 000 NEXRAD radar scans from seven stations across the state between 1995 and 2018 (86% of samples derived from 2013 to 2018) to measure spring and autumn migration intensity, phenology and directionality. A large bow-shaped terrestrial migratory system spanning the southern two-thirds of the state was identified, with birds generally moving along a northwest–southeast diagonal axis east of the 150th meridian, and along a northeast–southwest axis west of this meridian. Spring peak migration ranged from 3 May to 30 May and between, 18 August and 12 September during the autumn, with timing across stations predicted by longitude, rather than latitude. Across all stations, the intensity of migration was greatest during the autumn as compared to spring, highlighting the opportunity to measure seasonal indices of net breeding productivity for this important system as additional years of radar measurements are amassed. [ABSTRACT FROM AUTHOR]

The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase KDP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, σ e ⁡ (K DP , Z) = γ K DP α Z β . The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles' densities, the width of the canting angle distribution, and particles' aspect ratios, whereas the relation's exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient. [ABSTRACT FROM AUTHOR]

Assessments of spatiotemporal severe hailfall characteristics using hail reports are plagued by serious limitations in report databases, including biases in reported sizes, occurrence time, and location. Multiple studies have used Next Generation Weather Radar (NEXRAD) network observations or environmental hail proxies from reanalyses. Previous work has specifically utilized the single-polarization radar parameter maximum expected size of hail (MESH). In addition to previous work being temporally limited, updates are needed to include recent improvements that have been made to MESH. This study aims to quantify severe hailfall characteristics during a 23-yr period, markedly longer than previous studies, using both radar observations and reanalysis data. First, the improved MESH configuration is applied to the full archive of gridded hourly radar observations known as GridRad (1995–2017). Next, environmental constraints from the Modern-Era Retrospective Analysis for Research and Applications, version 2, are applied to the MESH distributions to produce a corrected hailfall climatology that accounts for the reduced likelihood of hail reaching the ground. Spatial, diurnal, and seasonal patterns show that in contrast to the report climatology indicating one high-frequency hail maximum centered on the Great Plains, the MESH-only method characterizes two regions: the Great Plains and the Gulf Coast. The environmentally filtered MESH climatology reveals improved agreement between report characteristics (frequency, location, and timing) and the recently improved MESH calculation methods, and it reveals an overall increase in diagnosed hail days and westward broadening in the spatial maximum in the Great Plains than that seen in reports. [ABSTRACT FROM AUTHOR]

Precipitation consists of many types of hydrometeors, such as raindrops, ice crystals, graupel, and hail. Due to their impacts, graupel and hail (GH) have received particular attention in the literature. Global Precipitation Measurement (GPM) dual-frequency radar (DPR) has proved to be a very reliable system for global precipitation retrievals. This paper aims to develop a GH identification algorithm for GPM DPR. This algorithm is constructed using a precipitation type index (PTI) defined for DPR. The PTI is effective in separating hydrometeor types and is calculated using measurements of reflectivity, dual-frequency ratio, and storm top height data. The output of the algorithm is a Boolean product representing the existence of graupel or hail along with the vertical profile for each Ku- and Ka-band matched footprint. Cross validation is performed with the Weather Service Radar (WSR-88D) network over continental United States as well as during the Remote sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) experiment. Evaluation of the GH identification algorithm is performed on a global basis, which illustrates promising comparisons with the global lightning and hail precipitation maps generated using radar and radiometer. [ABSTRACT FROM AUTHOR]

The first X-band dual polarized phased array weather radar (DP-PAWR), which simultaneously transmits pulses of horizontal and vertical polarized radiation, was developed and installed at Saitama University, Japan, in December 2017. The DP-PAWR uses mechanical and electronic scanning at azimuth and elevation angles, respectively. It provides polarimetric precipitation measurements via 3-D volume scanning with an update rate between 10 and 60 s, for a range of up to 80 km. Here, we describe the initial DP-PAWR observation results. To evaluate the DP-PAWR observation accuracy, we compared the observational data with radar variables derived from Parsivel disdrometer data. In comparison with the disdrometer, the relative observation accuracy for the DP-PAWR radar reflectively factor had a standard deviation of 1.1 dB and mean value of 0.4 dB. We also conducted detailed observations of a developing thunderstorm using a specific differential phase (Kdp) column, focusing on the Kdp core during the storm. The Kdp core movements provided useful information about the convection flow during the storm. [ABSTRACT FROM AUTHOR]

The paper is focused on the problem of weather radar precipitation compositing and aims to produce the data for the whole measurement domain in a way that ensures the quality of the composite is as high as possible. This is achieved by employing information about the data quality in the form of a quality index (QI) map. Different techniques that take the QI values as the compositing criterion are investigated; they apply the maximum or the weighted mean from particular radars. The proposed method employs a combination of the QI value and the distance from the radar site as a criterion in a weighted mean approach. Different versions of the quality‐based techniques are compared with standard techniques that are based on the maximum of estimated precipitation and distance from the nearest radar site. Verification of the techniques was performed employing statistical metrics by comparison against rain gauge observations. Moreover qualitative verification with assessment of seamlessness and the presence of contamination was carried out on long‐term accumulations of the composites generated by means of different methods. The proposed method based on QI value and distance from the radar site was found to be the most suitable for radar precipitation compositing. [ABSTRACT FROM AUTHOR]

The emergence of CubeSats has opened up the possibilities of advanced space missions with lower costs and faster development times. A major factor that limits the functionality of CubeSats is the absence of high-gain antennas (HGAs) that can sustain a high data-rate link for communications or provide the required spatial resolution for remote sensing. In this work, we discuss the development of one of the largest apertures at Ka-band for CubeSats—a 1 m mesh deployable offset reflector antenna, with a stowed volume of 3U ($10\times 10\times 30 \,\,\text {cm}^{3}$)—to enable precipitation radars that can achieve the required spatial resolution while meeting the stringent mechanical constraints posed by the small CubeSat volume. We detail the critical aspects of this antenna design, including RF characterization, antenna fabrication, and measurement. We also describe a novel deployment mechanism that facilitates the packaging of such a large aperture in a small volume. The antenna demonstrated a measured efficiency of 60%, with a half-power beamwidth of 0.60° at 35.75 GHz. The cost reduction afforded by CubeSats makes launching a constellation of such radar based CubeSats practical, allowing higher temporal sampling rates, which is essential for the observation of weather processes with a short-time evolution. [ABSTRACT FROM AUTHOR]

An important objective in scatterometry is the estimation of near-surface wind speed and direction in the presence of rain. We investigate an attenuation correction method using data from the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dual-frequency scatterometer, which operates at Ku and Ka band with dual conical scans at incidence angles of 30° and 40°. The method relies on the fact that the differential normalized surface cross section, δσ0 = σ0(Ka) − σ0(Ku), is relatively insensitive to wind speed and direction and that this quantity is closely related to the magnitude of the differential path attenuation, δA = A(Ka) − A(Ku), arising from precipitation, cloud, and atmospheric gases. As the method relies only on the difference between quantities measured in the presence and absence of rain, the estimates are independent of radar calibration error. As a test of the method's accuracy, we make use of the fact that the radar rain reflectivities just above the surface, as seen along different incidence angles, are approximately the same. This yields constraint equations in the form of differences between pairs of path attenuations along different lines of sight to the surface. A second validation method uses the dual-frequency radar returns from the rain just above the surface where it can be shown that the difference between the Ku- and Ka-band-measured radar reflectivity factors provide an estimate of differential path attenuation. Comparisons between the path attenuations derived from the normalized surface cross section and those from these surface-independent methods generally show good agreement. [ABSTRACT FROM AUTHOR]

A novel procedure based on filtering and interpolation approaches is proposed to estimate the sea state parameters, including significant wave height, peak wave direction, peak period, peak wavenumber, and peak wavelength in shallow waters using the X-band marine radars. The method compensates the distortions introduced by the radar acquisition process and the power decay of the radar signal along the distance applying image-enhancement techniques instead of empirical and semiempirical calibration methods that use signal-to-noise ratio and in situ measurements as external references. To determine the threshold value for the interpolation approach, the influence of the antenna height on shadowing modulation effects is examined through performing an analysis of variance (ANOVA) that uses data from two X-band radars deployed at 10 and 20 m above MSL. ANOVA results reveal that it is possible to explain the increment of intensities affected by shadowing throughout the distance using an adaptive threshold retrieved from a third-order polynomial function of the mean radar cross section (RCS). Finally, an X-band radar is installed at 13 m above MSL to test the proposed technique. During measurements, the wind and wave conditions varied, and the antenna-look direction remained constant. Errors for $H_{s}$ , $\theta _{p}$ , and $T_{p}$ calculated as the difference between estimated and true data show a mean bias and a relative value of 0.05 m (2.72%), 1.52° (5.94%), and 0.15 s (1.67%), respectively. The directional and wave energy spectra derived from radar estimates, acoustic wave and current, ADVs record, as well as JONSWAP formulation are presented to illustrate the improvement resulting from the proposed method over the frequency domain. [ABSTRACT FROM AUTHOR]

By using long pulses with extended dwell time, radar sensitivity is enhanced such that more clear-air returns are usable. This enhanced capability can increase clear-air data coverage and provide additional information for radar wind analysis and data assimilation. To explore this potential capability and related benefits, a new scan mode with long pulses and low antenna rotation rate is derived from the existing clear-air scan mode and applied to the named KOUN S-band test bed radar at NSSL. Velocities collected using this newly modified scan mode are significantly enhanced in data coverage compared to those collected with the unmodified scan mode from the nearby test bed radar (named KCRI). However, transmitter duty cycle limits the pulse repetition frequency (PRF) to 455 Hz for the long pulses, which reduces the Nyquist velocity to 12.53 m s−1 for the new scan mode. Such a small Nyquist interval leads to significantly worse problems with aliased velocities. A recently developed dealiasing algorithm is modified by using external reference velocities from KCRI to overcome increased difficulties in dealiasing, making the velocities from KOUN useful over increased range and area. This implies that similar increases in velocity data coverage can be achieved without using external reference velocities if the newly modified scan is paired with an unmodified scan into dual-PRF scans. This dual-PRF approach is proposed for future development. [ABSTRACT FROM AUTHOR]

Honeybees, Apis mellifera, perform re-orientation flights to learn about the new surroundings of the hive when their hive is transported to a new location. Since the pattern of re-orientation flights has not yet been studied, we asked whether this form of exploratory behavior differs from the well described exploratory orientation flights performed by young honeybees before they start foraging. We also investigated whether the exploratory components of re-orientation flights differ from foraging flights and if so how. We recorded re-orientation flights using harmonic radar technology and compared the patterns and flight parameters of these flights with the first exploratory orientation flights of young honeybees and foraging flights of experienced foragers. Just as exploratory orientation flights of young honeybees, re-orientation flights can be classified into short- and long-range flights, and most short-range re-orientation flights were performed under unfavorable weather conditions. This indicates that bees adapt the flight pattern of their re-orientation and orientation flights to changing weather conditions in a similar way. Unlike exploratory orientation flights, more than one sector of the landscape was explored during a long-range re-orientation flight, and significantly longer flight durations and flight distances were observed. Thus, re-orienting bees explored a larger terrain than bees performing their first exploratory orientation flight. By displacing some bees after their first re-orientation flight, we could demonstrate that a single re-orientation flight seems to be sufficient to learn the new location of the hive. The flight patterns of re-orientation flights differed clearly from those of foraging flights. Thus, re-orientation flights represent a special exploratory behavior that is triggered by a change in the location of the hive. [ABSTRACT FROM AUTHOR]

The X-band phased array weather radar (PAWR) at Osaka University has a rapid scanning rate and is capable of high-density observations in elevation; it produces approximately 100 plan position indicator radar images with a 60-km range, at different elevation angles, in less than 30 s. The PAWR uses a fan-shaped beam with a narrow beamwidth (1.2°) in azimuth and a wider beamwidth (from 5° to 10°) in elevation. With digital beamforming (DBF), the elevation beamwidth can be reduced to 1.2°, using 128 antenna elements arranged in tandem. Although the fan-shaped beam is useful for rapid scanning, the received signals tend to be affected by ground clutter. In this paper, we investigate the clutter suppression capability of common DBF methods: Fourier, Capon, and minimum mean-square error (MMSE) beamforming. Furthermore, to improve performance when the PAWR data contain errors—such as lacking data caused by mechanical problems—a correction method is proposed. The effect of clutter suppression using MMSE is shown to be greatly improved if used together with the proposed correction method. The resulting method is shown to sufficiently suppress clutter in all elevation angles above a few degrees, even in the presence of strong clutter; its clutter reduction performance is compared and found to be superior to the ones of the analyzed conventional DBF methods. [ABSTRACT FROM PUBLISHER]

The accuracy of rain-rate estimation using polarimetric radar measurements has been improved as a result of better characterization of radar measurement quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described because of the dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account radar observational error and dynamically varying rain microphysics is proposed in this study. Rain-rate estimation using the variational algorithm that uses event-based observational error and background rain climatological values is evaluated using observing system simulation experiments (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event occurred between 11 and 12 September 2013. The results from OSSE show that the variational algorithm with event-based observational error consistently estimates more accurate rain rate than does the ' R( ZHH, ZDR)' power-law algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of differential reflectivity ZDR than is the R( ZHH, ZDR) algorithm. The variational quantitative precipitation estimation (QPE) retrieved more accurate rainfall estimation than did the power-law dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the Next Generation Weather Radar (NEXRAD). [ABSTRACT FROM AUTHOR]

A comparison of model predictions with measurements of near-nadir normalized radar cross sections (NRCSs) of the sea surface at Ku- and Ka-bands is reported. Measurements of Airborne Precipitation Radar Second Generation (APR-2) from near nadir to 25 ° incidence angle, along with simultaneous wind truth from dropsonde observations, are compared with predictions of the “cutoff-invariant” two-scale model of sea scattering with the overall goal of assessing the model for possible future use in the APR-2 calibration process. The performance of the model as a function of wind speed and incidence angle is therefore emphasized. The measured data set, acquired primarily during the 2010 “Genesis and Rapid Intensification Processes” (GRIP) experiment, includes wind speeds from approximately 5 to 45 m/s. Model comparisons are limited by uncertainties in the wind fields due to limited dropsonde coverage; the data set is separated into “more reliable” (containing wind speeds of 5-20 m/s) and “less reliable” (wind speeds of 5-45 m/s) wind truth categories accordingly. Because a model of the sea spectrum is required for cutoff-invariant model predictions, comparisons with measured data are performed for three differing sea spectrum descriptions. It is found that a bias of less than ~ 1 dB over the wind speed range 5-40 m/s and a standard deviation less than 1 dB over the wind speed range 10-40 m/s can be achieved when using the “unified” sea spectrum description of Elfouhaily The model also provides error levels that are near uniform with respect to both incidence angle and wind speed. [ABSTRACT FROM PUBLISHER]

In the past few years, demands on flash flood forecasting have grown. The Flash Flood Indicator (FFI) is a system used at the Czech Hydrometeorological Institute for the evaluation of the risk of possible occurrence of flash floods over the whole Czech Republic. The FFI calculation is based on the current soil saturation, the physical-geographical characteristics of every considered area, and radar-based quantitative precipitation estimates (QPEs) and forecasts (QPFs). For higher reliability of the flash flood risk assessment, calculations of QPEs and QPFs are crucial, particularly when very high intensities of rainfall are reached or expected. QPEs and QPFs entering the FFI computations are the products of the Czech Weather Radar Network. The QPF is based on the COTREC extrapolation method. The radar-rain gauge-combining method MERGE2 is used to improve radar-only QPEs and QPFs. It generates a combined radar-rain gauge QPE based on the kriging with an external drift algorithm, and, also, an adjustment coefficient applicable to radar-only QPEs and QPFs. The adjustment coefficient is applied in situations when corresponding rain gauge measurements are not yet available. A new adjustment coefficient scheme was developed and tested to improve the performance of adjusted radar QPEs and QPFs in the FFI. [ABSTRACT FROM AUTHOR]

Surface Canopy Water (SCW) is the intercepted rain water that resides within the tree canopy and plays a significant role in the hydrological cycle. Challenges arise in measuring SCW in remote areas using traditional ground-based techniques. Remote sensing in the radio spectrum has the potential to overcome the challenges where traditional modelling approaches face difficulties. In this study, we aim at estimating the SCW by fusing information extracted from the radar imagery acquired with the Sentinel-1 constellation, aerial laser scanning, and meteorological data. To describe the change of radar backscatter with moisture, we focused on six forest stands in the H.J. Andrews experimental forest in central Oregon, as well as four clear cut areas and one golf course, over the summers of 2015–2017. We found significant relationships when we executed the analysis on radar images in which individual tree crowns were delineated from lidar, as opposed to SCW estimated from individual pixel backscatter. Significant differences occur in the mean backscatter between radar images taken during rain vs. dry periods (no rain for >1 h), but these effects only last for roughly 30 min after the end of a rain event. We developed a predictive model for SCW using the radar images acquired at dawn, and proved the capability of space-based radar systems to provide information for estimation of the canopy moisture under conditions of fresh rainfall during the dry season. [ABSTRACT FROM AUTHOR]

Real-time monitoring of volcanic tephra fallout rate is an important factor to predict ash plume dispersion and to mitigate risk to air traffic. Ground-based weather radar has been one of the fundamental instruments to detect the plume and derive eruptive source parameters, such as the tephra fallout rate. The current work presents the use of two small and compact X-band Multi-Parameter (X-MP) radars for a new tephra fallout rate model development and the technical aspects of the system in Sinabung and Merapi Volcanoes. The new model estimates the tephra fallout rate using two radar parameters: the specific differential phase shift parameter and the reflectivity intensity factor. Total cumulated mass estimated from the radar-based tephra fallout rate model from the radar is compared with the plume height model and an empirical radar-based model. A volcanic eruptive index (VEI)-2 of Sinabung generated a plume exceeding 15 km, resulting in a maximum tephra fallout rate of 0.58 kg m−2 h−1 and a total tephra mass of 51 × 106 kg. The VEI 1 of Sinabung caused a plume height of 2.5 km, resulting in a maximum tephra fallout rate of 0.3 kg m−2 h−1 and a total cumulated tephra of 9 × 106 kg. In the last case, a VEI 1 eruption of Mt. Merapi produces a 6 km plume, resulting in a maximum tephra fallout rate of 0.28 kg m−2 h−1 and a total cumulated tephra of 35 × 106 kg. The sector range height indicator scan-mode strategy in the VEI 2 eruption of Mt. Sinabung ran at six degrees azimuth angles capturing only a partial volume of the plume. Thus, the total mass was only 22% of the result from the empirical plume height model, even though the plume height was assumed to be equally the same with the maximum height scanned of radar at 7 km. In contrast, the volumetric scan by a plan position indicator strategy gave a total cumulated tephra mass, that matches better to the result of the empirical plume height model at 65–92%. Based on these results and the ability of the X-MP radar to capture the volcanic plume at the same reported onset time, we can confirm the importance of an X-MP radar for real-time tephra fallout monitoring during an eruption. • Utilization of a small-compact dual polarimetric X-band weather radar for a real-time eruption monitoring • Deployment, data acquisition, and data processing of a small-compact dual polarimetric X-band weather radar • Real-time tephra fallout rate by multi parameter of radar • Multi-Parameter radar information of tephra • Two-dimensional (2D) and Three-dimensional (3D) visualization of a volcanic plume by a weather radar [ABSTRACT FROM AUTHOR]

Rain attenuation can hinder the implementation of quantitative precipitation estimations using X-band weather radar. Numerous studies have been conducted on correcting the attenuation of radar reflectivity by utilizing a dual-polarimetric radar and an arbitrary-oriented microwave link; however, there is a need to optimize the required number of microwave links and their locations. In this study, we tested four attenuation correction methods and proposed a novel algorithm based on the sole use of adjacent multiple microwave links. The attenuation of the X-band radar reflectivity was corrected by performing forward iterations at each link, and the correction coefficients were statistically analyzed to reduce the instability problem. The algorithms of each method were evaluated by studying the cases of convective and stratiform rainfall, and then validated by comparing the corrected reflectivity of the X-band radar with the qualitatively controlled reflectivity of the S-band radar. The new method was as efficient as the conventional method based on the specific differential phase of dual-polarimetric radar. Furthermore, the correction coefficient was more effectively optimized and stabilized using seven microwave links rather than a single link, and no further independent reference data were required. In addition, the attenuation correction also accounted for spatiotemporal differentiation depending on the rainfall type, and could recover the physical structure of the rainfall. The method developed herein can facilitate estimations of quantitative rainfall in developing countries where dual-polarization weather radars are not common. The exploitation of microwave link data is a promising method for rainfall remote sensing. [ABSTRACT FROM AUTHOR]

Recently, the interest on snowfall remote sensing and quantitative precipitation estimation is becoming a popular topic by both the scientific and operational communities. As a matter of fact, snow plays a key role in the hydrological cycle and Earth energy budget and clearly represents a meteorological hazard that can seriously compromise human activities and properties. In this study, we used a dual-polarization X-band weather radar to quantify the near-surface liquid equivalent snowfall rate, proposing a new parameterization based on the use of radar reflectivity factor and specific differential phase shift. This effort adds to several recent works, mainly focused on S-band weather radar systems, demonstrating that the use of the radar specific differential phase shift (K dp) is able to enhance the estimation precision with respect to the more customary approaches making use of radar reflectivity factor alone. To demonstrate this concept also at X-band, some case studies were collected from December 2018 to May 2019 in the Southern Apennine Mountains in the area of Naples (Italy). They were used to compare the proposed radar based liquid equivalent snowfall rate estimations, based on Z and K dp , with reference laser-optical disdrometer time series collected in the close reference site of Montevergine observatory. Findings show that also at X band the use of K dp produces a better score between the radar-derived liquid equivalent snowfall rate and the reference one from the disdrometer. • The use of specific differential phase shift improves the radar-based estimation of snowfall rate. • Snow estimators based on equivalent radar reflectivity factor can be corrupted by melting layer effects. • In snow conditions, strong horizontal winds can corrupt laser-optical disdrometer measurements. [ABSTRACT FROM AUTHOR]

The manuscript reviews the current literature on scattering applications of RADAR (Radio Detecting And Ranging) in remote sensing and diagnostics. This paper gives prime features for a variety of RADAR applications ranging from forest and climate monitoring to weather forecast, sea status, planetary information, and mapping of natural disasters such as the ones caused by earthquakes. Both the fundamental parameters involved in scattering mechanisms of RADAR applications and the factors affecting RADAR performances are also discussed. [ABSTRACT FROM AUTHOR]

Accurate observation of the high spatio‐temporal variability of rainfall is crucial for hydrometeorological applications. However, the existing observations from rain gauges and weather radars have individual shortcomings that can introduce considerable errors and uncertainties. A fairly new technique to get additional rainfall information is the usage of the country‐wide commercial microwave link (CML) networks for rainfall estimation by exploiting the measurements of rain‐induced attenuation along these CMLs. This technique has seen an increasing number of applications during the last years. Different methods have been developed to process the noisy raw data and to derive rainfall fields. It has been shown that CMLs can provide important line‐integrated rainfall information that complements pointwise rain gauge and spatial radar observations. There exist several limitations, though. Robustly dealing with the erratic fluctuations of the CML raw data is a challenge, in particular with the growing number of CMLs. How to correctly compensate for the biases from the effect of wet antenna attenuation for different CMLs also remains a crucial research question. Progress is additionally hampered by the lack of method intercomparisons, which in turn is hampered by restricted data sharing. Hence, collaboration is key for further advancements, also with regard to extended interaction with the CML network operators, which is a prerequisite to achieve increased data availability. In regions where rain gauges and weather radars are available, CMLs are a welcome complement. But in developing countries, which are characterized by weak technical infrastructure and which often suffer from water stress, additional rainfall information is a necessity. CMLs could play a crucial role in this respect. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > Water QualityScience of Water > Methods Attenuation data from commercial microwave links can provided valuable rainfall information. But there are several challenges along the way from raw data access to rainfall fields. [ABSTRACT FROM AUTHOR]