Radar Elevation Research Articles

RADAR Echo Recognition of Squall Line Based on Deep Learning

Squall line (SL) is a convective weather process that often causes disasters. The automatic recognition and early warning of SL are important objectives in the field of meteorology. By collecting the new-generation weather RADARs (CINRAD/SA and CINRAD/SAD) base data during 12 SL weather events occurred in Jiangsu, Shanghai, Shandong, Hebei, and other regions of China from 2019 to 2021, the dataset has a total of 49,920 samples with a window size of 40 km. The 40 km area was labeled by employing manual classification and data augmentation to construct the deep learning dataset with a positive and negative sample ratio of 1:1, of which 80% and 20% are separated as the training and test set, respectively. Based on the echo height of each elevation beam at different distances, three deep learning-based models are trained for SL automatic recognition, which include a near-distance model (M1) trained by the data in nine RADAR elevation angles within 45 km from RADARs, a mid-distance model (M2) by the data in six elevations from 45 to 135 km, and a far-distance model (M3) by the data in three elevations from 135 to 230 km. A confusion matrix and its derived metrics including receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) are introduced as the indicators to evaluate the models by the test dataset. The results indicate that the accuracy of models are over 86% with the hit rates over 87%, the false alarm rates less than 21%, and the critical success indexes (CSI) surpass 78%. All the optimal critical points on the ROC curves are close to (0, 1), and the AUC values are above 0.95, so the three models have high hit rates and low false alarm rates for ensuring SL discrimination. Finally, the effectiveness of the models is further demonstrated through two SL events detected with Nanjing, Yancheng and Qingpu RADARs.

Enhancing the Performance of Quantitative Precipitation Estimation Using Ensemble of Machine Learning Models Applied on Weather Radar Data

Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents one of the most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study is in line with the efforts to improve radar-based rainfall estimates through the use of machine learning techniques applied on radar data. With this aim, as a proof of concept, six machine learning models are evaluated to make estimations of the radar-based hourly accumulated rainfall using reflectivity data collected on the lowest radar elevation angles, and we employ a new data model for representing these radar data. The data were collected by a WSR-98D weather radar of the Romanian Meteorological Administration, located in the central region of Romania, during 30 non-consecutive days of the convective seasons, between 2016 and 2021. We obtained encouraging results using a stacked machine learning model. In terms of the Root Mean Squared Error evaluation metric, the results of the proposed stacked regressor are better than the radar estimated accumulated rainfall by about 33% and also outperform the baseline computed using the Z-R relationship by about 13%.

Zhongshan HF Radar Elevation Calibration Based on Ground Backscatter Echoes

The super dual auroral radar network (SuperDARN) is an important tool in the remote sensing of ionospheric potential convection in middle and high latitudes, and also a major source of elevation data detection. A reliable elevation angle helps estimate the propagation paths of high-frequency radio signals between scattering spots and radars, which is crucial for determining high-frequency radar target geolocation. The SuperDARN radar uses interferometry to estimate the elevation of the returned signal. However, elevation data are still underutilized owing to the difficulties of phase difference calibration induced by the propagation time delay between two arrays. This paper statistically analyzes the distribution features of the group range-elevation angle and group range-virtual height before and after calibration using elevation data from the ground backscatter echoes of the Zhongshan SuperDARN radar, calculating the root mean square error (RMSE) of the virtual height; the results show that the RMSE after calibration is mostly reduced to within 54% of that before calibration. Furthermore, we validate the calibration factor based on the primary phase data. The data from 2013 to 2015 indicate that this technique can be efficiently used to estimate the daily calibration factor. Finally, we present the statistical distribution of the calibration factor, which provides technical support for the calibration of elevation data in the future.

SCALING-UP DEEP LEARNING PREDICTIONS OF HYDROGRAPHY FROM IFSAR DATA IN ALASKA

Abstract. The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska.

Generation of the Forest Cover Map of the Qinghai–Tibet Plateau Based on the Multisource Dataset and Random Forest

The Qinghai–Tibet Plateau (QTP) plays a significant role in global climate change and biodiversity conservation. As the third pole of the Earth, it has a wide range and complex terrain. QTP has a vertical distribution of vegetation, and its forest ecosystems play a key role in the region. Forest extraction in this region is still a comprehensive problem because of the phenological periods of different forest types in distinct regions of the QTP and the characteristics of frequent rain and cloudy weather in the south. Taking these factors into consideration, multiple features, including reflectance, spectral indices, statistical backscattering coefficients, topographic slope, and aspect, derived from a multisource dataset incorporating optical remote sensing data, synthetic radar, and digital elevation models, were applied to extract forest in the QTP based on the random forest (RF) classification method. As more than 30 features were involved, the 5-folded cross-validation method was used to determine the optimal parameters and features for the RF model. Using 14,919 forest samples and a multifeature optimized RF classification model, a 10-m resolution forest cover map of QTP in 2021 was generated based on the Sentinel series of satellite datasets and digital elevation model datasets on the Google Earth Engine (GEE) platform. After verification, the overall accuracy of the forest cover map generated in this article is 98.3%, and the Kappa coefficient is 0.95, which is better than the European Space Agency (ESA) WorldCover forest layer.

Range-Ambiguous Clutter Suppression via FDA MIMO Planar Array Radar with Compressed Sensing

Range-ambiguous clutter is an inevitable issue for airborne forward-looking array radars, especially with the high pulse repetition frequency (PRF). In this paper, a method to suppress the range-ambiguous clutter is proposed in an FDA-MIMO radar with a forward-looking planar array. Compressed sensing FDA technology is used to suppress the range-ambiguous clutter and the forward-looking non-uniformity short-range clutter of radar. Specifically, first, the range ambiguous clutter in different regions is separated by the characteristics of the planar array radar elevation dimension and FDA radar range coupling. Meanwhile, regarding the issue of the FDA radar main lobe moving between coherent pulses, a main lobe correction (MLC) algorithm proposes a solution for the issue, where the FDA radar cannot coherently accumulate signals in the case of non-full angle illumination. Finally, compressed sensing technology and elevation dimension filtering are utilized to suppress the range ambiguous clutter at the receiver, with the approach alleviating the range dependence of clutter in the observation region. A small number of clutter snapshots can obtain an approximately ideal clutter covariance matrix through compressed sensing sparse recovery. The method not only reduces the number of training samples, but also overcomes the problem of clutter non-uniformity in the forward-looking array. Therefore, the clutter suppression problems faced by the high repetition frequency airborne radar forward-looking array structure are solved. At the analysis stage, a comparison among the conventional MIMO and FDA methods is carried on by analyzing the improvement factor (IF) curves. Numerical results verify the effectiveness of the proposed method in range-ambiguous clutter suppression.

Co-Registration Methods and Error Analysis for Four Decades (1979–2018) of Glacier Elevation Changes in the Southern Patagonian Icefield

The main goal of this paper is to compare two co-registration methods for geodetic mass balance (GMB) calculation in 28 glaciers making up the Upper Santa Cruz River basin, Southern Patagonian Icefield (SPI), from 1979 to 2018. For this purpose, geospatial data have been used as primary sources: Hexagon KH-9, ASTER, and LANDSAT optical images; SRTM digital radar elevation model; and ICESat elevation profiles. After the analyses, the two co-registration methods, namely M1, based on horizontal displacements and 3D shift vectors, and M2, based on three-dimensional transformations, turned out to be similar. The errors in the GMB were analyzed through a k index that considers, among other variables, the error in elevation change by testing four interpolation methods for filling gaps. We found that, in 63% of the cases, the relative error in elevation change contributes 90% or more to k index. The GMB throughout our study area reported that a loss value of −1.44 ± 0.15 m w. e. a−1 (−3.0 Gt a−1) and an ice thinning median of −1.38 ± 0.11 m a−1 occurred within the study period. The glaciers that showed the most negative GMB values were Upsala, with an annual elevation change median of −2.07 ± 0.18 m w. e. a−1, and Ameghino, with −2.31 ± 0.22 m w. e. a−1.

Simultaneous Observations of a Sporadic E Layer by Digisonde and SuperDARN HF Radars at Zhongshan, Antarctica

AbstractSporadic E (Es) layers could be composed of metallic ions and formed, modified, or transported by the action of convective electric fields in the high latitude ionosphere. In this paper, by utilizing simultaneous observations from Digisonde and Super Dual Auroral Radar Network (SuperDARN) HF radars at Zhongshan Station (ZHS, 69.4°S, 76.4°E), Antarctica, a thin Es layer, which initially formed in the lower F region and descended into the lower E region, with wavelike structures, was recorded by Digisonde on 14 November 2019. The Es layer‐related concurrent ionospheric irregularities were also detected by the SuperDARN ZHS HF radar. By using a global‐scale 2‐D convection map, combined with images from the Special Sensor Ultraviolet Spectrographic Imager instruments onboard Defense Meteorological Satellite Program (DMSP) spacecraft, it is proposed that the flow shears associated with the duskside convective circulation are responsible for the evolution of the Es layer. Moreover, using the HF radar elevation angle data to measure the scatter height, it is strongly suggested that the Es layer was elongated with convection circulation. The electrodynamic processes responsible for the formation and evolution of the Es layer are discussed.

The Influence of WSR-88D Intra-Volume Scanning Strategies on Thunderstorm Observations and Warnings in the Dual-Polarization Radar Era: 2011–20

Abstract The Weather Surveillance Radar-1988 Doppler (WSR-88D) network has undergone several improvements in the last decade with the upgrade to dual-polarization capabilities and the ability for forecasters to rescan the lowest levels of the atmosphere more frequently through the use of Supplemental Adaptive Intra-volume Scanning (SAILS). SAILS reduces the revisit period for scanning the lowest 1 km of the atmosphere but comes at the cost of a longer delay between scans at higher altitudes. This study quantifies how often radar volume coverage patterns (VCPs) and all available SAILS options are used during the issuance of 148 882 severe thunderstorm and 18 263 tornado warnings, and near 10 474 tornado, 58 934 hail, and 127 575 wind reports in the dual-polarization radar era. A large majority of warnings and storm reports were measured with a VCP providing denser low-level sampling coverage. More frequent low-level updates were employed near tornado warnings and reports compared to severe thunderstorm warnings and hail or wind hazards. Warnings issued near a radar providing three extra low-level scans (SAILSx3) were more likely to be verified by a hazard with a positive lead time than warnings with fewer low-level scans. However, extra low-level scans were more frequently used in environments supporting organized convection as shown using watches issued by the Storm Prediction Center. In recent years, the number of midlevel radar elevation scans is declining per hour, which can adversely affect the tracking of convective polarimetric signatures, like ZDR columns, which were found above the lowest elevation angle in over 99% of cases examined.

Characterizing slope correction methods applied to satellite radar altimetry data: A case study around Dome Argus in East Antarctica

Slope correction is important to improve the accuracy of satellite radar elevation measurements by mitigating the slope-induced error (SE), especially over uneven ground surfaces. Although several slope correction methods have been proposed, guidance in the form of stepwise algorithm on how to implement these methods in processing radar altimetric data at the coding level, and the differences among these methods need to be presented and discussed systematically. In this paper, three existing types of slope correction methods—the direct method (DM), intermediate method (IM), and relocation method (RM, further divided into RM1 and RM2)—are described in detail. In addition, their main differences and features for various scientific applications are analyzed. We conduct a systematic experiment with CryoSat-2 Low Resolution Mode (LRM) data in a physically stable area around Dome Argus in East Antarctica, where in-situ measurements were available for comparison. The slope correction is implemented separately using the three methods, with the latest high-accuracy Reference Elevation Model of Antarctica (REMA) as the a-priori topography model. The bias and precision of the slope-corrected CryoSat-2 data results from the RM2 is −0.18 ± 0.86 m based on the comparison with the field Global Navigation Satellite System (GNSS) data. The results from the RM2 indicate higher precision compared to those from the RM1. According to the correlation analysis of the slope-corrected CryoSat-2 data results (RM1 and RM2), the bias enlarges and the precision becomes worse when the surface slope increases from 0 to 0.85°. After a comprehensively comparative analysis, we find that the results from the RM1 and RM2 are superior in precision (0.93 m and 0.86 m) with respect to the GNSS data. The relatively low precision (1.22 m) from the IM is due to the potential error from the a-priori digital elevation model (DEM). The DM has the lowest precision (2.66 m). Another experiment over rough topography in West Antarctica is carried out for comparison, especially between the RM1 (precision of 15.27 m) and RM2 (precision of 16.25 m). In general, the RM is recommended for the SE elimination among the three methods. Moreover, the RM2 is firstly considered over smooth topography due to the superior performance in bias and precision, while the RM1 is more suggested over the rough topography because of the slightly smaller bias and better precision. The IM relies much on the accuracy of the a-prior DEM and is not usually recommended, because of the strict requirement in the sampling time between the radar altimetry data and the a-priori DEM to avoid any surface change over time.

A Novel Airborne Dual-Antenna InSAR Calibration Method for Backprojection Imaging Model

The interferometric synthetic aperture radar (InSAR) elevation inversion method based on the backprojection (BP) imaging model simplifies the interferometric processing chain where image registration and phase unwrapping are not required. Meanwhile, BP algorithm is suitable for radar with different imaging modes and geometry. However, the existing calibration methods are all aimed at traditional InSAR which adopts frequency domain imaging algorithm, but the calibration method for backprojection InSAR elevation inversion model has not been published. In this paper, we propose a novel InSAR calibration method which combines Fast Fourier Transform (FFT) estimation method with sensitivity equation method based on backprojection imaging model. FFT is used to process the interferometric phase which has removed the terrain phase of external digital elevation model (DEM) under BP imaging, and the estimation of interferometric parameters is realized through the phase fringe frequency. In the calibration method based on sensitivity equations, the deviations of interferometric parameters are calculated by solving sensitivity equations according to the three-dimensional information of ground control points. FFT estimation method not only generates the initial baseline parameters for sensitivity equation method, which is conducive to obtaining the global optimal solution, but also separates the calibration of phase offset and baseline parameters, which makes it possible to minimize the condition number of sensitivity matrix in sensitivity equation method. Finally, we obtain the calibrated interferometric parameters through iteration of sensitivity equation method and realize high accuracy DEM inversion. Both simulation experiment and airborne dual-antenna InSAR data processing are performed to verify the effectiveness of this method.

METHOD FOR SELECTING A CLASSIFIED TRAINING SAMPLE FOR RADAR’S ELEVATION CHANNELS

Context. Under conditions of simultaneous exposure to active and passive interference, the passive component decorrelates the active component of the combined interference in the receiving channels of the surveillance radar, which significantly reduces the effectiveness of its suppression. Therefore, in order to adapt the weight coefficients of the spatial filter during spatio-temporal processing of radar signals, it is necessary, by one method or another, to form a classified training sample generated only by active noise interference.Objective. The goal is to optimize the choice of a method for generating a classified training sample for various elevation channels of the radar field of view, taking into account the predicted distribution of passive interference in range.Method. An analytical assessment of the distribution of the most powerful Cb type cumulonimbus cloud systems, followed by mathematical modeling of the effectiveness of the active noise suppression using various training samples generation methods.Results. Based on the performed analytical calculations, it was shown that in the upper part of the field of view of surveillance radar in the sector of elevation angles from 6º to 30º, the upper boundary of Cb type clouds does not exceed 75 km, which guarantees the absence of passive interference at the end of the radar range. This makes it possible to form a training sample at a time interval located before the radiation of the next probe pulse. In the lower part of the field of view in the sector of angles 0º to 6º, the upper boundary of the clouds is located at a distance of 75–270 km, which does not allow the formation of a training sample at the end of the radar range. It is proposed to use the correlation method, in which the time interval for the formation of the training sample is determined by the “on the fly” analysis of the magnitude of the inter-channel correlation coefficient. The choice of the interval with the highest value of inter-channel correlation during the repetition period allows us to form a training sample with the least influence of passive interference on the process of suppressing the active component of the combined interference. As a result of modeling, it was found that the use of a training sample significantly reduces the duration of the transient process when forming the weight coefficients of the spatial filter and, accordingly, improves the quality of the suppression of active interference.Conclusions. The scientific novelty of the work lies in the fact that for the first time the problem of individual selection of a classified training sample for the surveillance radar elevation channels has been solved. Practical significance consists in the development of a methodology for analyzing the location of the most powerful cloud systems in range in the radar field of view with varying degrees of influence of passive interference in elevation channels in a real signal-noise environment. To the practical novelty should also include the results of simulation modeling, which confirm the need to choose a method for generating a classified training sample in different elevation channels of the radar viewing area should also be attributed.

Satellite Remote Sensing of the Greenland Ice Sheet Ablation Zone: A Review

The Greenland Ice Sheet is now the largest land ice contributor to global sea level rise, largely driven by increased surface meltwater runoff from the ablation zone, i.e., areas of the ice sheet where annual mass losses exceed gains. This small but critically important area of the ice sheet has expanded in size by ~50% since the early 1960s, and satellite remote sensing is a powerful tool for monitoring the physical processes that influence its surface mass balance. This review synthesizes key remote sensing methods and scientific findings from satellite remote sensing of the Greenland Ice Sheet ablation zone, covering progress in (1) radar altimetry, (2) laser (lidar) altimetry, (3) gravimetry, (4) multispectral optical imagery, and (5) microwave and thermal imagery. Physical characteristics and quantities examined include surface elevation change, gravimetric mass balance, reflectance, albedo, and mapping of surface melt extent and glaciological facies and zones. The review concludes that future progress will benefit most from methods that combine multi-sensor, multi-wavelength, and cross-platform datasets designed to discriminate the widely varying surface processes in the ablation zone. Specific examples include fusing laser altimetry, radar altimetry, and optical stereophotogrammetry to enhance spatial measurement density, cross-validate surface elevation change, and diagnose radar elevation bias; employing dual-frequency radar, microwave scatterometry, or combining radar and laser altimetry to map seasonal snow depth; fusing optical imagery, radar imagery, and microwave scatterometry to discriminate between snow, liquid water, refrozen meltwater, and bare ice near the equilibrium line altitude; combining optical reflectance with laser altimetry to map supraglacial lake, stream, and crevasse bathymetry; and monitoring the inland migration of snowlines, surface melt extent, and supraglacial hydrologic features.

Observational and Modeling Study of Ice Hydrometeor Radar Dual-Wavelength Ratios

AbstractThe influence of ice hydrometeor shape on the dual-wavelength ratio (DWR) of radar reflectivities at millimeter-wavelength frequencies is studied theoretically and on the basis of observations. Data from dual-frequency (Ka–W bands) radar show that, for vertically pointing measurements, DWR increasing trends with reflectivity Ze are very pronounced when Ka-band Ze is greater than about 0 dBZ and that DWR and Ze values are usually well correlated. This correlation is explained by strong relations between hydrometeor characteristic size and both of these radar variables. The observed DWR variability for a given level of reflectivity is as large as 8 dB, which is in part due to changes in mean hydrometeor shape as expressed in terms of the particle aspect ratio. Hydrometeors with a higher degree of nonsphericity exhibit lower DWR values when compared with quasi-spherical particles because of near-zenith reflectivity enhancements for particles outside the Rayleigh-scattering regime. When particle mass–size relations do not change significantly (e.g., for low-rime conditions), DWR can be used to differentiate between quasi-spherical and highly nonspherical hydrometeors because (for a given reflectivity value) DWR tends to increase as particles become more spherical. Another approach for differentiating among different degrees of nonsphericity for larger scatterers is based on analyzing DWR changes as a function of radar elevation angle. These changes are more pronounced for highly nonspherical particles and can exceed 10 dB. Measurements of snowfall spatiotemporally collocated with spaceborne CloudSat W-band radar and ground-based S-band operational weather radars also indicate that DWR values are generally smaller for ice hydrometeors with higher degrees of nonsphericity, which, for the same level of S-band reflectivity, exhibit greater differential reflectivity values.

Facing the peat CO2 threat: digital mapping of Indonesian peatlands\u2014a proposed methodology and its application

PurposeWe conducted this project to develop a feasible method for mapping tropical peat lands of Bengkalis Island—as a test site—in Indonesia.Materials and methodsThe method based on limited availability of field measurements and a wide range of remotely sensed spatial datasets like radar elevation product, MODIS, and Landsat imageries. We applied land use category based sampling to extend existing field data of peat thickness. New peat thickness data was collected by boring and simultaneous electrical resistivity tomography (ERT). Based on remotely sensed and field data sets, peat maps were compiled by simulated spatial annealing. Peat map statistics were derived after 500 runs including mean, median, minimum, maximum, and percentile values.Results and discussionThe resulted maps represent the limiting values of expected peat thickness using 90% confidence level. Results showed that ERT is suitable for determining peat layer thickness. Using independent samples, we found that peat thickness predictions tend to overestimate peat thickness by ca. 2 m in general.ConclusionsAccording to predictions, the peat volume of Bengkalis Island is estimated to be in the range of 3.28–3.58 km3.

Low angle estimation for MIMO radar with arbitrary array structures

ABSTRACTLow angle target estimation is always a difficult problem for radar elevation estimation. Multiple-input multiple-output (MIMO) radar can increase array degree of freedom and resolution, and improve the low angle target measurement precision. At the same time, the computation complexity of the MIMO radar is also increasing, especially for non-uniform linear array (ULA) configuration in which some fast algorithms cannot be used. In this paper, a fast low angle target estimation method is proposed for monostatic MIMO radar with arbitrary array configurations. With the array interpolation and the quasi-spatial-smoothing technique, the MIMO steering vector can be transformed into a virtual low dimension (typical similar size as the number of array elements) ULA steering vector. Then, the polynomial rooting algorithm is used to estimate the elevation of the low angle target. The simulation results of the proposed algorithm are presented and the performances are investigated and analysed.

Development of an Operational Convective Nowcasting Algorithm Using Raindrop Size Sorting Information from Polarimetric Radar Data

AbstractRaindrop size sorting is a ubiquitous microphysical occurrence in precipitating systems. Owing to the greater terminal fall speed of larger particles, a raindrop’s fall trajectory can be sensitive to its size, and strong air currents (e.g., a convective updraft) can enhance this sensitivity. Indeed, observational and numerical model simulation studies have confirmed these effects on raindrop size distributions near convective updrafts. One striking example is the lofting of liquid drops and partially frozen hydrometeors above the environmental 0°C level, resulting in a small columnar region of positive differential reflectivity ZDR in polarimetric radar data, known as the ZDR column. This signature can serve as a proxy for updraft location and strength, offering operational forecasters a tool for monitoring convective trends. Beneath the 0°C level, where WSR-88D spatiotemporal resolution is highest, anomalously high ZDR collocated with lower reflectivity factor at horizontal polarization ZH is often observed within and beneath convective updrafts. Here, size sorting creates a deficit in small drops, while relatively large drops and melting hydrometeors are present in low concentrations. As such, this unique raindrop size distribution and its related polarimetric signature can indicate updraft location sooner and more frequently than the detection of a ZDR column. This paper introduces a novel algorithm that capitalizes on the improved radar coverage at lower levels and automates the detection of this size sorting signature. At the algorithm core, unique ZH–ZDR relationships are created for each radar elevation scan, and positive ZDR outliers (often indicative of size sorting) are identified. Algorithm design, examples, performance, strengths and limitations, and future development are discussed.

우주감시레이다에 대한 지구 대기권 영향 분석 연구

In this study, both the altitude error due to the refraction and the range error due to the delay in the ionosphere with respect to the frequency are extracted according to the radar elevation to analyze the effect of atmosphere on the space surveillance radar. To achieve this, the radio refractivity profile is modeled using the measured data from domestic weather stations. Then, the altitude-error due to the refraction is extracted using the ray tracing method, and the range error in the ionosphere is extracted according to the frequency. Further, considerations for radar design with respect to the radar error characteristics are discussed based on the abroad space surveillance radar and proposed domestic space surveillance radar. This analysis of the error characteristics is expected to be utilized for the determination of radar location, range of steering, and frequency in the space surveillance radar design.

Implications of drainage rearrangement for passive margin escarpment evolution in southern Brazil

Although several authors have pointed out the importance of earth surface process to passive margin escarpments relief evolution and even drainage rearrangements, the dynamics of a consolidated capture area (after a drainage network erodes the escarpment, as the one from the Itajaí-Açu River) remain poorly understood. Here, results are presented from radar elevation and aerial imagery data coupled with in-situ-produced 10Be concentrations measured in sand-sized river-born sediments from the Serra Geral escarpment, southern Brazil. The Studied area's relief evolution is captained by the drainage network: while the Itajaí-Açu watershed relief is the most dissected and lowest in elevation, it is significantly less dissected in the intermediate elevation Iguaçu catchment, an important Paraná River tributary. These less dissected and topographically higher areas belong to the Uruguai River catchment. These differences are conditioned by (i) different lithology compositions, structures and genesis; (ii) different morphological configurations, notably slope, range, relief; and (iii) different regional base levels. Along the Serra Geral escarpment, drainage features such as elbows, underfitted valleys, river profile anomalies, and contrasts in mapped χ-values are evidence of the rearrangement process, mainly beheading, where ocean-facing tributaries of the Itajaí-Açu River capture the inland catchments (Iguaçu and Uruguai). The 10Be derived denudation rates reinforced such processes: while samples from the Caçador and Araucárias Plateaus yield weighted means of 3.1 ± 0.2 and 6.5 ± 0.4 m/Ma respectively, samples from along the escarpment yield a weighted mean of 46.8 ± 3.6 m/Ma, almost 8 times higher. Such significant denudation rate differences are explained by base-level control, relief characteristics, and the geology framework. The main regional morphological evolutionary mechanism is headward denudation and piracy by the Itajaí-Açu River tributaries. As the escarpment moves from east to west, Itajaí-Açu River tributaries develop, leading to regional relief lowering and area losses within the Iguaçu and Uruguai catchments. Such processes were accelerated since Itajaí-Açu tributaries reached into sedimentary and volcanic rocks. From this moment on, Serra Geral became the main hydrographic divide between the ocean- and inland facing-catchments in the area.

Winter Precipitation Liquid–Ice Phase Transitions Revealed with Polarimetric Radar and 2DVD Observations in Central Oklahoma

AbstractObservations and analysis of an ice–liquid phase precipitation event, collected with an S-band polarimetric KOUN radar and a two-dimensional video disdrometer (2DVD) in central Oklahoma on 20 January 2007, are presented. Using the disdrometer measurements, precipitation is classified either as ice pellets or rain/freezing rain. The disdrometer observations showed fast-falling and slow-falling particles of similar size. The vast majority (>99%) were fast falling with observed velocities close to those of raindrops with similar sizes. In contrast to the smaller particles (<1 mm in diameter), bigger ice pellets (>1.5 mm) were relatively easy to distinguish because their shapes differ from the raindrops. The ice pellets were challenging to detect by looking at conventional polarimetric radar data because of the localized and patchy nature of the ice phase and their occurrence close to the ground. Previously published findings referred to cases in which ice pellet areas were centered on the radar location and showed a ringlike structure of enhanced differential reflectivity ZDR and reduced copolar correlation coefficient ρhv and horizontal reflectivity ZH in PPI images. In this study, a new, unconventional way of looking at polarimetric radar data is introduced: slanted vertical profiles (SVPs) at low (0°–1°) radar elevations. From the analysis of the localized and patchy structures using SVPs, the polarimetric refreezing signature, reflected in local enhancement in ZDR and reduction in ZH and ρhv, became much more evident. Model simulations of sequential drop freezing using Marshall–Palmer DSDs along with the observations suggest that preferential freezing of small drops may be responsible for the refreezing polarimetric signature, as suggested in previous studies.