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A robust and computationally efficient object tracking algorithm is developed by incorporating various tracking techniques. Physical properties of the objects, such as brightness temperature or reflectivity, are not considered. Therefore, the algorithm is adaptable for tracking convection-like features in simulated data and remotely sensed two-dimensional images. In this algorithm, a first guess of the motion, estimated using the Fourier phase shift, is used to predict the candidates for matching. A disparity score is computed for each target–candidate pair. The disparity also incorporates overlapping criteria in the case of large objects. Then the Hungarian method is applied to identify the best pairs by minimizing the global disparity. The high-disparity pairs are unmatched, and their target and candidate are declared expired and newly initiated objects, respectively. They are tested for merger and split on the basis of their size and overlap with the other objects. The sensitivity of track duration is shown for different disparity and size thresholds. The paper highlights the algorithm's ability to study convective life cycles using radar and simulated data over Darwin, Australia. The algorithm skillfully tracks individual convective cells (a few pixels in size) and large convective systems. The duration of tracks and cell size are found to be lognormally distributed over Darwin. The evolution of size and precipitation types of isolated convective cells is presented in the Lagrangian perspective. This algorithm is part of a vision for a modular platform [viz., TINT is not TITAN (TINT) and Tracking and Object-Based Analysis of Clouds (tobac)] that will evolve into a sustainable choice to analyze atmospheric features. [ABSTRACT FROM AUTHOR]

Irrigation can be responsible for salt accumulation in the root zone of grapevines when late autumn and winter precipitation is not enough to leach salts from the soil upper horizons, turning the soil unsuitable for grape production. The aim of this work is to present a novel methodology to outline areas, within a drip-irrigated vineyard, with a low soil moisture content (SMC) during, and after, an 11-month agricultural drought. Soil moisture (SM) field measurements were performed in two plots at the vineyard, followed by a geostatistical method (indicator kriging) to estimate the SM class probabilities according to a threshold value, enlarging the training set for the classification algorithms. The logistic regression (LR) and Random Forest (RF) methods used the features of the Sentinel-1 and Sentinel-2 images and terrain parameters to classify the SMC probabilities at the vineyard. Both methods classified the highest SMC probabilities above 14% that is located close to the stream at the lower altitudes. The RF method performed very well in classifying the topsoil zones with a lower SMC during the autumn-winter period. This delineation allows the prevention of the occurrence of areas affected by salinisation, indicating which areas will need irrigation management strategies to control the salinity, especially under climate change, and the expected increase in droughts. [ABSTRACT FROM AUTHOR]

A method based on machine learning and image processing techniques has been developed to track the surface expression of internal waves in near–real time. X-band radar scans are first preprocessed and averaged to suppress surface wave clutter and enhance the signal-to-noise ratio of persistent backscatter features driven by gradients in surface currents. A machine learning algorithm utilizing a support vector machine (SVM) model is then used to classify whether or not the image contains an internal solitary wave (ISW) or internal tide bore (bore). The use of machine learning is found to allow rapid assessment of the large dataset, and provides insight on characterizing optimal environmental conditions to allow for radar illumination and detection of ISWs and bores. Radon transforms and local maxima detections are used to locate these features within images that are determined to contain an ISW or bore. The resulting time series of locations is used to create a map of propagation speed and direction that captures the spatiotemporal variability of the ISW or bore in the coastal environment. This technique is applied to 2 months of data collected near Point Sal, California, and captures ISW and bore propagation speed and direction information that currently cannot be measured with instruments such as moorings and synthetic aperture radar (SAR). [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]

With the launch of the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) in 2014, renewed interest in retrievals of snowfall in the atmospheric column has occurred. The current operational GPM-DPR retrieval largely underestimates surface snowfall accumulation. Here, a neural network (NN) trained on data that are synthetically derived from state-of-the-art ice particle scattering models and measured in situ particle size distributions (PSDs) is used to retrieve two parameters of the PSD: liquid equivalent mass-weighted mean diameter D m l and the liquid equivalent normalized intercept parameter N w l . Evaluations against a test dataset showed statistically significantly improved ice water content (IWC) retrievals relative to a standard power-law approach and an estimate of the current GPM-DPR algorithm. Furthermore, estimated median percent errors (MPE) on the test dataset were −0.7%, +2.6%, and +1% for D m l , N w l , and IWC, respectively. An evaluation on three case studies with collocated radar observations and in situ microphysical data shows that the NN retrieval has MPE of −13%, +120%, and +10% for D m l , N w l , and IWC, respectively. The NN retrieval applied directly to GPM-DPR data provides improved snowfall retrievals relative to the default algorithm, removing the default algorithm's ray-to-ray instabilities and recreating the high-resolution radar retrieval results to within 15% MPE. Future work should aim to improve the retrieval by including PSD data collected in more diverse conditions and rimed particles. Furthermore, different desired outputs such as the PSD shape parameter and snowfall rate could be included in future iterations. [ABSTRACT FROM AUTHOR]

Two power-law relations linking equivalent radar reflectivity factor Ze and snowfall rate S are derived for a K-band Micro Rain Radar (MRR) and for a W-band cloud radar. For the development of these Ze–S relationships, a dataset of calculated and measured variables is used. Surface-based video-disdrometer measurements were collected during snowfall events over five winters at the high-latitude site in Hyytiälä, Finland. The data from 2014 to 2018 include particle size distributions (PSD) and their fall velocities, from which snowflake masses were derived. The K- and W-band Ze values are computed using these surface-based observations and snowflake scattering properties as provided by T-matrix and single-particle scattering tables, respectively. The uncertainty analysis shows that the K-band snowfall-rate estimation is significantly improved by including the intercept parameter N0 of the PSD calculated from concurrent disdrometer measurements. If N0 is used to adjust the prefactor of the Ze–S relationship, the RMSE of the snowfall-rate estimate can be reduced from 0.37 to around 0.11 mm h−1. For W-band radar, a Ze–S relationship with constant parameters for all available snow events shows a similar uncertainty when compared with the method that includes the PSD intercept parameter. To demonstrate the performance of the proposed Ze–S relationships, they are applied to measurements of the MRR and the W-band microwave radar for Arctic clouds at the Arctic research base operated by the German Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) and the French Polar Institute Paul Emile Victor (IPEV) (AWIPEV) in Ny-Ålesund, Svalbard, Norway. The resulting snowfall-rate estimates show good agreement with in situ snowfall observations while other Ze–S relationships from literature reveal larger differences. [ABSTRACT FROM AUTHOR]

A spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. This study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking, and collating hailstone trajectories using dual-polarized, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initializing the model with radar retrievals of hail differential reflectivity HDR was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia, demonstrated that trajectory modeling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented, such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10–15 min) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk. [ABSTRACT FROM AUTHOR]

Several studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements from the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) to validate several passive microwave approaches. We assess the retrievals on the basis of how tightly they constrain the radar reflectivity at −20°C and how this measured radar reflectivity aloft varies geographically. The algorithm that combines minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly and gives the least appearance of regional biases. A retrieval that is based on a 19-GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally adjusted minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the minimum 37-GHz PCT (≤230 K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high-latitude, and high-terrain land profiles had the slowest decreases of reflectivity with height. [ABSTRACT FROM AUTHOR]

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014-18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates thanDPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1mmh-1 and underestimation when gauge estimates are larger than 1mmh-1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5mmh-1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS's performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates. [ABSTRACT FROM AUTHOR]

We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds. [ABSTRACT FROM AUTHOR]

In this article, a theoretical study is carried out to seek an approach to remotely measure the thickness of the oil film. It is known that it is difficult to probe the oil thickness using a microwave radar due to the fact the dielectric constant of oil is very small compared with seawater for microwave frequencies. While in the range of terahertz (THz) electromagnetic (EM) wave frequency, the dielectric constants of oil and seawater are comparable, resulting in the THz EM fields scattered from the air–oil interface and those from the oil–water interface can produce constructive and destructive interference with the variation in the oil film thickness. Based on this principle, the sensitivity of the THz EM wave to the oil thickness is investigated in this work. First, the oil–seawater two-layer medium is equivalent to a single-layer medium by an equivalent dielectric constant model. Then, the THz EM scattering from the oil-covered sea surface is simulated using the first-order small slope approximation method (SSA-1) and the obtained equivalent dielectric constant. Meanwhile, the influences caused by the damping effect of the oil film, the reduction for friction velocity, and the change in equivalent permittivity on the normalized radar cross section (NRCS) are analyzed. The numerical results show that the NRCSs of THz frequencies are more sensitive to the change in oil thickness than that of microwave frequencies. This property makes the THz EM wave have the potential to probe the thickness of the oil film. [ABSTRACT FROM AUTHOR]

In this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques have displayed impressive success in many practical fields. Deep neural networks owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of a large labeled dataset, presence of noise in the data or missing data. In our radar data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electro-optical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we show that selecting the right model and training it on the radar imagery from the start yields far better results. [ABSTRACT FROM AUTHOR]

The sea surface velocity measurements obtained during the period of 2014–2016 using two high-frequency radars (HFR), which were installed on the northern coast of Vietnam, were reprocessed by using a variational approach (EOF/2dVar) to provide an extended dataset. The high temporal resolution of measurements and large spatial coverage of the radar data enabled the assessment of the surface circulation in the data-poor region of the southern Gulf of Tonkin (GoT) and the characterization of its temporal variations in a wide range of scales: from tidal frequency to annual cycle. It was found that tidal motions account for a large percentage (approximately 60%) of surface current variability in the GoT. The temporal variability of the kinetic energy of the surface currents is tightly correlated with wind variations in the monsoon-dominated atmospheric circulation system. Stratification, induced by the discharge of the Red River, also affects the tidal currents within the coastal region (~ 40 km wide) by modifying the tidal ellipses polarizations. Close to the shore, the prominent coastal flow experiences large temporal variations in response to wind and buoyancy forcing. The seasonal variability of the zonal wind component was found to play a significant role in coastal flow speed variations through the cross-shore mass exchange. The Red River freshwater discharge controls the seaward extension of the coastal flow and causes asymmetry in the seasonal variability of flow velocity. The data obtained and analysis results could be useful for the improvement of the regional circulation models, for further studies of the physical and biological processes in the GoT, and for marine resource management. [ABSTRACT FROM AUTHOR]

VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated continuous-wave (FMCW) radar mode, offering a transmit power of 200–300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167–174.8 GHz results in a nominal range resolution of 15 m. The radar’s measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as −26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR’s absolute sensitivity was validated using a spherical metal target in the radar antenna’s far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver. [ABSTRACT FROM AUTHOR]

A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a). [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 nonstationary clutter is one of the biggest challenge to ocean remote sensing radar system such as high frequency (HF) mixed-mode surface wave radar. The performance of space-time adaptive processing (STAP) degrades badly with limited homogeneous secondary training data support. Single dataset algorithms overcome the problem by working on primary data solely. But the heavy computational complexity as well as the inaccurate estimation of the clutter covariance matrix restricts the practical application of these methods. In this letter, we propose a novel reduce-rank-based single dataset STAP to suppress the nonhomogeneous clutter in practical HF radar system. A fast implementation of subspace tracking algorithm is introduced to estimate the clutter subspace as well as reduce the computational cost via pulse iteration method. The effectiveness of the proposed method is verified by both simulated and experimental data. The results show it outperforms traditional single dataset STAP methods and the nonstationary clutter can be greatly suppressed. [ABSTRACT FROM AUTHOR]

Partial beam blockage (PBB) correction is an indispensable step in weather radar data quality control and subsequent quantitative applications, such as precipitation estimation, especially in urban and/or complex terrain environments. This paper developed a novel PBB correction procedure based on the improved ZPHI method for attenuation correction and regional specific differential propagation phase (KDP)–reflectivity (ZH) relationship derived from in situ raindrop size distribution (DSD) measurements. The practical performance of this PBB correction technique was evaluated through comparing the spatial continuity of reflectivity measurements, the consistency between radar-measured and DSD-derived KDP and ZH relationships, as well as rainfall estimates based on R(ZH) and R(KDP). The results showed that through incorporating attenuation and PBB corrections (i) the spatial continuity of ZH measurements can effectively be enhanced; (ii) the distribution of radar-measured KDP versus ZH is more consistent with the DSD-derived KDP versus ZH; (iii) the measured ZH from a C-band radar in the PBB-affected area becomes more consistent with collocated S-band measurements, particularly in the rainstorm center area where ZH is larger than 30 dBZ; and (iv) rainfall estimates based on R(ZH) in the PBB-affected area are incrementally improved with better spatial continuity and the performance tends to be more comparable with R(KDP). [ABSTRACT FROM AUTHOR]

Possibilities of using radar polarimetry methods for identifying landslide zones are analyzed. The transformation of the dominant mechanism of signal scattering by the reflecting surface was used as a key feature of landslide zones. The polarimetric data from the PALSAR-2 radar of the ALOS-2 satellite are processed using the Freeman–Durden and Cloude–Pottier decompositions at four test sites selected in the region of the landslide caused by the collapse of the bank of the Bureya River. It is found that the results of decompositions are consistent with each other; however, in some areas there are significant differences due to the specific features of the basic model assumptions. It is shown that, before the descent of the landslide masses, three main mechanisms of radar signal scattering existed in the analyzed region: single surface, volumetric, and double scattering. After the collapse, this area was dominated by single scattering characteristic of the reflective surface with large-scale irregularities free of vegetation, due to which the landslide descent zone can be confidently recognized. The significant potential of using radar polarimetry for remote diagnostics of the consequences of landslide phenomena has been demonstrated. [ABSTRACT FROM AUTHOR]

The primary objective of these studies was to quantitatively assess the ground deformation velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried Baku-Tbilisi-Ceyhan Oil, South Caucasus Gas, Western Route Oil and South Caucasus Pipeline Expansion Gas pipelines in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. PS-InSAR analysis showed that the continuous subsidence was prevailing in the kilometer range of 13-70 of pipelines crossing two active seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0-13. The minimum and maximum vertical ground movement velocities were observed to be −21.3 mm/y and 14.1 mm/y along 70 km section of pipelines with 250 m buffer zone. Both of these sites were observed at the range of seismic faults. The spatial distribution of sites with ground deformation velocity less than −15 mm/y and more than 15 mm/y was diverse and random all along 70 km of pipelines without any cumulative spatial patterns. Based on the lower mean, variation and standard deviation of pixel values, the seismic fault in the kilometer range of 21-31 revealed its higher vulnerability to subsidence processes rather than the Seismic Fault in the kilometer range of 46-54. The ground deformation velocities within the range of Seismic Fault KP21-31 revealed the minimum and maximum values of −19.74 mm/y and 14.1 mm/y, respectively whereas at the Seismic Fault KP46-54, the minimum and maximum values were −17.07 mm/y and 9.29 mm/y, respectively. Encouraging level of agreement with the regression coefficients of 0.92 and 0.96 for known subsiding sites at KP28 + 500 and KP52 + 750 and 0.97 and 0.96 for known uplifting sites at KP04 + 900 and KP35 + 050 respectively was observed between the high-precision GPS and PS-InSAR measurements. The diverse spatial distribution and variation of ground movement processes along pipelines demonstrated that general geological and geotechnical understanding of the study area is not sufficient to find and mitigate all the critical sites of subsidence and uplifts for the pipeline operators. The prediction of the potential subsidence or uplift locations based on the field visual verifications holds a lot of uncertainties without broad and detailed scale airborne and satellite space observation technologies. The justification of the budget for the geotechnical maintenance activities along long-range oil and gas pipelines requires sophisticated prioritization and planning of the remediation sites and clear quantitative and qualitative risk assessment proving the activeness of these sites and effectiveness of the remediation measures. This means that the PS-InSAR – based approach outlined in this paper is a significant improvement over current ground-based monitoring practices or can significantly contribute them in the initial phase of risk assessment and prioritization. [ABSTRACT FROM AUTHOR]

Terrestrial simulations for crewed missions are critically important for testing technologies and improving methods and procedures for future robotic and human planetary exploration. In February 2018, AMADEE-18 simulated a mission to Mars in the Dhofar region of Oman. During the mission, a field crew coordinated by the Österreichisches Weltraum Forum (OeWF) accomplished several experiments in the fields of astrobiology, space physiology and medicine, geology, and geophysics. Within the scientific payload of AMADEE-18, ScanMars provided geophysical radar imaging of the subsurface at the simulated landing site and was operated by analog astronauts wearing spacesuits during extra-vehicular activities. The analog astronauts were trained to operate a ground-penetrating radar instrument that transmits and then collects radio waves carrying information about the geological setting of the first few meters of the subsurface. The data presented in this work show signal returns from structures down to 4 m depth, associated with the geology of the investigated rocks. Integrating radar data and the analog astronauts' observations of the geology at the surface, it was possible to identify the contact between shallow sediments and bedrock, the local occurrence of conductive soils, and the presence of pebbly materials in the shallow subsurface, which together describe the geology of recent loose sediments overlying an older deformed bedrock. The results obtained by ScanMars confirm that subsurface radar sounding at martian landing sites is key for the geological characterization at shallow depths. The geologic model of the subsurface can be used as the basis for reconstructing palaeoenvironments and paleo-habitats, thus assisting scientific investigations looking for traces of present or past life on the Red Planet. Highlights: The ScanMars experiment brings a ground-penetrating radar to the AMADEE-18 simulated Mars mission. The ScanMars radar was operated following procedures and training developed before the mission. Approximately 2000 m of radar data profiles have been acquired during the analog mission. Combining the results for ScanMars, orbital remote sensing data, and first-person observation in the field while wearing spacesuits (analog astronauts), it was possible to generate a geological model at the AMADEE-18 study site. [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]

This paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivity Z between −30 and 0 dBZ and all values of lidar backscatter β observations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold of Z as in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path. [ABSTRACT FROM AUTHOR]

High-resolution boundary layer water vapor profile observations are essential for understanding the interplay between shallow convection, cloudiness, and climate in the trade wind atmosphere. As current observation techniques can be limited by low spatial or temporal resolution, the synergistic benefit of combining ground-based microwave radiometer (MWR) and dual-frequency radar is investigated by analyzing the retrieval information content and uncertainty. Synthetic MWR brightness temperatures, as well as simulated dual-wavelength ratios of two radar frequencies are generated for a combination of Ka and W band (KaW), as well as differential absorption radar (DAR) G-band frequencies (167 and 174.8 GHz, G2). The synergy analysis is based on an optimal estimation scheme by varying the configuration of the observation vector. Combining MWR and KaW only marginally increases the retrieval information content. The synergy of MWR with G2 radar is more beneficial due to increasing degrees of freedom (4.5), decreasing retrieval errors, and a more realistic retrieved profile within the cloud layer. The information and profile below and within the cloud is driven by the radar observations, whereas the synergistic benefit is largest above the cloud layer, where information content is enhanced compared to an MWR-only or DAR-only setup. For full synergistic benefits, however, G-band radar sensitivities need to allow full-cloud profiling; in this case, the results suggest that a combined retrieval of MWR and G-band DAR can help close the observational gap of current techniques. [ABSTRACT FROM AUTHOR]

Recent developments in electronics have pushed miniaturized satellites to the femto-scale, with masses between 10 and 100 g. Although femtosatellites have been proven as a feasible concept, most designs are limited in mission capacity and lifetime due to the lack of environmental protection and onboard propellant. In this article, a novel concept for femtosatellites for earth remote sensing is proposed. In particular, a swarm of femtosatellites are used as elements of a sparse array in orbit to receive radar echoes. They also feature active orbit control enabled by solar radiation pressure to extend their lifetime. A simple active orbit control algorithm has been demonstrated. A mission concept based on a sun-synchronous circular orbit is proposed to maximize the benefit for both earth remote sensing and active orbit control. A synthetic aperture radar mission has been used to characterize their performance. [ABSTRACT FROM AUTHOR]

A cokriging model using three variables is developed to estimate bare-surface soil moisture content based on multi-temporal synthetic-aperture radar (SAR) data. This model utilizes cross-semivariogram function to take into account spatially varied correlation among multiple variables. Here, five sentinel-1 SAR scenes were acquired on different dates using the interferometric wide-swath (IW) mode and a mean incidence angle of 39.02° to build the backscatter temporal-ratio in VV polarization. This algorithm is generally based on the assumption of contributions of soil moisture and surface roughness to the backscattering coefficient under the given radar configurations. In this study, soil moisture is the target variable, and the surface roughness and backscatter temporal-ratio in VV polarization are the auxiliary variables. A cross-semivariogram relationship is formulated among those three spatial variables; then ordinary cokriging is used, based on that cross-semivariogram formula, to estimate the spatial distribution of bare soil moisture content. The root mean square error (RMSE) of soil-moisture retrieval ranges from 2.62 to 2.66 vol%. The new empirical model described in this paper will provide new insights into the study of soil environments. [ABSTRACT FROM AUTHOR]

Remote sensing observations at sub-millimeter wavelengths provide higher sensitivity to small hydrometeors and low water content than observations at millimeter wavelengths, which are traditionally used to observe clouds and precipitation. They are employed increasingly in field campaigns to study cloud microphysics and will be integrated into the global meteorological observing system to measure the global distribution of ice in the atmosphere with the launch of the Ice Cloud Imager (ICI) radiometer on board the second generation of European operational meteorological satellites (Metop-SG). Observations at these novel wavelengths provide valuable information not only on their own but also in combination with complementary observations at other wavelengths. This study investigates the potential of combining passive sub-millimeter radiometer observations with a hypothetical W-band cloud radar for the retrieval of frozen hydrometeors. An idealized cloud model is used to investigate the information content of the combined observations and establish their capacity to constrain the microphysical properties of ice hydrometeors. A synergistic retrieval algorithm for airborne observations is proposed and applied to simulated observations from a cloud-resolving model. Results from the synergistic retrieval are compared to equivalent radar- and passive-only implementations in order to assess the benefits of the synergistic sensor configuration. The impact of the assumed ice particle shape on the retrieval results is assessed for all retrieval implementations. We find that the combined observations better constrain the microphysical properties of ice hydrometeors, which reduces uncertainties in retrieved ice water content and particle number concentrations for suitable choices of the ice particle model. Analysis of the retrieval information content shows that, although the radar contributes the largest part of the information in the combined retrieval, the radiometer observations provide complementary information over a wide range of atmospheric states. Furthermore, the combined observations yield slightly improved retrievals of liquid cloud water in mixed-phase clouds, pointing towards another potential application of combined radar–radiometer observations. [ABSTRACT FROM AUTHOR]

This article describes a four-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.pangaea.de/10.1594/PANGAEA.918315, Gehring et al. (2020a)). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85% of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before. [ABSTRACT FROM AUTHOR]

A strong windthrow affected many forests in Slovenia and northern Italy on the night of the 29th and the 30th of October, 2018. In this paper, we propose a new methodology for windthrow identification, comprising two new indexes, PCA-CI for evaluating change detection and PCA-WI for the identification of windthrows by analysing the time series of C-SAR Sentinel-1 data. The proposed methodology, unlike the previous studies on the identification of windthrows, does not subtract the values of dual and single polarised VH-VV data combinations, but proposes a new approach with a multivariate transformation of dimensionality reduction of the time series of the dual and single polarised data in two different time frames. [ABSTRACT FROM AUTHOR]

Based on unprecedented 22‐year records from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR), this study aims to investigate the global distribution and linkage between both convections and precipitation extremes quasi‐globally. Overall, the spatial distributions of extreme events show significant regional variability and land‐ocean contrast. Extreme precipitation is dominant over tropical ocean while extreme convections are exclusive over land from tropics to higher latitude. Besides, there are strong positive relationships between extreme convections and precipitation based on multiple threshold‐based definitions of extremes, which provides more consistent physical properties of extreme events globally. The correlation is significant over land but relatively weak over oceans. Seasonal occurrences of extreme events are also examined. Furthermore, extreme events exhibit notable diurnal characteristics over global land with most extreme convections occurring in the afternoon, while diurnal cycles are more homogeneous over ocean. Plain Language Summary: Extreme rainfall and convection events are of significant social interest due to their close linkage with a variety of natural hazards, while the two types of events are used too interchangeably in literature. To document the spatial variability and linkage between extreme convections and extreme precipitation, we use 22‐year records from the precipitation radar onboard Tropical Rainfall Measuring Mission satellite and the dual‐frequency precipitation radar onboard Global Precipitation Measurement satellite. Threshold‐based definitions of extremes are used. Results show that the correlation between extreme convections and precipitation is strong over land, while ocean region shows a little weaker association from tropics to higher latitudes. The occurrences of extreme events show notable seasonal variations and exhibit a significant peak in the afternoon. Key Points: The spatial distributions of extreme events show significantly regional variability and land‐ocean contrastExtreme precipitation events have highly positive correlation with extreme convections particularly in land regionsExtreme convections exhibit substantial afternoon peaks over land, while extreme precipitation exhibit more homogenous diurnal cycles [ABSTRACT FROM AUTHOR]

For an unmanned aerial vehicle (UAV) carrying out a maritime radar surveillance mission, there is a tradeoff between maximizing information obtained from the search area and minimizing fuel consumption. This article presents an approach for the optimization of a UAV's trajectory for maritime radar wide area persistent surveillance to simultaneously minimize fuel consumption, maximize mean probability of detection, and minimize mean revisit time. Quintic polynomials are used to generate UAV trajectories due to their ability to provide complete and complex solutions while requiring few inputs. Furthermore, the UAV dynamics and surveillance mission requirements are used to ensure that a trajectory is realistic and mission compatible. A wide area search radar model is used within this article in conjunction with a discretized grid in order to determine the search area's mean probability of detection and mean revisit time. The trajectory generation method is then used in conjunction with a multiobjective particle swarm optimization algorithm to obtain a global optimum in terms of path, airspeed (and thus time), and altitude. The performance of the approach is then tested over two common maritime surveillance scenarios and compared to an industry recommended baseline. [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]

In remote sensing, micro-Doppler signatures are widely used in moving target detection and automatic target recognition. However, since Doppler signatures are easily affected by the moving direction of the target, prior information of aspect angle is essential for spectral analysis. Thus, a micro-Doppler-based classifier is considered to be “angle-sensitive.” In this article, we propose an angle-insensitive classifier for the omnidirectional classification problem using the monostatic radar through a proposed new convolutional neural network. We further provide a sensible definition of “angle sensitivity,” and perform experiments on two data sets obtained through simulations and measurements. The results demonstrate that the proposed algorithm outperforms both feature-based and existing deep-learning-based counterparts, and resolve the issue of angle sensitivity in micro-Doppler-based classification. [ABSTRACT FROM AUTHOR]

The morning of November 23, 2013, a lava fountain formed from the New South-East Crater (NSEC) of Mt. Etna (Italy), one of the most active volcanoes in Europe. The explosive activity was observed from two ground-based radars, the X-band polarimetric scanning and the L-band Doppler fixed-pointing, as well as from a thermal-infrared camera. Taking advantage of the capability of the microwave radars to probe the volcanic plume and extending the volcanic ash radar retrieval (VARR) methodology, we estimate the mass eruption rate (MER) using three main techniques, namely surface-flux approach (SFA), mass continuity-based approach (MCA), and top-plume approach (TPA), as well as provide a quantitative evaluation of their uncertainty. Estimated exit velocities are between 160 and 230 m/s in the paroxysmal phase. The intercomparison between the SFA, MCA, and TPA methods, in terms of retrieved MER, shows a fairly good consistency with values up to 2.4 × 106 kg/s. The estimated total erupted mass (TEM) is 3.8 × 109, 3.9 × 109, and 4.7 × 109 kg for SFA with L-band, X-band, and thermal-infrared camera, respectively. Estimated TEM is between 1.7 × 109 kg and 4.3 × 109 for TPA methods and 3.9 ×109 kg for the MCA technique. The SFA, MCA, and TPA results for TEM are in fairly good agreement with independent evaluations derived from ground collection of tephra deposit and estimated to be between 1.3 ± 1.1 × 109 and 5.7 × 109 kg. This article shows that complementary strategies of ground-based remote sensing systems can provide an accurate real-time monitoring of a volcanic explosive activity. [ABSTRACT FROM AUTHOR]

Despite widespread use of radio-echo sounding (RES) in glaciology and broad distribution of processed radar products, the glaciological community has no standard software for processing impulse RES data. Dependable, fast and collection-system/platform-independent processing flows could facilitate comparison between datasets and allow full utilization of large impulse RES data archives and new data. Here, we present ImpDAR, an open-source, cross-platform, impulse radar processor and interpreter, written primarily in Python. The utility of this software lies in its collection of established tools into a single, open-source framework. ImpDAR aims to provide a versatile standard that is accessible to radar-processing novices and useful to specialists. It can read data from common commercial ground-penetrating radars (GPRs) and some custom-built RES systems. It performs all the standard processing steps, including bandpass and horizontal filtering, time correction for antenna spacing, geolocation and migration. After processing data, ImpDAR's interpreter includes several plotting functions, digitization of reflecting horizons, calculation of reflector strength and export of interpreted layers. We demonstrate these capabilities on two datasets: deep (~3000 m depth) data collected with a custom (3 MHz) system in northeast Greenland and shallow (<100 m depth, 500 MHz) data collected with a commercial GPR on South Cascade Glacier in Washington. [ABSTRACT FROM AUTHOR]

Since the year 2000, Greenland ice sheet mass loss has been dominated by a decrease in surface mass balance rather than an increase in solid ice discharge. Southeast Greenland is an important region to understand how high accumulation rates can offset increasing Greenland ice sheet meltwater runoff. To that end, we derive a new 9-year long dataset (2009–17) of accumulation rates in Southeast Greenland using NASA Operation IceBridge snow radar. Our accumulation dataset derived from internal layers focuses on high elevations (1500–3000 m) because at lower elevations meltwater percolation obscured internal layer structure. The uncertainty of the radar-derived accumulation rates is 11% [using Firn Densification Model (FDM) density profiles] and the average accumulation rate ranges from 0.5 to 1.2 m w.e. With our observations spanning almost a decade, we find large inter-annual variability, but no significant trend. Accumulation rates are compared with output from two regional climate models (RCMs), MAR and RACMO2. This comparison shows that the models are underestimating accumulation in Southeast Greenland and the models misrepresent spatial heterogeneity due to an orographically forced bias in snowfall near the coast. Our dataset is useful to fill in temporal and spatial data gaps, and to evaluate RCMs where few in situ measurements are available. [ABSTRACT FROM AUTHOR]

This paper provides an update and overview of the Center for Remote Sensing of Ice Sheets (CReSIS) radars and platforms, including representative results from these systems. CReSIS radar systems operate over a frequency range of 14–38 GHz. Each radar system's specific frequency band is driven by the required depth of signal penetration, measurement resolution, allocated frequency spectra, and antenna operating frequencies (often influenced by aircraft integration). We also highlight recent system advancements and future work, including (1) increasing system bandwidth; (2) miniaturizing radar hardware; and (3) increasing sensitivity. For platform development, we are developing smaller, easier to operate and less expensive unmanned aerial systems. Next-generation platforms will further expand accessibility to scientists with vertical takeoff and landing capabilities. [ABSTRACT FROM AUTHOR]

The growing population of artificial satellites in near-Earth orbit has made the monitoring of orbital debris objects ever more important. Orbital debris objects pose a threat to these satellites as their orbit cannot be changed in order to avoid a collision. In recent years, the European Space Agency (ESA)'s Space Surveillance and Tracking (SST) programme has been assisting national institutions in the upgrading of their space debris detection and monitoring capabilities. One of the latest such systems within this programme is the BIRALES space surveillance system based in Italy. The receiving antenna is a radio telescope that is made up of 32 receivers which are placed on eight parabolic cylindrical reflectors of the North–South arm of the Istituto Nazionale di Astrofisica (INAF)'s Northern Cross. This work introduces a new software backend which was developed for this novel space debris sensor. The system was designed to be a fast, highly configurable software backend for the radio telescope's acquisition and processing system and whose monitoring and control can be realized by a simple front-end web-based application. The real-time detection of Resident Space Object (RSO) is an important prerequisite for such a system as it gives the operator an immediate feedback loop on any detections whilst keeping the storage requirements at a minimum given that there is no need to save the raw data. The detection of high-velocity objects is achieved by means of a specially developed data processing pipeline that uses the received raw antenna voltages to generate a number of beams, collectively known as a multipixel, that cover the Field of View (FoV) of the instrument. The trajectory of the detected objects is determined by considering the illumination sequence within this multipixel. The initial results on known objects represent the first steps in extending the growing network of European SST systems. [ABSTRACT FROM AUTHOR]

This article presents the design and optimization of a deployable 1 m mesh reflector compatible with a 12U-class CubeSat. This antenna is designed for telecommunication and is compatible with NASA’s deep-space network (DSN) at ${X}$ -band (i.e., uplink: 7.145–7.19 GHz; downlink: 8.4–8.45 GHz) and Ka-band frequencies (i.e., uplink: 34.2–34.7 GHz; downlink: 31.8–32.3 GHz). Three right-handed circularly polarized (RHCP) antennas, both transmit and receive, are introduced here: ${X}$ -band only, Ka-band only, and ${X}$ -/Ka-band. For the ${X}$ -band-only antenna, a gain of 36.1 and 36.8 dBic is achieved at uplink and downlink frequency bands, respectively. This translates into an aperture efficiency of 72% and 62%. For the Ka-band-only antenna, a gain of 48.4 and 48.7 dBic is obtained at downlink and uplink frequency bands, which translate into an aperture efficiency of 62% and 72%, respectively. [ABSTRACT FROM AUTHOR]

Snow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability and flooding in cold regions. The vast size and remote nature of the Arctic present serious logistical and financial challenges to measuring snow over extended time periods. Satellite observations provided by the Cloud Profiling Radar instrument—installed on the National Aeronautics and Space Administration satellite CloudSat—allow the retrieval of snowfall rates in high‐latitude regions, which have been used to estimate surface snow accumulation. In this study, a validation of CloudSat‐derived terrestrial snow estimates is presented at four Environment and Climate Change Canada weather stations situated in the Arctic for the common period 2007–2015. Comparisons of monthly climatological snow accumulation show mean biases of less than 1.5‐mm snow water equivalent annually. Monthly time series exhibit correlations above 0.5 and root‐mean‐square error below 10‐mm snow water equivalent at the two highest‐latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70°N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat's accumulation estimates, or underestimation related to sampling. These results imply that CloudSat can provide reliable estimates of snow accumulation across similar high‐latitude regions above 70°N.Key Points: Taking repeated in situ snowfall measurements across the Arctic is a challenging and expensive taskCloudSat provides spaceborne retrievals of snowfall rates throughout the ArcticAggregating CloudSat overpasses onto a spatial grid allows us to gain new insights into Arctic snow [ABSTRACT FROM AUTHOR]

The Chinese Gaofen-3 satellite was launched into space in 2016. Based on the corner reflector (CR) evaluation, it was found that the co-pol channel imbalance phase may exceed the maximum tolerance in the distributed standard product, i.e., ± 10°. The ground-deployed CRs are the most precise way to calibrate the phase error. However, the Mongolia calibration site of Gaofen-3 is a temporal calibration field, and the calibration time is limited for the periodic evaluation over the 8-year satellite life. In this paper, to accelerate the Gaofen-3 calibration mission, we propose an improved unitary zero helix (IUZH) framework to fully replace the use of CRs when estimating the co-pol channel imbalance phase by the use of natural media. Compared with our previous works (Shi et al., 2014; Shi et al., 2020), this novel algorithm presents three main contributions to remote sensing society. Firstly, we believe that this natural media calibration technique could replace the use of CRs, to satisfy the radar design specification, and that this natural media calibration technique could be more than just a substitute in an emergency event when there are no CRs deployed in-scene. Secondly, the IUZH framework is less dependent on choosing the particular reference targets, such as isolated point-like objects or Amazon forest. As a result, it will help radar researchers to relax the constraint of selecting references, and will make the non-CR calibration task easier in satellite or airborne cases. Last but not least, the proposed technique simultaneously derives the calibration phase after the sensor has completed a long-strip observation, which will significantly accelerate the Gaofen-3 external calibration mission, at little cost and time-consuming. The experiments with more than 500 images demonstrate that the phase bias of the Gaofen-3 standard product can be robustly calibrated, even when CRs are unavailable in the scene. The HH and VV imbalance phase with the IUZH method is generally found to be less than 4°. The potential phase error of the Gaofen-3 product is thus significantly suppressed, and the calibration accuracy satisfies the 10° design specification. [ABSTRACT FROM AUTHOR]

Radar retrievals of drop size distribution (DSD) parameters are developed and evaluated over the mountainous Olympic Peninsula of Washington State. The observations used to develop retrievals were collected during the 2015/16 Olympic Mountain Experiment (OLYMPEX) and included the NASA S-band dual-polarimetric (NPOL) radar and a collection of second-generation Particle Size and Velocity (PARSIVEL2) disdrometers over the windward slopes of the barrier. Nonlinear and random forest regressions are applied to the PARSIVEL2 data to develop retrievals for median volume diameter, liquid water content, and rain rate. Improvement in DSD retrieval accuracy, defined by the mean error of the retrieval relative to PARSIVEL2 observations, was achieved when using the random forest model when compared with nonlinear regression. Evaluation of disdrometer observations and the retrievals from NPOL indicate that the radar retrievals can accurately reproduce observed DSDs in this region, including the common wintertime regime of small but numerous raindrops that is important there. NPOL retrievals during the OLYMPEX period are further evaluated using two-dimensional video disdrometers (2DVD) and vertically pointing Micro Rain Radars. Results indicate that radar retrievals using random forests may be skillful in capturing DSD characteristics in the lowest portions of the atmosphere. [ABSTRACT FROM AUTHOR]

Compact high-frequency surface wave radar has been widely applied to the measurement of sea surface current, but its accuracy and direction resolution of wave parameter estimation are always limited due to the wide beam of the antenna. In this article, a novel wave-height mapping method based on the second-order harmonic peak (SHP) of radar Doppler spectra is proposed to address this concern. The characteristic of the SHP at the Doppler frequency of $\sqrt {2}$ times the Bragg frequency is studied through the theoretical derivation and numerical simulation. A relationship between the ratio ($R$) of the SHP power to the Bragg peak power and significant wave height ($H_{s}$) is derived. Furthermore, the $R$ – $H_{s}$ model is improved by incorporating influences, such as background noise and antenna beamwidth. With this improved model, a wave-height mapping algorithm based on the direction finding technique is presented. This approach enables the significant wave-height map extraction using a broad-beam radar. Finally, wave-height maps obtained at different sea states are depicted and analyzed, and the wave heights appearing on the maps are compared with buoy data over a one-month experiment to verify the validity and robustness of the algorithm. During this period, the significant wave height varies from about 0.5 to 4.5 m, and the radar measured wave heights at different range/distance bins show an overall root-mean-square error (RMSE) of 0.33–0.77 m and a correlation coefficient (CC) of 0.78–0.94, with respect to the buoy measurements. [ABSTRACT FROM AUTHOR]

Following the August 13, 2011, Indiana State Fair stage collapse tragedy, caused by a wind gust from an approaching thunderstorm, Purdue University enforced a wind speed restriction of 30 mph (13 m s-1) for tents at outdoor events. During these events, volunteers stand outside with handheld anemometers, measuring and reporting when the wind speeds exceed this limit. In this study, we report testing of a new system to automate high-wind alerts based on observations from a Doppler radar, the X-band Teaching and Research Radar (XTRRA), near Purdue's campus. XTRRA scans over campus at low elevations approximately every 5 minutes. Using XTRRA data collected over its first eight months of operation, we developed an algorithm that generates highwind alerts whenever observed winds at altitudes below 240 m (the height of Ross-Ade Stadium) exceed the 13 m s-1 threshold. We describe how a combination of median filtering, clutter filtering, and statistical outlier removal mitigated false alarms caused by noise and ground clutter. The high-wind alerts are validated against wind gust observations from a nearby Automated Surface Observing System at Purdue University Airport, known as KLAF. Results indicate that the alerts work well in high-wind events associated with precipitation but less well in high-wind events not associated with precipitation (e.g., frontal passages). This is likely because XTRRA, which has a wavelength of 3 cm, is less sensitive to clear-air echoes than an operational WSR-88D. Following further testing, we envision that these automated high-wind alerts will be distributed to interested parties such as campus event coordinators and safety officials. [ABSTRACT FROM AUTHOR]

Analyzing time series data with remote sensing provides a better understanding of vegetation dynamics, since previous conditions and changes that have occurred over a given period are known. The objective of this paper was to analyze the current status and recent advances in the use of time series data obtained from remote sensors for vegetation monitoring. A systematic search of scientific papers was performed and 167 papers were found, published during the period 1996 to 2017. No significant difference in the amount of years analyzed was found between time series analyzed with a single sensor and those analyzed with a combination of several sensors (i.e. Landsat and SPOT, Landsat and Sentinel, among others). However, the combination of data from different sensors (fusion of images) can improve the quality of the results. Special attention must also be given to the fusion of optical and radar data, since this offers more unique spectral and structural information for land cover and land use assessments. [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]

We present and discuss an electromagnetic model for the description of the coherent scattering from bare soils illuminated by a radar system under arbitrary bistatic geometries. The scattering problem is solved under the Kirchhoff approximation (KA) accounting for both the sphericity of the wavefront of the incident wave and the radiation pattern of the transmitting and receiving antennas. We propose here a general formulation and solution of the scattering problem applicable to an arbitrary bistatic geometry. We discuss and demonstrate the importance of our extension for the characterization of the coherent scattering generated in bistatic radar systems, both inside and outside the plane of incidence. The model is validated against the numerical solution of the Kirchhoff integral and, in the case of the perfect plane conductor, by comparison with the image theory. The work is intended to provide a simple methodology to characterize the coherent normalized radar cross section (NRCS) of a rough surface to be used within the radar equation for extended targets, similarly to what is done for the incoherent component. It aims at enabling a local characterization of the coherent scattering in realistic conditions (e.g., in the presence of inhomogeneous and mountainous surfaces), a feature that is particularly important for practical applications, such as the modeling and understanding of the bistatic scattering generated by sources of opportunity and specifically for Global Navigation Satellite System Reflectometry (GNSS-R) related applications. [ABSTRACT FROM AUTHOR]

High-frequency (HF) ocean radar has the potential to observe sea ice, but this application has not been thoroughly investigated. In this article, we explore sea ice observation with HF radar (HFR) and develop a corresponding signal processing procedure. We derived sea ice radial velocities from raw HFR data and determined angular positions using the MUltiple SIgnal Classification (MUSIC) algorithm. Sea ice velocities were evaluated by comparison with measurements from moored acoustic Doppler current profilers (ADCPs) and a drifting buoy. The root-mean-square (rms) differences between the sea ice velocities obtained by HFR and the ADCPs decreased with increasing radar signal-to-noise ratio (SNR), implying that a high SNR was associated with accurate measurement of sea ice velocity. These results agree with previous validations of HFR ocean current observations. The sea ice pattern mapped by HFR is consistent with the simultaneous C-band microwave radar observations. In particular, the HFR captured the approach of the sea ice edge toward the coast. Sharp attenuation of the HF signal over high concentration sea ice resulted in missing and/or low-SNR data at ranges beyond the front edge of pack ice, in qualitative agreement with model estimates of attenuation. [ABSTRACT FROM AUTHOR]

Criteria for the localization of gold deposits within the Ayan–Yuryakh anticlinorium in Magadan oblast have been developed using processed satellite imagery data and the analysis of geological and geophysical materials. The structural elements are linear, arc, ring, and concentric lineaments of the first (11–35 km), second (5–10 km), and third (less than 5 km) rank. The ring structures of the second and third rank form linear chains confined to the hidden linear structures of the northwest and north-northeast strike. Gold deposits occur in the central or peripheral parts of the ring structures of the second rank at the intersections of the latter with regional sublatitudinal faults. The area of ​​the Pavlik deposit has a block structure, due to the dominant faults of the northwestern strike, complicated by faults of the northeastern and meridional strike, as well as arc structural elements. When predicting prospective ore areas, the localization patterns of gold ore, determined by the results of gravimetric and gamma surveys, should be taken into account. [ABSTRACT FROM AUTHOR]