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The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods. [ABSTRACT FROM AUTHOR]

Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field. [ABSTRACT FROM AUTHOR]

The goal of this paper is to propose a method to achieve a higher classification rate in Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In our work, PolSAR features are extracted from Convolutional Neural Networks (CNNs) and also Graph Convolutional Networks (GCNs). Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a substantial computational cost, particularly in large‐scale remote sensing (RS) problems. To this end, first of all, we propose a new mini‐batch GCN (called Mini‐GCN), which allows to train large‐scale GCNs in a mini‐batch fashion. More significantly, our designed mini‐GCN is capable of inferring out‐of‐sample data without re‐training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of PolSAR features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since mini‐GCNs can perform batch‐wise network training (enabling the combination of CNNs and GCNs), we can use the feature fusion strategy. Therefore, in this paper, a local graph‐based fusion method is proposed to couple dimension reduction and feature fusion of the information extracted from the designed CNN and Mini‐GCN. Experimental results on real PolSAR data are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed feature fusion method improves the overall classification accuracy on the real PolSAR data sets for more than 5%. Moreover, the experiments conducted on PolSAR datasets, demonstrate the advantages of the used mini‐GCNs over the traditional GCNs and the superiority of the proposed feature fusion method with regards to the single CNN or GCN models. [ABSTRACT FROM AUTHOR]

Radio waves propagating in the atmosphere are affected by the prevailing atmospheric state. The state of the atmosphere can cause radio waves to refract more or less towards the ground. When the refractive index of the atmosphere differs from standard atmospheric conditions, the propagation is considered to be anomalous. Radars which are affected by anomalous propagation can observe ground clutter far beyond the radar horizon. In this work, 4.5 years' worth of data from five operational Swedish C-band dual-polarization weather radars are presented. Analyses of the data reveal a strong seasonal cycle and a weaker diurnal cycle in ground clutter from coastal regions across nearby waters. A comparison was drawn between the impacts of anomalous propagation on ground clutter measured with horizontal polarization and vertical polarization, respectively; however, no clear difference was found. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an imaging modality that can estimate a visualization of the conductivity distribution inside the human body. However, the spatial resolution of EIT is limited because measurements are sensitive to noise. We investigate a technique to incorporate a priori information into the EIT reconstructions of the D-Bar algorithm. Our paper aims to help engineers understand the behavior of the D-Bar algorithm and its implementation. The a priori information is provided by a radar setup and a one-dimensional reconstruction of the radar data. The EIT reconstruction is carried out with a D-Bar algorithm. An intermediate step in the D-Bar algorithm is the scattering transform. The a priori information is added in this exact step to increase the spatial resolution of the reconstruction. As the D-Bar algorithm is widely used in the mathematical community and thus far has limited usage in the engineering domain, we also aim to explain the implementation of the algorithm and give an intuitive understanding where possible. Different parameters of the reconstruction algorithm are analyzed systematically with the help of the GREIT figures of merit. Even a limited one-dimensional a priori information can increase the reconstruction quality considerably. Artifacts from noisy EIT measurements are reduced. However, the selection of the amount of a priori information and the estimation of its value can worsen the reconstruction results again. [ABSTRACT FROM AUTHOR]

Radio waves propagating in the atmosphere are affected by the prevailing atmospheric state. The state of the atmosphere can cause radio waves to refract more or less towards the ground. When the refractive index of the atmosphere differs from standard atmospheric conditions the propagation is called anomalous. Radars which are affected by anomalous propagation can receive ground clutter far beyond the radar horizon. In this work 4.5 years of data from five operational Swedish C-band dual polarization weather radars are presented. Analyses of the data reveal a strong seasonal cycle and weaker diurnal cycle in ground clutter from coastal regions across nearby waters. The impact of anomalous propagation on ground clutter, measured with horizontal and vertical polarization, was compared but no clear difference was found. [ABSTRACT FROM AUTHOR]

In this study, hourly data assimilation (DA) cycling is performed during a 24-h time period for Hurricane Matthew (2016), assimilating ground-based (GBR) and tail-Doppler radar (TDR) observations together, as well as separately using HWRF and its Hybrid 3DEnVar DA system. The objective is to examine the impacts of assimilating such data on the analysis and prediction of the weakening and re-intensification stages of the eyewall replacement cycle (ERC) of Matthew. Experiments assimilating GBR observations make quicker corrections to the initially inconsistent storm structure than does the TDR experiment, resulting in the primary and secondary eyewalls being realistically represented during the DA cycling period. The TDR experiment analyses show less-realistic concentric eyewall structure before, during, and after TDR observations become available. The forecasts from experiments assimilating GBR observations show more-realistic structural and point intensity changes for the ERC consistently throughout the cycling period when compared with the experiments assimilating TDR observations. Combined assimilation of GBR and TDR observations show similar ERC forecasts, on average, to the GBR experiment. The superior performance of the GBR experiments is shown to be tied to its earlier and longer availability despite its limited low-level coverage especially at the early stage of the cycling. The inferior performance of the TDR experiments even during the availability of TDR is hypothesized to be a result of rapidly changing 3D observational coverage during the high-frequency cycling. Brief mechanism diagnostics additionally suggest the need of properly initializing the TC concentric eyewalls to capture the ERC during the forecasts. [ABSTRACT FROM AUTHOR]

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth's surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase. [ABSTRACT FROM AUTHOR]

The impact of assimilating China's operational X‐band Phased‐Array radar's (X‐PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S‐band operational radars and one X‐PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X‐PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X‐PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data assimilation, assimilating X‐PAR data improved the predictability of tornadic storm. Plain Language Summary: Tornadoes are difficult to predict worldwide. However, the scan speed of current operational S‐band (10 cm wavelength) radar is too slow to capture the evolution of tornadoes. The newly built X‐band (3 cm wavelength) Phase‐Array radar (X‐PAR) network in Guangdong Province, China, are hopeful to fill in the gap with their higher scan frequency and higher spatial resolution, which can capture the fine‐scale structure and rapid evolution of small scale systems such as tornadic storms. This study focuses on a case where a tornado embedded within the typhoon Ewiniar was well captured by the X‐PAR network. It demonstrated for the first time the use of X‐PAR data on the prediction of a tornadic storm in China. We used ensemble‐based data assimilation techniques named Ensemble Kalman Filter to utilize the information provided by the X‐PAR data. With the improved information, the analyzed tornadic storm vortex structure at low levels was greatly improved, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data, the experiment using X‐PAR data successfully predicted the tornadic vortex a few minutes in advance. Key Points: The impact of assimilating China's X‐band Phased‐Array radar (X‐PAR) radar data on the analysis and forecast of a tornadic storm was first investigated using an Ensemble Kalman Filter systemThe assimilation of X‐PAR data helps to produce a more realistic‐looking tornadic storm structure, especially at low levelsIncreasing assimilation frequency of X‐PAR data reduces the analysis error and subsequently improves the 8‐min tornadic storm forecast [ABSTRACT FROM AUTHOR]

This paper conducts a safety assessment for reduced route spacing for RNP 2 aircraft under a radar environment. Although the criteria for 15 NM separation standards exist, past safety assessment did not consider the surveillance environment. This consideration may reduce the possible route spacing. Here, to account for the surveillance environment, the recently developed ASEPS model is applied. Since this model was intended for deployment on oceanic route systems, the model parameters are modified appropriately while keeping the consistency of the past safety analysis and data analysis. In particular, the parameter of occupancy is set based on one-year flight data in Japanese airspace, and the calculation of action time to resolve the conflict is modified to estimate the collision probability more accurately. The results show that 8 NM route spacing satisfies the safety criteria. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of data transmission for the joint radar and communication systems (JRCSs). The performance of the JRCS is characterized by data throughput related to the radar echo data (RED) and communication data rate (CDR). Two spectral coexistence schemes are proposed based on the degree of spectrum sharing for radar and communication, i.e., the isolated subfrequency band (ISFB) and mix-used frequency band (MUFB) schemes. Firstly, the signals of radar and communication are operated on the isolated subcarriers, enabling the received signals to be processed independently and bringing the advantage of interference avoidance. Secondly, the signals of radar and communication can be jointly operated on the same subcarriers for the MUFB scheme, which enhances the spectrum efficiency. Unlike the ISFB scheme, the CDR of the MUFB scheme is maximized along with the interference from the radar signal, and meanwhile, the allocated radar power on each subcarrier is derived by maximizing the radar mutual information. Numerical results show that the MUFB scheme significantly improves the performance of data transmission over the ISFB scheme, and a significant performance gain in the data transmission can be achieved, compared to the average power allocation case. [ABSTRACT FROM AUTHOR]

This paper proposed the system development especially for watering the agricultural crops depend upon the WSN. This paper focused to develop and model a control process by joint radars in the agricultural crop along with information management through web and smartphone application. The 3 elements are application of mobile, web and hardware. The first element i.e. hardware was executed and designed in manage box hardware linked to gather information about the crops. Soil humidity radars are used to detect the agricultural field linked to the control box. The 2nd element i.e. web method was web depend method which was executed and modeled to handle the details of field and crop information. This element applied information mining to examine the information for finding perfect soil humidity, moisture level and temperature. The last element i.e. mobile method was used mainly to manage field watering by a mobile method in a phone. This allows manual or automatic control by the controller. An automatic control uses information from soil humidity radars for watering the crops. The user may choose the manual method for watering the field in the system control method. The method may send notifications by LINE API for the line app. The method was tested and executed in Northeast India. The outputs displayed the executions to be helpful in the field of agriculture. The humidity level of the soil was appropriately maintained for improving manufacturing in agriculture, growth of vegetables and decreasing cost. Therefore, this paper displays the driving agriculture field by digital creativity. [ABSTRACT FROM AUTHOR]

The Lunar penetrating radar (LPR) carried by Chang'E-3 (CE-3) mission is an important application of radar in lunar exploration. An opportunity of significance to detect the information of regolith and the subsurface structure on the landing site is offered by LPR aboard the Yutu rover. On the basis of a data processing flow, a low-frequency radar image has been available for mapping subsurface structure. The noise interfered data give a huge challenge for geological stratification and interpretation. To solve the limitation imposed by noise, we adopt the shearlet transform as a promising tool for data analysis and noise attenuation. The different distributions of the noise and signal in the shearlet domain decrease the difficulty of noise attenuation. To optimize the denosing strategy, we replace a local adaptive thresholding function with the conventional hard threshold. The quality of the processed data is improved, which is helpful for geological stratification and interpretation. Finally, by combining these data with the regional geology and previous research, especially the LPR data, we can provide an interpretation of the LPR CH-1. [ABSTRACT FROM AUTHOR]

There is an urgent need for high-quality and high-spatial-resolution hourly precipitation products around the globe, including the UK. Although hourly precipitation products exist for the UK, these either contain large errors, or are insufficient in spatial resolution. An efficient way to solve this is to develop a merged precipitation product that combines the information and benefits from multiple data sources, improving both the spatial resolution and accuracy of hourly precipitation estimates over the UK. In this study, we develop a UK high-resolution gauge–radar–satellite merged hourly precipitation analysis: the UKGrsHP. It covers the UK from 12.5° W to 3.5° E, 49° N–60° N, with a spatial resolution of 0.01° × 0.01° in latitude/longitude (equivalent to 1 km resolution in the mid-latitudes). An optimal interpolation (OI)–based multi-source merging scheme with compound strategy is developed and tested for producing the UKGrsHP. Three input data sources are used: gauge analysis data interpolated from 1903 quality-controlled hourly observations, the UK Nimrod radar precipitation analysis and the GSMaP global satellite precipitation analysis. Using independent tests against ~ 220 independent gauge observations on 1 year's experimental UKGrsHP, covering the period from January to December 2014, we find that the final merged data performs better than three individual precipitation analyses used as inputs. A full version of the UKGrsHP starting in April 2004 is now under production, which will have wide applications in climate services and scientific research across multiple disciplines. [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]

Before we had the Internet, computers rarely cooperated with one another. In the 1970s, over a dozen closely connected computers processed the radar data for the UK's Air Defence system. Some of the techniques used then were described in 1972, without disclosing the context. Many years after the decommissioning and replacement of that system, we can now describe the software that made it work. Innovative principles and strategies were used to produce a large multi-computer software system. In practice, the infrastructure was much larger than the application software; and the experience gained was more about computer systems than about radar. As well as describing the software concerned, we identify the significant influences on it, and the benefits that were achieved from it. [ABSTRACT FROM AUTHOR]

Highlights • A method for radar HRRP target recognition is proposed based on Conditional VAE. • The model is regularized through the average HRRP for robust representations. • The label information is introduced by a three-way weight conditional model. • Weight factorization is utilized to effectively reduce model parameters. • Our method performs well under the low SNR and small amount of training data. Abstract In this paper, we propose a conditional generative model for radar high resolution range profile (HRRP) target recognition to learn the discriminative representations and sufficiently encode the observed feature variability by taking the multi-layer perception (MLP) as the sufficient statistics of posterior approximation distribution, thus offering the potential to improve the overall recognition performance. Considering the target-aspect sensitivity of HRRP, the model is regularized through reconstructing the average profiles. Then we introduce three-way weight tensors for MLPs to capture the multiplicative interactions between label information and HRRP samples, which are then further factorized to effectively reduce model parameters. The extensive experimental results on the measured HRRP data demonstrate that the proposed algorithm achieves the promising target recognition and reconstruction performance. [ABSTRACT FROM AUTHOR]

The issue of ambiguous frequency is a major drawback of the Doppler through-wall radar in target tracking applications. To solve the issue, an additional receiver, which can acquire target information from a different detection perspective, is added to the radar system, and the data fusion process is proposed to identify and track the targets in real time. The linear prediction coding technique is combined with the data fusion process to predict the target initial state for higher positioning accuracy. Compared with the traditional tracking approach, the proposed algorithm can not only improve the target positioning accuracy, suppress the issue of ambiguous frequency, but also help to identify stationary and tangentially moving targets, which is very essential in target tracking applications. A series of experiments are conducted to illustrate an outranking performance of the proposed algorithm in comparison with the traditional approach. [ABSTRACT FROM AUTHOR]

PyTroll (http://pytroll.org) is a suite of open-source easy-to-use Python packages to facilitate processing and efficient sharing of Earth Observation (EO) satellite data. The PyTroll software is intended for both 24/7 real-time operations as well as research and development. PyTroll grew out of the need to provide a resilient and agile platform that can respond quickly to new user needs and new data sources. PyTroll, being open source, stimulates international collaboration, which is vital with the rapid increase of satellite information availability. The PyTroll software development is strongly user driven and has grown over the past eight years from a collaborative effort between the Danish and Swedish national meteorological services to encompass a worldwide community with active contributors. PyTroll is being used at least operationally in the national meteorological services of Denmark, Norway, Sweden, Finland, Germany, Switzerland, Italy, Estonia, and Latvia. However, given its simplicity, minimal demand on user resources, and community-driven approach, it also encourages and facilitates usage of EO data for individual applications. While PyTroll was originally developed to cater to the needs of the atmospheric remote sensing community, it could be equally useful for land and ocean applications and within hydrology. This article provides an overview of PyTroll, with examples showing the capability of some of the core packages. [ABSTRACT FROM AUTHOR]

This contribution proposes an evaluation of lidar and radar data processing and its potential in revealing archaeological features within a level plain environment, the southern lowland of Verona (Italy), focusing on evidences dating back to the Bronze Age. Many archaeological sites in the research area, including some of the most outstanding settlements of Terramare Culture, were identified or at least examined through aerial photo observation. Even if in several occasions modern agricultural activities contributed to the discoveries, bringing to the surface artifacts and scrapes of buried layers, this kind of impact has also been progressively deteriorating the archaeological record, hence the proto-historic landscape is now discernible through evanescent marks which cannot be always detected using customary optical sensors. Lidar and radar data analysis has then been considered as an alternative, non-invasive method of investigation on such a vast area. [ABSTRACT FROM AUTHOR]

ABSTRACT The ensemble transform Kalman filter based ensemble 3D variational ( ETKF-En3DVAR) data assimilation ( DA) system is employed to evaluate the potential value of assimilating radar radial wind ( Vr) data for the analysis and forecasting of Typhoon Saomai (2006). The DA system conducted cycling assimilation every 30 min when Saomai started to enter radar coverage. Within the DA cycles, the control analysis was updated by the ETKF-En3DVAR algorithm whereas the forecast ensemble perturbations in the hybrid scheme were updated by the ETKF algorithm. The benefits from the use of the flow-dependent ensemble covariance are explored by comparing the analysis increments, analysis and subsequent forecasts from the hybrid scheme with those from a pure 3DVAR using static background error covariance. Sensitivity to the horizontal correlation scale in the 3DVAR and the vertical covariance localization in the hybrid are also explored. The reduced horizontal correlation scale in the 3DVAR yields much more reasonable circulation analyses than the default scale. The vertical covariance localization scale specified in terms of geometric height instead of model levels allows for desirable spreading of Vr data to the surface. It seems that the assimilation with the hybrid method leads to further improved vortex intensity forecast and track forecast of the typhoon compared to those in the analyses from the global forecast system and 3DVAR. The results also indicated that the hybrid has a significant effect on the 12 h accumulated rainfall forecasts. Such improvements for analysis and forecast are probably due to the use of the flow-dependent background error covariance. [ABSTRACT FROM AUTHOR]

Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and reporting. The conventional approach of using visible and near-infrared based vegetation index (VI) observations has prevailed for decades since the onset of the global satellite era. However, other satellite data encompass diverse spectral ranges that may contain complementary information on crop growth and yield, but have been largely understudied and underused. Here we conducted one of the first attempts at synergizing multiple satellite data spanning a diverse spectral range, including visible, near-infrared, thermal and microwave, into one framework to estimate crop yield for the U.S. Corn Belt, one of the world's most important food baskets. Specifically, we included MODIS Enhanced VI (EVI), estimated Gross Primary Production based on GOME-2 solar-induced fluorescence (SIF-GPP), thermal-based ALEXI Evapotranspiration (ET), QuikSCAT Ku-band radar backscatter, and AMSR-E X-band passive microwave Vegetation Optical Depth (VOD) in this study, benchmarked on USDA county-level crop yield statistics. We used Partial Least Square Regression (PLSR), an effective statistical model for dimension reduction, to distinguish commonly shared and unique individual information from the various satellite data and other ancillary climate information for crop yield estimation. In the PLSR model that includes all of the satellite data and climate variables from 2007 to 2009, we assessed the first two major PLSR components and found that the first component (an integrated proxy of crop aboveground biomass) explained 82% variability of modelled crop yield, and the second component (dominated by environmental stresses) explained 15% variability of modelled crop yield. We found that most of the satellite derived metrics (e.g. SIF-GPP, radar backscatter, EVI, VOD, ALEXI-ET) share common information related to aboveground crop biomass (i.e. the first component). For this shared information, the SIF-GPP and backscatter data contain almost the same amount of information as EVI at the county scale. When removing the above shared component from all of the satellite data, we found that EVI and SIF-GPP do not provide much extra information; instead, Ku-band backscatter, thermal-based ALEXI-ET, and X-band VOD provide unique information on environmental stresses that improves overall crop yield predictive skill. In particular, Ku-band backscatter and associated differences between morning and afternoon overpasses contribute unique information on crop growth and environmental stress. Overall, using satellite data from various spectral bands significantly improves regional crop yield predictions. The additional use of ancillary climate data (e.g. precipitation and temperature) further improves model skill, in part because the crop reproductive stage related to harvest index is highly sensitive to environmental stresses but they are not fully captured by the satellite data used in our study. We conclude that using satellite data across various spectral ranges can improve monitoring of large-scale crop growth and yield beyond what can be achieved from individual sensors. These results also inform the synergistic use and development of current and next generation satellite missions, including NASA ECOSTRESS, SMAP, and OCO-2, for agricultural applications. [ABSTRACT FROM AUTHOR]

Rainfall-runoff simulations in urban environments require meteorological input data with high temporal and spatial resolutions. The availability of precipitation data is constantly increasing due to the shift towards more open data sharing. However, the applicability of such data for urban runoff assessments is often unknown. Here, the feasibility of Finnish Meteorological Institute’s open rain gauge and open weather radar data as input sources was studied by conducting Storm Water Management Model simulations at a very small (33.5 ha) urban catchment in Helsinki, Finland. In addition to the open data sources, data were also available from two research gauges, one of them located on-site, and from a research radar. The results confirmed the importance of local precipitation measurements for urban rainfall-runoff simulations, implying the suitability of open gauge data to be largely dictated by the gauge’s distance from the catchment. Performance of open radar data with 5 min and 1 km 2 resolution was acceptable in terms of runoff reproduction, albeit peak flows were constantly and flow volumes often underestimated. Gauge adjustment and advection interpolation were found to improve the quality of the radar data, and at least gauge adjustment should be performed when open radar data are used. Finally, utilizing dual-polarization capabilities of radars has a potential to improve rainfall estimates for high intensity storms although more research is still needed. [ABSTRACT FROM AUTHOR]

This letter presents the monitoring results of the sea waves and the surface currents obtained by analyzing data acquired by a X-band marine radar in two different operative conditions, namely the short and medium pulse modes. In particular, we investigated the feasibility to use a medium radar pulse for sea state monitoring by comparing the performance in both the radar modes. The comparison was carried out by means of an experimental campaign and we observed a good agreement for surface current and sea state parameters estimation. [ABSTRACT FROM AUTHOR]

In this work, we will identify the existence of "rough dependence on initial conditions" in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the weather using the Navier-Stokes equations is useless after some characteristic time scale. With rough dependence, two initial states diverge very quickly, implying forecasting may be impossible. Using previous research in atmospheric science, rough dependence is characterized by using quantities that can be calculated using atmospheric data and quantities. Rough dependence will be tested for and identified in atmospheric phenomena at different time scales using case studies. Data were provided for this project by archives outside the University of Missouri (MU) and by using the MU RADAR at the South Farm experiment station. [ABSTRACT FROM AUTHOR]

Most applications of land cover maps that have been derived from satellite data over the Arctic require higher thematic detail than available in current global maps. A range of application studies has been reviewed, including up-scaling of carbon fluxes and pools, permafrost feature mapping and transition monitoring. Early land cover mapping studies were driven by the demand to characterize wildlife habitats. Later, in the 1990s, up-scaling of in situ measurements became central to the discipline of land cover mapping on local to regional scales at several sites across the Arctic. This includes the Kuparuk basin in Alaska, the Usa basin and the Lena Delta in Russia. All of these multi-purpose land cover maps have been derived from Landsat data. High resolution maps (from optical satellite data) serve frequently as input for the characterization of periglacial features and also flux tower footprints in recent studies. The most used map to address circumpolar issues is the CAVM (Circum Arctic Vegetation Map) based on AVHRR (1 km) and has been manually derived. It provides the required thematic detail for many applications, but is confined to areas north of the treeline, and it is limited in spatial detail. A higher spatial resolution circumpolar land cover map with sufficient thematic content would be beneficial for a range of applications. Such a land cover classification should be compatible with existing global maps and applicable for multiple purposes. The thematic content of existing global maps has been assessed by comparison to the CAVM and regional maps. None of the maps provides the required thematic detail. Spatial resolution has been compared to used classes for local to regional applications. The required thematic detail increases with spatial resolution since coarser datasets are usually applied over larger areas covering more relevant landscape units. This is especially of concern when the entire Arctic is addressed. A spatial resolution around 30 m has been shown to be suitable for a range of applications. This implies that the current Landsat-8, as well as Sentinel-2 missions would be adequate as input data. Recent studies have exemplified the value of Synthetic Aperture Radar (SAR) in tundra regions. SAR missions may be therefore of added value for large-scale high latitude land cover mapping. [ABSTRACT FROM AUTHOR]

The high-frequency (HF) radar inversion algorithm for spectrum estimation (HIAS) can estimate ocean wave directional spectra from both dual and single radar. Wave data from a dual radar and two single radars are compared with in situ observations. The agreement of the wave parameters estimated from the dual radar with those from in situ observations is the best of the three. In contrast, the agreement of the wave parameters estimated from the single radar in which no Doppler spectra are observed in the cell closest to the in situ observation point is the worst among the three. Wave data from the dual radar and the two single radars are compared. The comparison of the wave heights estimated from the single and dual radars shows that the area sampled by the Doppler spectra for the single radar is more critical than the number of Doppler spectra in terms of agreement with the dual-radar-estimated wave heights. In contrast, the comparison of the wave periods demonstrates that the number of Doppler spectra observed by the single radar is more critical for agreement of the wave periods than the area of the Doppler spectra. There is a bias directed to the radar position in the single radar estimated wave direction. [ABSTRACT FROM AUTHOR]

Disaster due to inundation is one of the major threats of coal mines. Sudden heavy inrush of high volume of water from the unapproachable and unknown waterlogged workings in underground mines is defined as inundation. There have been several instances of inrush of water due to old galleries getting connected by new development headings, resulting in disasters involving heavy fatalities and loss of machines. This type of disaster has occurred in many coal producing countries and may occur in future until a suitable tool to investigate barriers thickness is in regular use. Directorate General of Mines Safety (DGMS) has stipulated that a safety barrier thickness of not less than 60 m must be maintained between abandoned workings and contemporary mine developments. It has been estimated that a large number of underground coal mines in India are under threat of adjacent unknown waterlogged mine workings. Therefore, evaluation of barrier thickness up to 60 m is essential to prevent mine disasters due to inundations. Considering the above facts, a new GPR system named MineVue radar is developed having depth of penetration of 60 m subject to geo-mining conditions of Indian coal mines first time in the world. Final trial of the developed MineVue radar system was done at the Kachhi Balihari colliery of Bharat Cocking Coal Limited (BCCL), Dhanbad. The validation of GPR data in the known mine sites were done with accurate existing mine-plans to establish the reliability and confidence of mine operators, policy makers and safety personnel. [ABSTRACT FROM AUTHOR]

Summary Numerous studies have shown that radar rainfall estimates need to be adjusted against rain gauge measurements in order to be useful for hydrological modelling. In the current study we investigate if adjustment can improve radar rainfall estimates to the point where they can be used for modelling overflows from urban drainage systems, and we furthermore investigate the importance of the aggregation period of the adjustment scheme. This is done by continuously adjusting X-band radar data based on the previous 5–30 min of rain data recorded by multiple rain gauges and propagating the rainfall estimates through a hydraulic urban drainage model. The model is built entirely from physical data, without any calibration, to avoid bias towards any specific type of rainfall estimate. The performance is assessed by comparing measured and modelled water levels at a weir downstream of a highly impermeable, well defined, 64 ha urban catchment, for nine overflow generating rain events. The dynamically adjusted radar data perform best when the aggregation period is as small as 10–20 min, in which case it performs much better than static adjusted radar data and data from rain gauges situated 2–3 km away. [ABSTRACT FROM AUTHOR]

Based on the WRF-3DVar system, different momentum control variables in radar data assimilation are investigated for their effects on the analysis and forecast of strong convective weather systems in the study. The single observation assimilation tests are performed besides three cycling data assimilation experiments based on U-wind/ V -wind (UV) and stream-function and velocity potential (ψχ) that are scalar representations of flow fields for their irrotational and divergence components. The wind field increments for both control variables in single observation tests show that when UV are the control variables, the wind field increments have a smaller range of influence that it better reflects the characteristics of the observed information itself at the convective scale than ψχ. When ψχ are used as control variables, negative increments are generated in the analyzed wind field because ψχ have the property of maintaining the integrated value of the wind field. In addition, radar data from 5 Doppler radars in northeast China are assimilated. The cycling assimilation experiments show that CV_UV produces enhanced forecast than CV_ ψχ in terms of radar reflectivity and precipitation. Another case also proves this conclusion. In particular, CV_UV improves the dynamics field and water vapor convergence, which lead to better intensity and structure prediction of the strong convection process. In contrast, CV_ ψχ yields relative discontinuous wind field structure, which tends to reduce the organization of the forecast strong echoes. Another set of experiments are further designed with only radar reflectivity assimilated with CV_UV. The results show that assimilating radial velocity is helpful to improve the forecasting skill. • Different control variables are studied for their effects on the analysis and forecast of strong convective systems. • CV_UV is relatively more localized in terms of analysis increments and is applicable to the convective scale. • CV_UV improves the dynamics field and water vapor convergence for the strong convection process. • CV_UV produces enhanced forecast than CV_ψχ in terms of radar reflectivity and precipitation. [ABSTRACT FROM AUTHOR]

We analyze Cassini VIMS data of several areas on Titan’s surface looking for variations with time. Three of these locations are near the equator (10–30°S), namely Hotei Regio, Tui Regio and Sotra Patera; in some cases changes in brightness and/or in appearance were reported therein. We also investigate a portion of the undifferentiated plains, areas relatively homogeneous and dark in radar observations, located near 20–25°N. This is a follow-up on a previous paper in which we had inferred surface albedos for some distinct regions of interest (RoIs) identified within the Hotei, Tui and Sotra areas through a Principal Component Analysis (PCA) and radiative transfer (RT) modeling (Solomonidou [2014]. J. Geophys. Res. 119, 1729–1747). We apply the same methods here to a larger dataset looking for variations of the surface albedo with time and using the Huygens landing site as the ‘ground truth’ for calibration purposes. As expected, the undifferentiated plains remain unchanged from January 2010 to June 2012. Our analysis of Hotei Regio data from March 2005 to March 2009 also does not show any significant surface albedo variations within uncertainties. We note however that our RT retrievals are not optimal in this case because of the use of a plane-parallel code and the unfavorable geometry of the associated datasets. Conversely, Tui Regio and Sotra Patera show surface albedo fluctuations with time with pronounced trends for darkening and for brightening respectively. The Tui Regio spectrum exhibits a surface albedo decrease from March 2005 to February 2009, at 0.94, 1.08, 2.03, and 5 μm wavelengths, while the spectrum shape remains the same over that time. On the contrary, the Sotra Patera area became at least two times brighter within a year (April 2005–February 2006), at 1.58 μm, 2.03 μm, and 5 μm. We also retrieved surface albedo spectra for three reference regions surrounding Hotei, Tui and Sotra and for three additional regions we use as ‘test cases’ that correspond to dune fields. During the time periods explored here we find that, as expected and contrary to Tui Regio and Sotra Patera, the test cases did not show any significant changes in surface albedo. We therefore suggest that temporal variations of surface albedo exist for some areas on Titan, but that their origin may differ from one region to the other. They could be due to diverse, past and/or ongoing formation processes (endogenic and/or exogenic, possibly cryovolcanic), as discussed here. [ABSTRACT FROM AUTHOR]

The impact of the assimilation of satellite sea surface height, sea surface temperature and surface velocity fields observed by a set of high-frequency (HF) radars is studied using a three-dimensional ocean circulation model configured for the southern Tyrrhenian Sea. The study period is October–December 2010 covered by a large number of data. The nonlinear model is based on the Regional Ocean Modeling System (ROMS) and the data assimilation component on the four-dimensional variational (4D-Var) algorithm. Assimilation proceeds in a series of 7-day windows, providing an analysis solution in each window. The assimilation of surface velocity combined with other observations has more utility in recovering the density fields based on the theory of geostrophic adjustment and a strong impact both on near-surface horizontal volume fluxes and subsurface flows, constraining surface geostrophic currents in the area not covered by the HF radar data. The adjoint of the 4D-Var gain matrix was used to quantify the impact of individual observations and observation platforms on different aspects of the 4D-Var circulation estimates during both the analysis and subsequent forecast cycles. In this study, we focus on the alongshore transport of the surface and intermediate waters in the eastern zone of southern Tyrrhenian Sea. The majority of the observations available during any given analysis cycle are from HF radar, and on average these data, together with SSH data, exert the largest controlling influence on the analysis increments of coastal transport. Also, observations from satellite platforms in the form of SST have a considerable impact on analyses and forecasts of coastal transport, even though these observations represent a relatively small fraction of the available data at any particular time. During 4D-Var, the observations are used to correct for uncertainties in the model control variables, namely, the initial conditions, surface forcing, and open boundary conditions. It is found that correcting for uncertainties in the initial conditions and only secondarily in the boundary conditions has the largest impact on the analysis increments in alongshore transport. Finally, we note that both the control vector and the observation impact calculations are a useful way for monitoring the performance of the data assimilation system, as well as quantifying the impact of the observations on the circulation estimates. [ABSTRACT FROM AUTHOR]

Complex terrain poses significant challenges to the radar based quantitative precipitation estimation (QPE) because of blockages to the lower tilts of radar observations. The blockages often force the use of higher tilts data to estimate precipitation at the ground and result in errors due to vertical variations of the radar variables. To obtain accurate radar QPEs in the subtropical complex terrain of Taiwan, a vertically corrected composite algorithm (VCCA) was developed for two C-band polarimetric radars. The new algorithm corrects higher tilt radar variables with the vertical profile of reflectivity (VPR) or vertical profile of specific differential phase (VPSDP) and estimates rainfall rate at the ground through an automated combination of R-Z and R-KDP relations. The VCCA was assessed with three precipitation cases of different regimes including typhoon, mei-yu, and summer stratiform precipitation events. The results showed that a combination of R-Z and R-KDP relations provided more accurate QPEs than each alone because R-Z provides better rainfall estimates for light rains and R-KDP relation is more suitable for heavy rains. The vertical profile corrections for reflectivity and specific differential phase significantly reduced radar QPE errors caused by inadequate sampling of the orographic enhancement of precipitation near the ground. [ABSTRACT FROM AUTHOR]

The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) carried out in February 2000 has provided near global topographic data that has been widely used in many fields of earth sciences. The mission goal of an absolute vertical accuracy within 16 m (with 90% confidence)/RMSE ∼10 m was achieved based on ground validation of SRTM data through various studies using global positioning system (GPS). We present a new and independent assessment of the vertical accuracy of both the X- and C-band SRTM datasets using data from the International GNSS Service (IGS) network of high-precision static GPS stations. These stations exist worldwide, have better spatial distribution than previous studies, have a vertical accuracy of 6 mm and constitute the most accurate ground control points (GCPs) possible on earth; these stations are used as fiducial stations to define the International Terrestrial Reference Frame (ITRF). Globally, for outlier-filtered data (135 X-band stations and 290 C-band stations), the error or difference between IGS and SRTM heights exhibits a non-normal distribution with a mean and standard error of 8.2 ± 0.7 and 6.9 ± 0.5 m for X- and C-band data, respectively. Continent-wise, Africa, Australia and North America comply with the SRTM mission absolute vertical accuracy of 16 m (with 90% confidence)/RMSE ∼10 m. However, Asia, Europe and South America have vertical errors higher than the SRTM mission goal. At stations where both the X- and C-band SRTM data were present, the root mean square error (RMSE) of both the X- and C-bands was identical at 11.5 m, indicating similar quality of both the X- and C-band SRTM data. [ABSTRACT FROM AUTHOR]

Radar-identified convective modes, peak low-level rotational velocities, and near-storm environmental data were assigned to a sample of tornadoes reported in the contiguous United States during 2009-13. The tornado segment data were filtered by the maximum enhanced Fujita (EF)-scale tornado event per hour using a 40-km horizontal grid. Convective mode was assigned to each tornado event by examining full volumetric Weather Surveillance Radar-1988 Doppler data at the beginning time of each event, and 0.5° peak rotational velocity ( Vrot) data were identified manually during the life span of each tornado event. Environmental information accompanied each grid-hour event, consisting primarily of supercell-related convective parameters from the hourly objective mesoscale analyses calculated and archived at the Storm Prediction Center. Results from examining environmental and radar attributes, featuring the significant tornado parameter (STP) and 0.5° peak Vrot data, suggest an increasing conditional probability for greater EF-scale damage as both STP and 0.5° peak Vrot increase, especially with supercells. Possible applications of these findings include using the conditional probability of tornado intensity as a real-time situational awareness tool. [ABSTRACT FROM AUTHOR]

An object-based verification technique that keys off the radar-retrieved vertically integrated liquid (VIL) is used to evaluate how well the High-Resolution Rapid Refresh (HRRR) predicted mesoscale convective systems (MCSs) in 2012 and 2013. It is found that the modeled radar VIL values are roughly 50% lower than observed. This mean bias is accounted for by reducing the radar VIL threshold used to identify MCSs in the HRRR. This allows for a more fair evaluation of the model's skill at predicting MCSs. Using an optimized VIL threshold for each summer, it is found that the HRRR reproduces the first (i.e., counts) and second moments (i.e., size distribution) of the observed MCS size distribution averaged over the eastern United States, as well as their aspect ratio, orientation, and diurnal variations. Despite threshold optimization, the HRRR tended to predict too many (few) MCSs at lead times less (greater) than 4 h because of lead time-dependent biases in the modeled radar VIL. The HRRR predicted too many MCSs over the Great Plains and too few MCSs over the southeastern United States during the day. These biases are related to the model's tendency to initiate too many MCSs over the Great Plains and too few MCSs over the southeastern United States. Additional low biases found over the Mississippi River valley region at night revealed a tendency for the HRRR to dissipate MCSs too quickly. The skill of the HRRR at predicting specific MCS events increased between 2012 and 2013, coinciding with changes in both the model physics and in the methods used to assimilate the three-dimensional radar reflectivity. [ABSTRACT FROM AUTHOR]

On 31 May 2013, a supercell produced a tornado rated as 3 on the enhanced Fujita scale (EF3) near El Reno, Oklahoma, which was sampled by the S-band phased-array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma. Collaboration with the forecaster who issued tornado warnings for the El Reno supercell during real-time operations focused the analysis on critical radar signatures frequently assessed during warning operations. The wealth of real-world experience provided by the forecaster, along with the quantitative analysis, highlighted differences between rapid-scan PAR data and the Weather Surveillance Radar-1988 Doppler located near Oklahoma City, Oklahoma (KTLX), within the context of forecast challenges faced on 31 May 2013. The comparison revealed that the 70-s PAR data proved most advantageous to the forecaster's situational awareness in instances of rapid storm organization, sudden mesocyclone intensification, and abrupt, short-term changes in tornado motion. Situations where PAR data were most advantageous in the depiction of storm-scale processes included 1) rapid variations in mesocyclone intensity and associated changes in inflow magnitude; 2) imminent radar-indicated development of the short-lived (EF0) Calumet, Oklahoma, and long-lived (EF3) El Reno tornadoes; and 3) precise location and motion of the tornado circulation. As a result, it is surmised that rapid-scan volumetric radar data in cases like this would augment a forecaster's ability to observe rapidly evolving storm features and deliver timely, life-saving information to the general public. [ABSTRACT FROM AUTHOR]

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory. [ABSTRACT FROM PUBLISHER]

A new constant false-alarm rate (CFAR) detector for non-Rayleigh data, based on fuzzy statistical normalization, is proposed. The proposed detector carries out the detection with two stages. The first stage of the fuzzy statistical normalization CFAR processor is background level estimation, based on fuzzy statistical normalization. The second stage is signal detection, based on the original data and the defuzzification normalized data. Performance comparisons are carried out to validate the superiority of the proposed CFAR detector. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of adjusting the mean field bias (MFB) in radar-based precipitation data on analysis and prediction of streamflow and soil moisture in assimilating streamflow or streamflow and in situ soil moisture data into distributed hydrologic models. To evaluate the effect of adjusting the MFB under realistic as well as idealized conditions, both real-world and synthetic experiments are carried out for the Eldon Catchment on the border of Oklahoma and Arkansas in the US. In the synthetic experiment, the MFB is modeled as a stationary Markov chain process. The synthetic experiment showed that adjusting the MFB in the assimilation process significantly improves streamflow analysis when the initial conditions are known with reasonable certainty, and that assimilating soil moisture in addition to streamflow improves analysis of streamflow as well as soil moisture if the initial conditions are largely uncertain. Adjusting the MFB during the assimilation process noticeably improved streamflow analysis over ranges of the MFB and random noise in the precipitation data. On the other hand, increasing the MFB and random noise in the precipitation data tended to degrade soil moisture analysis due possibly to over-adjusting soil moisture to mitigate the precipitation error. The real-world experiment with one-year dataset showed that adjusting the MFB during the assimilation process helped capture the peak as well as volume of outlet flow analysis as well as prediction, and that additionally assimilating interior flow observations was necessary to improve analysis and prediction of peak flows at interior locations. [ABSTRACT FROM AUTHOR]

The comparison of rain areas observed by the radar network BALTRAD and those produced by the model BALTIMOS shows that the model is able to reproduce the rain areas' basic properties. These are power law distributed frequency distributions as well as lognormal rain area size distributions. The parameters governing these distributions are also matched by power laws evident for the observations and the BALTIMOS data. The distributions also provide evidence that the model produces too many large structures and too little small ones. However, the shape of these structures, represented by their fractal dimension, is well met. The perimeter of the model structures is only slightly smoother than that of the observed ones. The model is also able to reproduce the diurnal cycle of convective activity with emerging and decaying convective cells, though some deficiencies in the timing and the magnitude of the maximum occurrence of rain areas and peaks are evident. Additionally, it was found that the methods developed within this project are a valuable tool to validate BALTIMOS and potentially also other regional climate models. [ABSTRACT FROM AUTHOR]

The digital elevation products produced by the Shuttle Radar Topography Mission (SRTM) are disseminated with different resolutions. This study investigates the differences in information content between different resolutions. Three 1° cells are selected to represent flat area, moderate terrain, and mountain ranges. All of these cells are located in the continental territory of the United States. The theory of information loss caused by the sampling density is shown with the increasing deviation from the reference in the experiment. The error resulting from bilinear interpolation is also assessed. The experimental result also indicates that if the 1-arcsecond SRTM, digital elevation model (DEM) is released for global coverage, the height accuracy would be improved over the 3-arcsecond product. [ABSTRACT FROM AUTHOR]

The reprocessing of the archive data of the television experiment performed by the Venera-13 spacecraft in 1982 on the surface of the planet allowed an image of the nearby crater 1.5 km across to be obtained in detail. Its structural features apparently indicate its volcanic origin. All of the earlier acquired analogous images of such formations were composed only from the orbital radar data and correspond to the sizes of tens of kilometers. [ABSTRACT FROM AUTHOR]

The characterisation of soil surface roughness is a key requirement for the correct analysis of radar backscattering behaviour. It is noteworthy that an increase in the number of surface roughness parameters in a model also increases the difficulty with which data can be inverted for the purposes of estimating soil parameters. In this paper, a new description of soil surface roughness is proposed for microwave applications. This is based on an original roughness parameter, Zg , which combines the three most commonly used soil parameters: root mean surface height, correlation length, and correlation function shape, into just one parameter. Numerical modelling, based on the moment method and integral equations, is used to evaluate the relevance of this approach. It is applied over a broad dataset of numerically generated surfaces characterised by a large range of surface roughness parameters. A strong correlation is observed between this new parameter and the radar backscattering simulations, for the HH and VV polarisations in the C and X bands. It is proposed to validate this approach using data acquired in the C and X bands, at several agricultural sites in France. It was found that the parameter Zg has a high potential for the analysis of surface roughness using radar measurements. An empirical model is proposed for the simulation of backscattered radar signals over bare soil. [ABSTRACT FROM AUTHOR]

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF. [ABSTRACT FROM AUTHOR]

We analyze the internal stratigraphy in radio-echo sounding data of the northeast Greenland ice stream to infer past and present ice dynamics. In the upper reaches of the ice stream, we propose that shear-margin steady-state folds in internal reflecting horizons (IRHs) form due to the influence of ice flow over spatially varying basal lubrication. IRHs are generally lower in the ice stream than outside, likely because of greater basal melting in the ice stream from enhanced geothermal flux and heat of sliding. Strain-rate modeling of IRHs deposited during the Holocene indicates no recent major changes in ice-stream vigor or extent in this region. Downstream of our survey, IRHs are disrupted as the ice flows into a prominent overdeepening. When combined with additional data from other studies, these data suggest that upstream portions of the ice stream are controlled by variations in basal lubrication whereas downstream portions are confined by basal topography. [ABSTRACT FROM AUTHOR]

The transition from limited-slip conditions at the base of grounded ice to free-slip conditions beneath floating ice occurs across the few-kilometers-wide grounding zone. This region involves either an elastic flexural transition from bedrock to hydrostatically supported elevations (often tidally influenced), a transition from thicker to thinner ice over a flat bed, or some combination of these two processes. In either case, ice must flow across a changing stress field, often resulting in brittle deformation, manifested as basal crevassing. Thus the position and morphology of basal crevasses reveal important information about the stress state across this transition. Our gridded ground-based radar surveys on Whillans Ice Stream, West Antarctica, indicate a complex pattern of basal crevasses, but most are associated with regions where the surface elevation gradient is steepest. Due to the high reflectivity of sea water, we image many off-nadir crevasses from a corner-reflector geometry involving reflections from the ice/sea-water interface and then from the crevasse, producing echoes with an inverted phase that could be misinterpreted as subglacial returns. Our results indicate that basal crevasses offer a rich dataset for diagnosing stress state and salient processes across grounding zones, and that special care is needed when interpreting subglacial returns in radar data. [ABSTRACT FROM AUTHOR]

Isolated areas of high basal drag, or 'sticky spots', are important and poorly understood features in the force balance and dynamics of West Antarctic ice streams. Characterizing sticky spots formed by thin or drying subglacial till using ice-penetrating radar is theoretically possible, as high radar bed-returned power (BRP) is commonly related to an abundance of free water at the ice/bed interface, provided losses from englacial attenuation can be estimated. In this study we use airborne radar data collected over Evans Ice Stream to extract BRP profiles and test the sensitivity of BRP to the adopted englacial attenuation correction. We analyse 11 ~20km profiles in four fast-flow areas where sticky spots have been inferred to exist on the basis of model and surface data inversions. In the majority of profiles we note that the increase in basal drag is accompanied by a decrease in BRP and suggest that this is evidence both for the presence of a sticky spot in those locations and that local variations in subglacial hydrology are responsible for their existence. A comparison is made between empirical and numerical modelling approaches for deriving englacial attenuation, and our findings generally support previous studies that advocate a modelling approach. [ABSTRACT FROM AUTHOR]