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This study investigates the feasibility of using a basic 24 GHz one-dimensional (1D) radar for Human Activity Recognition (HAR), focusing on differentiating between walking and standing movements. We evaluate the radar's performance using various machine learning models, including K-means, GMM, SVM, and LSTM. Using the silhouette score and the Davies-Bouldin index, we evaluate the intra- and interclass results of K-means and GMM, while SVM and LSTM are used to analyze their performance. The results indicate that the LSTM model achieves high accuracy in both vertical and horizontal dimensions, with precision, recall, and F1-scores all above 98% for both standing and walking movements. However, the SVM model faces challenges in horizontal movement detection, consistent with the unsupervised learning results where the inter-class and intra-class distances for the horizontal dimension are not significant, making differentiation difficult. These findings delineate the boundaries and capabilities of a lower-specification radar for HAR, providing insights into its practical applications and limitations. [ABSTRACT FROM AUTHOR]

Abstract 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 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.

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]

This study aims to revisit the classic “hot tower” hypothesis proposed by Riehl and Simpson (Malkus) in 1958 and revisited in 1979. Our investigation centers on the convective mass flux of hot towers within the tropical trough zone, using geostationary (GEO) satellite data and an innovative analysis technique, known as ML16, which integrates various data sources, including hot tower heights, ambient profiles, and a plume model, to determine convective mass flux. The GEO-based ML16 approach is evaluated against collocated ground-based radar wind profiler observations, showing broad agreement. Our GEO-based estimate of hot tower convective mass flux, 2.8 × 1011–3.4 × 1011 kg s−1, is similar to the revisited estimate in Riehl and Simpson (1979), 2.6–3.0 × 1011 kg s−1. Additionally, our analysis gives a median count of around 550 hot towers with a median size of about 11 km, in contrast to the previous estimates of 1600–2400 hot towers, each characterized by a fixed size of 5 km. We discuss the causes of these discrepancies, emphasizing the fundamental differences between the two approaches in characterizing tropical hot towers. While both approaches have various uncertainties, the evidence suggests that greater credibility should be placed on results derived from direct satellite observations. Finally, we identify future opportunities in Earth Observations that will provide more accurate measurements, enabling further evaluation of the role played by tropical hot towers in mass transport. [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]

Meteor radars have become a widely used instrument to study atmospheric dynamics, in particular in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular/transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and introduce a mathematical debiasing process to mitigate them. This process makes use of a spatio-temporal Laplace filter which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals a good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostic such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ±1-2 m/s for most of the analyzed data during two years of collected data, which is consistent to the values reported from GCMs for this time scale and spatial resolution. [ABSTRACT FROM AUTHOR]

nd is a software framework for the analysis of n-dimensional datacubes. Such data structures are common in Earth observation where the data are often multivariate on a spatio-temporal grid. The Python library xarray already exists to represent such data sets. This software builds on xarray by providing an interface with other parts of the Python ecosystem, such as scikit-learn and rasterio. Most of the functionality is accessible through an added accessor xarray.Dataset.nd. The software is maintained on GitHub and releases are published on the Python Package Index (PyPI) and Zenodo. [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 records of 24,797 traffic accidents (9039 involving fatalities or severe injury) during rainy conditions from 2007 to 2017 in Seoul, South Korea, were used to analyze the spatial distribution of the traffic accidents and rainfall events based on radar and gauge rainfall data. According to the spatial correspondence analysis between rainfall distribution and accident locations for localized and stratiform rain events, radar data in a two-dimensional grid (250 by 250 m) of 10 min temporal resolution benefited the localized rainfall distribution concerning the accident location. The relative accident rate (RAR) from radar data, which was used as a quantitative reference value for the effect of rainfall on traffic accidents, was about 18% higher than that from gauge rainfall. The radar data more clearly classified the number of traffic accidents during rainy conditions because its spatial distribution was more precise for all accidents. In addition, the RAR estimation of accidents involving fatalities and severe injury during rainfall could provide information on the district in which traffic accidents increase due to rainfall. The study results support the adoption of radar-derived rainfall data to analyze the influence of rainfall on accidents and the development of more accurate risk-assessment tools for drivers and planners. [ABSTRACT FROM AUTHOR]

We introduce TAASRAD19, a high-resolution radar reflectivity dataset collected by the Civil Protection weather radar of the Trentino South Tyrol Region, in the Italian Alps. The dataset includes 894,916 timesteps of precipitation from more than 9 years of data, offering a novel resource to develop and benchmark analog ensemble models and machine learning solutions for precipitation nowcasting. Data are expressed as 2D images, considering the maximum reflectivity on the vertical section at 5 min sampling rate, covering an area of 240 km of diameter at 500 m horizontal resolution. The TAASRAD19 distribution also includes a curated set of 1,732 sequences, for a total of 362,233 radar images, labeled with precipitation type tags assigned by expert meteorologists. We validate TAASRAD19 as a benchmark for nowcasting methods by introducing a TrajGRU deep learning model to forecast reflectivity, and a procedure based on the UMAP dimensionality reduction algorithm for interactive exploration. Software methods for data pre-processing, model training and inference, and a pre-trained model are publicly available on GitHub (https://github.com/MPBA/TAASRAD19) for study replication and reproducibility. Measurement(s) hydrological precipitation process Technology Type(s) computational modeling technique • digital curation Factor Type(s) year of data collection Sample Characteristic - Environment climate system Sample Characteristic - Location Autonomous Region of Trentino-Alto Adige/Sudtirol Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12529484 [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]

Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on applying machine learning and deep learning to solve complicated problems. It is a well-known fact that an adequate amount of data is a positively necessary condition in machine learning and deep learning. Generative adversarial networks (GANs) have received extensive attention for their remarkable data generation capacity, with a fascinating competitive structure having been proposed since. Consequently, a massive number of variants have been proposed; which model is adequate to solve the given problem is an inevitable concern. In this paper, we propose exploring the problem of radar image synthesis and evaluating different GANs with authentic radar observation results. The experimental results showed that the improved Wasserstein GAN is more capable of generating similar radar images while achieving higher structural similarity results. [ABSTRACT FROM AUTHOR]

Pyrad is a real-time data processing framework developed by MeteoSwiss. The framework is aimed at reading, processing and visualizing polar data from individual weather radars as well as composite Cartesian products both off-line and in real time. The processing flow is controlled by three simple configuration files. This allows the construction of reproducible data processing chains. In the off-line mode, data from multiple radars can be ingested. It is written in the Python programming language. Most of the signal processing and part of the data visualization is performed by a MeteoSwiss-developed version of the Py-ART radar toolkit, which contains enhanced features. Thanks to the broad types of input files accepted and its flexibility it can be easily adapted and used by any member of the weather radar community. The source code is available on GitHub. Compiled versions are also available on PyPI and conda-forge. They are distributed under a BSD license. [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]

The analysis of the MST Radar Data has become more predominant in the present world. A major research on MST Radar Data was going on in NARL, Gadanki, which provides the data regarding the atmospheric movements. In order to obtain the information on the wind parameters, the signals collected from the radar are analyzed, which mainly involves the estimation of power spectrum. Analysis of the MST Radar Data involves the estimation of the Signal to Noise Ratio (SNR) of the Doppler profiles, Wind Profiles and Parameters, Temperature Profiles, etc. In NARL, the weather data or the Meteor data was collected and the data was converted into raw format. This raw data was used for processing either in time domain or frequency domain. Many techniques have been employed for processing the MST Radar data, which shows better results when compared to the GPS Data (Data Processed in NARL). This paper mainly gives survey of papers in which various methods are used for the analysis of the MST Radar data. [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]

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]

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]