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Two seismic applications of time reversal mirrors (TRMs) are introduced and tested with field experiments. The first one is sending, receiving, and decoding coded messages similar to a radio except seismic waves are used. The second one is, similar to radar surveillance, detecting and tracking a moving object(s) in a remote area, including the determination of the objects speed of movement. Both applications require the prior recording of calibration Green's functions in the area of interest. This reference Green's function will be used as a codebook to decrypt the coded message in the first application and as a moving sensor for the second application. Field tests show that seismic radar can detect the moving coordinates (x(t), y(t), z(t)) of a person running through a calibration site. This information also allows for a calculation of his velocity as a function of location. Results with the seismic radio are successful in seismically detecting and decoding coded pulses produced by a hammer. Both seismic radio and radar are highly robust to signals in high noise environments due to the super-stacking property of TRMs., (© 2011 Acoustical Society of America)

Tonglushan old mine site located in Huangshi City, China, is very famous in the world. However, some of the ruins had suffered from geological disasters such as local deformation, surface cracking, in recent years. Structural abnormalities of rock-mass in deep underground were surveyed with borehole ground penetrating radar (GPR) to find out whether there were any mined galleries or mined-out areas below the ruins. With both the multiresolution analysis and sub-band directional of Curvelet transform, the feature information of targets' GPR signals were studied on Curvelet transform domain. Heterogeneity of geotechnical media and clutter jamming of complicated background of GPR signals could be conquered well, and the singularity characteristic information of typical rock mass signals could be extracted. Random noise had be removed by thresholding combined with Curvelet and the statistical characteristics of wanted signals and the noise, then direct wave suppression and the spatial distribution feature extraction could obtain a better result by making use of Curvelet transform directional. GprMax numerical modeling and analyzing of the sample data have verified the feasibility and effectiveness of our method. It is important and applicable for the analyzing of the geological structure and the disaster development about the Tonglushan old mine site., (Copyright © 2011 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.)

The Arnhem Anti-Tank Range (Canadian Forces Base [CFB] Valcartier, Canada, in operation since the 1970s) has been characterized, including the drilling, installation, and characterization of 25 wells and a ground-penetrating radar survey. The observed particular features of this site include highly variable flow velocities (from < 3 to 1200 m/yr) and transient flow regime in the regional aquifer below the contaminant source zone of the impact area, sharp flow direction shifts, discontinuous stratigraphy and a local perched aquifer. A transient ground water flow model permitted us to understand how the complex hydrogeological setting shapes contaminant transport in the regional aquifer. The model explains the highly variable energetic material (EM) concentrations measured in the plume with peaks associated to spring and to a lesser extent to fall recharge events. As a conclusion from this work, the authors suggest that the characterization of contaminant sources on slopes should extend over all seasons to be sure to detect potential transient flow conditions and variable contaminant concentrations.

The most significant biotic and abiotic stress agents of water extremity, salinity, and infection lead to wood decay and modifications of moisture and ion content, and density. This strongly influences the (di-)electrical and mechanical properties and justifies the application of geophysical imaging techniques. These are less invasive and have high resolution in contrast to classical methods of destructive, single-point measurements for inspecting stresses in trees and soils. This review presents some in situ and in vivo applications of electric, radar, and seismic methods for studying water status and movement in soils, roots, and tree trunks. The electrical properties of a root-zone are a consequence of their moisture content. Electrical imaging discriminates resistive, woody roots from conductive, soft roots. Both types are recognized by low radar velocities and high attenuation. Single roots can generate diffraction hyperbolas in radargrams. Pedophysical relationships of water content to electrical resistivity and radar velocity are established by diverse infiltration experiments in the field, laboratory, and in the full-scale 'GeoModel' at Kiel University. Subsurface moisture distributions are derived from geophysical attribute models. The ring electrode technique around trunks images the growth ring structure of concentric resistivity, which is inversely proportional to the fluid content. Healthy trees show a central high resistivity within the dry heartwood that strongly decreases towards the peripheral wet sapwood. Observed structural deviations are caused by infection, decay, shooting, or predominant light and/or wind directions. Seismic trunk tomography also differentiates between decayed and healthy woods.

Conceptual geological models based on geophysical data can elucidate aquifer architecture and heterogeneity at meter and smaller scales, which can lead to better predictions of preferential flow pathways. The macrodispersion experiment (MADE) site, with >2000 measurements of hydraulic conductivity obtained and three tracer tests conducted, serves as an ideal natural laboratory for examining relationships between subsurface flow characteristics and geophysical attributes in fluvial aquifers. The spatial variation of hydraulic conductivity measurements indicates a large degree of site heterogeneity. To evaluate the usefulness of geophysical methods for better delineating fluvial aquifer heterogeneities and distribution of preferential flow paths, a surface grid of two-dimensional ground penetrating radar (GPR) and direct current (DC) resistivity data were collected. A geological model was developed from these data that delineate four stratigraphic units with distinct electrical and radar properties including (from top to bottom) (1) a meandering fluvial system (MFS); (2) a braided fluvial system (BFS); (3) fine-grained sands; and (4) a clay-rich interval. A paleochannel, inferred by other authors to affect flow, was mapped in the MFS with both DC resistivity and GPR data. The channel is 2 to 4 m deep and, based on resistivity values, is predominantly filled with clay and silt. Comparing previously collected hydraulic conductivity measurements and tracer-plume migration patterns to the geological model indicates that flow primarily occurs in the BFS and that the channel mapped in the MFS has no influence on plume migration patterns.

The identification of surface mineralogical composition using hyperspectral sensors is now the major remote sensing opportunity for exploration geologists seeking refined vectors to potential ore-bearing hydrothermal systems. This involves no less than remote, visible and infrared spectroscopy of the molecular composition of geological materials from remote platforms using a large number of calibrated spectral bands. From field-portable systems to those flying in high-flying aircraft on the edges of space, it is now possible to define a long list of minerals and their weathering products detectable at these wavelengths. These include hydroxyl-bearing minerals such as hydrothermal clays, sulfates, ammonium-bearing minerals, phyllosilicates, iron oxides, carbonates, and a wide range of silicates. Indeed, even the chemical composition of micas and chlorites has been mapped remotely using subtle wavelength shifts in their diagnostic reflectance spectra, indicating varying degrees of Na, K, Al, Mg and Fe substitution. Spatial zones, relative abundances and assemblages of these minerals allow geologists to reconstruct the mineralogical, chemical and sometimes thermal disposition of ancient hydrothermal systems in their search for optimal drilling targets. Such minerals not only result directly from the hydrothermal processes involved but may also 'expose' older host rocks caught up in the process and brought to the surface. New microwave radar systems are also shedding new light on landscape processes, textures and structure and occasionally penetrating dry surface layers to reveal buried structures.

Abstract Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach.

ABSTRACTSoil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio ([Formula: see text]) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and ([Formula: see text]) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.

Abstract This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies.

Abstract The mapping of available water–ice is a crucial step in the lunar exploration missions. Ground penetrating radars have the potential to map the subsurface structure and the existence of water–ice in terms of the electromagnetic properties, specifically, the permittivity. Slight differences in permittivity can be significantly important when applied in a dry environment, such as on the Moon and Mars. The capability of detecting a small fraction of putative water–ice depends on the permittivity changes in terms of its dependent parameters, such as the frequency, the temperature, the porosity, and the chemical composition. Our work aims at mitigating false detection or overlooking of water–ce by considering their conditions that previous researches did not cover. We measured the permittivity of different lunar regolith relevant analogue samples with a fixed 40 % porosity in the ultra-high-frequency–super-high-frequency band. We used the coaxial probe method to measure anorthosite, basalt, dunite and ilmenite at $$20\,^\circ \hbox {C}$$ 20 ∘ C , $$-20\,^\circ \hbox {C}$$ - 20 ∘ C and $$-60\,^\circ \hbox {C}$$ - 60 ∘ C , and we find that, at $$-60\,^\circ \hbox {C}$$ - 60 ∘ C , the permittivity decreases about 6–18 % compared with the values at $$20\,^\circ \hbox {C}$$ 20 ∘ C . Within this temperature range, the permittivity is quite similar to the permittivity of water–ice. We find that the conventional calculation would overestimate the permittivity in the low temperature areas, such as the permanently shadowed regions. We also find that each component in the lunar regolith has different temperature-dependent permittivity, which might be important for radar data analysis to detect lunar polar water–ice. Our results also suggest that it should be possible to estimate the water–ice content from radar measurements at different temperatures given an appropriate method. Graphical Abstract

Lineament mapping is a very important step in geological studies and mineral exploration, with the evolution of remote sensing, processing methods as well as the availability of several optical and radar satellite data, lineaments can be detected without using traditional methods. The objective of this work is to recommend the most suitable data for lineaments to be used in the field of geological science, by comparing the performance of three different RADAR data, namely ALOS PALSAR (The Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), Radarsat-1 and Sentinel-1 for automatic lineament extraction using a combination of edge detection and line linking algorithms. The methodology consists to link between the length, number, orientation and density of lineaments with surface characteristics such as the slope, faults, lithology, discontinuities and mineral veins. The results obtained show that the extracted lineaments from Sentinel-1 VH polarization have a better correlation with geological units, tectonic system direction, as well as shading and slope maps, which is due to the effectiveness of VH polarization, which is independent of soil properties compared to the other polarizations that have shown an overestimation of the lineaments.

Abstract On 20 December 2020, after more than 2 years of quiescence at Kīlauea Volcano, Hawaiʻi, renewed volcanic activity in the summit crater caused boiling of the water lake over a period of ∼90 min. The resulting water‐rich, electrified plume rose to 11–13 km above sea level, which is among the highest plumes on record for Kīlauea. Although conventional models would infer a high mass flux from explosive magma‐water interaction, the plume was not associated with an infrasound signal indicative of “explosive” activity, nor did it produce a measurable ash‐fall deposit. We use multisensor data to characterize lava‐water interaction and plume generation during this opening phase of the 2020–21 eruption. Satellite, weather radar, and eyewitness observations revealed that the plume was rich in water vapor and hydrometeors but transported less ash than expected from its maximum height. Volcanic lightning flashes detected by ground‐based cameras were confined to freezing altitudes of the upper cloud, suggesting that the ice formation drove the electrification of this plume. The low acoustic energy from lava‐water interaction points to a weakly explosive style of hydrovolcanism. Heat transfer calculations show that the lava to water heat flux was sufficient to boil the lake within 90 min. Limited mixing of lava and water inhibited major steam explosions and fine fragmentation. Results from one‐dimensional plume modeling suggest that the models may underpredict plume height due to overestimation of crosswind air‐entrainment. Our findings shed light on an unusual style of volcanism in which weakly explosive lava‐water interaction generated an outsized plume.

Lineament mapping is a very important step in geological studies and mineral exploration, with the evolution of remote sensing, processing methods as well as the availability of several optical and radar satellite data, lineaments can be detected without using traditional methods. The objective of this work is to recommend the most suitable data for lineaments to be used in the field of geological science, by comparing the performance of three different RADAR data, namely ALOS PALSAR (The Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), Radarsat-1 and Sentinel-1 for automatic lineament extraction using a combination of edge detection and line linking algorithms. The methodology consists to link between the length, number, orientation and density of lineaments with surface characteristics such as the slope, faults, lithology, discontinuities and mineral veins. The results obtained show that the extracted lineaments from Sentinel-1 VH polarization have a better correlation with geological units, tectonic system direction, as well as shading and slope maps, which is due to the effectiveness of VH polarization, which is independent of soil properties compared to the other polarizations that have shown an overestimation of the lineaments. [ABSTRACT FROM AUTHOR]

Within the Horizon 2020 Project WaterSENSE a modular approach was developed to provide different stakeholders with the required precipitation information. An operational high-quality rainfall grid was set up for the Namoi catchment in Australia based on rain gauge adjusted radar data. Data availability and processing considerations make it necessary to explore alternative precipitation approaches. The gauge adjusted radar data will serve as a benchmark for the alternative precipitation data. The two well established satellite-based precipitation datasets IMERG and GSMaP will be analyzed with the temporal and spatial requirements of the applications envisioned in WaterSENSE in mind. While first results appear promising, these datasets will need further refinements to meet the criteria of WaterSENSE, especially with respect to the spatial resolution. Inferring information from soil moisture-derived from EO observations to increase the spatial detail of the existing satellite-based datasets is a promising approach that will be investigated along with other alternatives.

With well-controlled orbits and a more accurate timing system of the new-generation synthetic aperture radar (SAR) satellites, the ability of high-precision geolocation with multiple SAR acquisitions has been verified based on radar reflectors. In this letter, SAR absolute and differential geolocation methods are introduced and implemented to retrieve the coordinates of two different types of radar reflectors: corner reflectors (CRs) and multidirectional dihedral reflectors (MDRs). The experimental results demonstrate that decimeter- even centimeter-level positioning accuracy can be obtained by SAR stereo geolocation based on multiaspect SAR acquisitions from RadarSAT-2 (RS-2), COSMO-SkyMed (CSK), Sentinel-1 (S1), and TerraSAR-X (TSX), respectively. [ABSTRACT FROM AUTHOR]

In 2014 a catastrophic ice storm occurred in the forests of Börzsöny Mts., Hungary. In this study we analyzed the potential of Synthetic Aperture Radar (SAR) data, complemented, and compared with optical imagery, in mapping this event. Great emphasis was put on reference data: three types of field-based reference datasets were used and the damaged patches were delineated manually based on the visual interpretation of pre- and post-event orthophotos. Four classifications with different set-ups were carried out by applying the eXtreme Gradient Boosting method. Combinations of radar backscatter coefficients, polarimetric descriptors, interferometric coherence, and optical data variables were tested. All classifications were suitable for identifying uprooted trees properly (1–11% underestimation), but none of them could detect crown loss accurately (55–58% overestimation), based on the validation of the most damaged area. Proper differentiation of healthy forests with various levels of canopy closure in the reference data seems crucial for accurate canopy loss estimation. In the case of methods using only Sentinel-1 imagery, interferometric coherence together with polarimetric descriptors provided the best results (OA: 65.7%). This setup can be useful for immediate uproot damage detection for planning salvage logging if a natural disturbance happens outside the vegetation period.

Abstract Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability.

Abstract In this study, we propose a volume‐to‐point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location‐specific precipitations. In contrast to QPE methods based on the Z–R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013–2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume‐to‐point framework performed comparably well in general cases and excelled in detecting heavy‐rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.

In this article, we propose a novel method utilizing iterative semidefinite relaxation for geolocation of stationary uncooperative radars. In our scenario, the geolocation is to be performed in a receiver located on a moving platform. The proposed method uses frequency measurements that are Doppler-shifted due to the platform motion. First, constructed nonconvex maximum likelihood cost function for position estimation is relaxed to a convex optimization problem by applying linearization to range variables. Then, at each iteration of the method, carrier frequency and position of the radar are estimated jointly. Conducted experiments show that a few iterations are enough for convergence to accurate estimates. The proposed method is computationally less expensive compared to traditional techniques, which require extensive grid search procedures in either position or carrier frequency parameter space. In the experiments, the performance of the proposed method is compared to the state-of-the-art techniques and the Cramer–Rao lower bound (CRLB). It is observed that the proposed method attains the CRLB at low noise levels while still providing accurate solutions at high noise cases. Furthermore, it is also seen that platform-radar geometry has an impact on the proposed method's performance. If the radar lies in the convex hull of the receiver path, the proposed method performs significantly better due to improvement in the linearization of range values. [ABSTRACT FROM AUTHOR]

Abstract The Saura radar is designed and used to measure winds and electron densities at polar latitudes (69$$^\circ $$ ∘ N) within the D region, namely between 50 and 100 km altitude. A relatively narrow radar beam can be generated and steered into distinct pointing directions as a rather large antenna array is used. From the observed radial velocities of the individual pointing directions, the horizontal and vertical wind fields can be obtained using the Doppler beam swinging (DBS) method. With recent upgrades to the radar, the interferometric capabilities are largely improved allowing simultaneous application of different wind estimation techniques now, and also echo localization. In recent studies, Saura DBS winds assuming isotropic scattering were found to be underestimated in comparison with highly reliable winds observed with the MAARSY MST radar in the presence of polar mesospheric summer echoes (PMSE). This underestimation has been investigated by analyzing the scattering positions as well as applying the imaging Doppler interferometry technique. Besides this, Saura winds derived with the classical DBS method seem to be error prone at altitudes above 90 km and even below this altitude for periods of enhanced ionization, e.g., particle precipitations. Various methods taking into account the scattering positions have been used to correct the wind underestimation. These winds are compared to MST radar winds during PMSE, and an optimal combination of these methods for the Saura radar is presented. This combined wind data appears to be reliable; it shows reasonable amplitudes as well as tidal structures for the entire altitude region.

Abstract With the recent increase in numbers of small and versatile low-power meteor radars, the opportunity exists to benefit from simultaneous application of multiple systems spaced by only a few hundred km and less. Transmissions from one site can be recorded at adjacent receiving sites using various degrees of forward scatter, potentially allowing atmospheric conditions in the mesopause regions between stations to be diagnosed. This can allow a better spatial overview of the atmospheric conditions at any time. Such studies have been carried out using a small version of such so-called multistatic meteor radars, e.g. Chau et al. (Radio Sci 52:811–828, 2017, https://doi.org/10.1002/2016rs006225). These authors were able to also make measurements of vorticity and divergence. However, measurement uncertainties arise which need to be considered in any application of such techniques. Some errors are so severe that they prohibit useful application of the technique in certain locations, particularly for zones at the midpoints of the radars sites. In this paper, software is developed to allow these errors to be determined, and examples of typical errors involved are discussed. The software should be of value to others who wish to optimize their own MMR systems.

Abstract Ground‐based radar sounders are used to characterize the dynamics and chemistry of Earth's upper atmosphere by using measurements of ionospheric peak electron density (NmF2) and its associated altitude (hmF2). Continuous sounder observations of the E and F regions of the ionosphere have been carried out regularly at dozens of stations worldwide since the midtwentieth century. A deep understanding of short‐ and long‐term upper atmospheric variability depends on a fundamental understanding of these observational data. The manual analysis of historical analog (predigital age) ionograms to derive the plasma frequency profiles and the ionospheric parameters hmF2 and NmF2 is a tedious procedure and susceptible to human error. In order to better understand this human error, a study is conducted in which ionograms from vertical sounders are manually scaled by a team of ionospheric researchers. The results of the study are then used to estimate the variability of the hand‐scaled ionospheric parameters foF2 and foE. Those results are then used to estimate the downstream impact on ionospheric models that use foF2 and foE as input. The results demonstrate that there can be large variability in the manual scaling of foF2 and fmaxE from vertical incidence ionograms. However, the participants did typically better than 5% uncertainty for benign ionograms. A long‐term analysis of hmF2 modeling exhibits low sensitivity to statistical errors imposed on foF2 and foE, but a short‐term analysis showed that modeled hmF2, neutral winds, and electron densities can be very different when small adjustments are made to foF2 and fmaxE.

Abstract Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES.

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

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

Abstract Typical equatorial spread-F events are often said to occur during post-sunset, equinox conditions in most longitude sectors. Recent studies, however, have found an unexpected high occurrence of ionospheric F-region irregularities during June solstice, when conditions are believed to be unfavorable for the development of plasma instabilities responsible for equatorial spread-F (ESF). This study reports new results of a multi-instrumented investigation with the objective to better specify the occurrence of these atypical June solstice ESF in the American sector and better understand the conditions prior to their development. We present the first observations of June solstice ESF events over the Jicamarca Radio Observatory (11.95° S, 76.87° W, ∼ 1° dip latitude) made by a 14-panel version of the Advanced Modular Incoherent Scatter Radar system (AMISR-14). The observations were made between July 11 and August 4, 2016, under low solar flux conditions and in conjunction with dual-frequency GPS, airglow, and digisonde measurements. We found echoes occurring in the pre-, post-, and both pre- and post-midnight sectors. While at least some of these June solstice ESF events could have been attributed to disturbed electric fields, a few events also occurred during geomagnetically quiet conditions. The late appearance (22:00 LT or later) of three of the observed events, during clear-sky nights, provided a unique opportunity to investigate the equatorial bottomside F-region conditions, prior to ESF, using nighttime airglow measurements. We found that the airglow measurements (630 nm) made by a collocated all-sky camera show the occurrence of ionospheric bottomside F-region perturbations prior to the detection of ESF echoes in all three nights. The airglow fluctuations appear as early as 1 hour prior to radar echoes, grow in amplitude, and then coincide with ESF structures observed by AMISR-14 and GPS TEC measurements. They also show some of the features of the so-called large-scale wave structures (LSWS) that have been detected, previously, using other types of observations and have been suggested to be precursors of ESF. The bottomside fluctuations have zonal spacings between 300 and 500 km, are aligned with the magnetic meridian, and extend at least a few degrees in magnetic latitude.

The Sentinel-1 satellite mission provides high-resolution, all-weather, day and night Synthetic Aperture Radar images. Studies about wind parameters above the ocean are frequent, inland lakes were seldom investigated. The objective of this article is to investigate the potential of C-band SAR for wind retrieval over lakes for evaluation of representativeness of meteorological station data. A number of lakes from Austria and Hungary (lakes in Salzkammergut, Lake Neusiedl and Lake Balaton), where wind measurements from automatic weather stations are available from the surroundings, has been selected for this study. Functions for speed retrieval are developed from empirical relationships for both available polarizations, VV and VH. Meteorological station data are used for calibration and validation. Best results were obtained for VV although with a dependency on incidence angle. Functions for different incidence angles according to the wind speed were, therefore, determined. There is a good correlation between the measured and the calculated wind speed (R of 0.71–0.91 for calibration sites; average R 0.71 for all sites). Average wind speed patterns for the lakes can be derived, but need to be analyzed separately for descending and ascending acquisitions due to difference in daytime (morning versus late afternoon).