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Tropical cyclones (TCs) can transport birds and insects near their center of circulation. In this study, we examined the maximum altitude, area and density of the radar‐derived bioscatter signature across a set of 42 TC centers of circulation sampled from 2011 to 2020. All TC events contained at least one time when a bioscatter signature was present. More intense hurricanes with closed eyes typically had taller and denser bioscatter signatures, and sometimes larger areas dominated by bioscatter. This indicated a larger number of organisms within the circulation of more intense hurricanes, supporting the speculation that those storms were most likely to trap birds that do not want to risk flying through their eyewall thunderstorms. Larger and denser bioscatter signatures, indicating a larger number of birds, tend to occur when fall migration brings a large bird population to the Gulf and East Coasts where most storms were sampled. TC formation location was not related to bioscatter characteristics, but storms sampled in the Gulf of Mexico and Florida tended to have larger and denser bioscatter signatures.

Upcoming Synthetic Aperture Radar (SAR) missions with larger swath like NASA-ISRO Synthetic Aperture Radar (NISAR) (~250 km) requires a large, flat and homogeneous low-background site, free from human-made structures for point targets deployment for calibration. Finding out such a large, flat, homogeneous area devoid of perceived sources of radar clutter is a challenging task. In this regard, Antarctica is a potential site for calibration as it fulfills many of the criteria required for the ideal calibration site and suitable for setting up a corner reflector (CR) network for SAR calibration. This network will help in the calibration study and aid the studies related to Interferometric SAR (InSAR) viz. ice velocity estimation. As a part of this activity, in-house designed and developed CRs were installed near Indian research base stations Maitri and Bharati at Antarctica during the 38th Indian Scientific Expedition to Antarctica (ISEA) during 2018–2019. Corner reflectors were designed keeping in mind the harsh environment of Antarctica, such as high katabatic winds, blizzards, snowfall and low temperature. Temporal and seasonal analysis of radar backscatter data using available Indian Radar Imaging Satellite (RISAT-1) and European SENTINEL-1 SAR satellite over Maitri and Bharati was carried out to find out the suitable CR deployment site. Super-hydrophobic microwave transparent cover was designed and installed over CRs to protect snow accumulation. Hydrophobic radar absorbing materials were installed over central-mount to decrease the background noise due to it. Various tests such as structural analysis of total CR system, thermal analysis of materials and Radar Cross Section (RCS) characterization of CR were done before sending it to Antarctica. Integrated Wide Swath (IW) mode data of Sentinel-1 satellite was used for analyzing time-series response of CR in SAR image.

Differential absorption radar (DAR) near the 183 GHz water vapor absorption line is an emerging measurement technique for humidity profiling inside of clouds and precipitation with high vertical resolution, as well as for measuring integrated water vapor (IWV) in clear air regions. For radar transmit frequencies on the water line flank away from the highly attenuating line center, the DAR system becomes most sensitive to water vapor in the planetary boundary layer (PBL), which is a region of the atmosphere that is poorly resolved in the vertical by existing spaceborne humidity and temperature profiling instruments. In this work, we present a high-fidelity, end-to-end simulation framework for notional spaceborne DAR instruments that feature realistically achievable radar performance metrics, and apply this simulator to assess DAR's PBL humidity observation capabilities. Both the assumed instrument parameters and radar retrieval algorithm leverage recent technology and algorithm development for an existing airborne DAR instrument. To showcase the capabilities of DAR for humidity observations in a variety of relevant PBL settings, we implement the instrument simulator in the context of large eddy simulations (LES) of 5 different cloud regimes throughout the trade-wind subtropical-to-tropical cloud transition. Three distinct DAR humidity observations are investigated: IWV between the top of the atmosphere and the first detected cloud bin or Earth's surface; in-cloud water vapor profiles with 200 meter vertical resolution; and IWV between the last detected cloud bin and the Earth's surface, which can provide a precise measurement of the sub-cloud humidity. We provide a thorough assessment of the systematic and random errors for all 3 measurement products for each LES case, and analyze the humidity precision scaling with along-track measurement integration. While retrieval performance depends greatly on the specific cloud regime, we find generally that for a radar with cross-track scanning capability, in-cloud profiles with 200 m vertical resolution and 10–20 % uncertainty can be retrieved for horizontal integration distances of 100–200 km. Furthermore, column IWV can be retrieved with 10 % uncertainty for 10–20 km of horizontal integration. Finally, we provide some example science applications of the simulated DAR observations, including estimating near-surface relative humidity using the cloud-to-surface column IWV, and inferring in-cloud temperature profiles from the DAR water vapor profiles by assuming a fully saturated environment.

For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges, and some other special events such as large rock and snow avalanches.

A new method is proposed for improving the IRI2016 vertical electron density profile by ingesting the in situ Ne values from Swarm A, B and C. The method is validated by comparing the improved electron density profiles with measured full height profiles over three Incoherent Scatter Radar stations in the American sector (Jicamarca, Millstone Hill and Sondrestrom) during the period 5–11 September 2017 covering both quiet and storm days. We considered 90 epochs corresponding to times when one of the Swarm satellites passed near an Incoherent Scatter Radar at a time when the Incoherent Scatter Radar was measuring electron density profiles. The method results in improvements of the electron density profile, as measured in terms of the Root-Mean-Square-Error in 63% of the observed epochs. The fraction of improvements on quiet days is 63% of the observed epochs, while 37% of the improvements occurred on storm days. The change in Root-Mean-Square-Error of the IRI profile compared to the ISR profiles before and after the modification was not dependent on the distance between the observation location and the ISR, nor on the difference between the hmF2 predicted by the IRI and measured by the ISR.

Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations to assimilate and for assignment of the OE covariance matrix that can be diagonal or full and correlated.

The crystal orientation fabric (COF) of ice sheets records the past history of ice sheet deformation and influences present-day ice flow dynamics. Though not widely implemented, coherent ice-penetrating radar is able to detect bulk anisotropic fabric patterns by exploiting the birefringence of ice crystals at radar frequencies, with the assumption that one of the crystallographic axes is aligned in the vertical direction. In this study, we conduct a suite of quad-polarimetric measurements consisting of four orthogonal antenna orientation combinations near the Western Antarctic Ice Sheet (WAIS) Divide ice core site. From these measurements, we are able to quantify the azimuthal fabric asymmetry at this site to a depth of 1400 m at a bulk-averaged resolution of up to 15 m. Our estimates of fabric asymmetry closely match corresponding fabric estimates directly measured from the WAIS Divide ice core. While ice core studies are often unable to determine the absolute fabric orientation due to core rotation during extraction, we are able to identify and conclude that the fabric orientation is depth-invariant to at least 1400 m, equivalent to 6700 years BP (years before 1950) and aligns closely with the modern surface strain direction at WAIS Divide. Our results support the claim that the deformation regime at WAIS Divide has not changed substantially through the majority of the Holocene. Rapid polarimetric determination of bulk fabric asymmetry and orientation compares well with much more laborious sample-based COF measurements from thin ice sections. Because it is the bulk-averaged fabric that ultimately influences ice flow, polarimetric radar methods provide an opportunity for its accurate and widespread mapping and its incorporation into ice flow models.

Ground-penetrating radar (GPR) is a geophysical technique widely used in near-surface noninvasive detecting. It has the ability to obtain a high-resolution internal structure of living tree trunks. Full-waveform inversion (FWI) has been widely used to reconstruct the dielectric constant and conductivity distribution for crosswell applications. However, in some cases, the amplitude information is not reliable due to the antenna coupling, radiation pattern, and other effects. We have developed a multiscale phase inversion (MPI) method, which largely matches the phase information by normalizing the magnitude spectrum; in addition, a natural multiscale approach by integrating the input data with different times is implemented to partly mitigate the local minimal problem. Two synthetic GPR data sets generated from a healthy oak tree trunk and from a decayed trunk are tested by MPI and FWI. A field GPR data set consisting of 30 common-shot GPR data is acquired on a standing white oak tree ( Quercus alba); the MPI and FWI methods are used to reconstruct the dielectric constant distribution of the tree cross section. Results indicate that MPI has more tolerance to the starting model, noise level, and source wavelet. It can provide a more accurate image of the dielectric constant distribution compared to the conventional FWI.

Non-specular meteor trail echoes are radar reflections from plasma instabilities that are caused by field-aligned irregularities. Meteor simulations are examined to show that these plasma instabilities, and thus the associated meteor trail echo, strongly depend on the meteoroid properties and the characteristics of the atmosphere in which the meteoroid is embedded. The effects of neutral winds, as a function of altitude, are analyzed to understand how their amplitude variability impacts the temporal–space signatures of non-specular meteor trail echoes present in very high-frequency (VHF) radar observations. It is found that amplitudes of the total horizontal neutral wind smaller than 0.6 m s−1 do not provide the right physical conditions to enable the genesis of non-specular meteor echoes. It is also found that a 0.0316 µg meteoroid traveling at 35 km s−1 can be seen as a meteor trail echo if the amplitudes of horizontal neutral winds are stronger than 15 m s−1. In contrast, a 0.316 µg meteoroid, traveling at the same speed, requires horizontal winds stronger than 1 m s−1 to be visible as a meteor trail echo. The neutral velocity threshold illustrates how simulations show that no trail echo is created below a critical wind value. This critical wind value is not mapped directly to radar observations, but it is used to shed light on the physics of meteor trails and improve their modeling. The meteor simulations also indicate that time delays on the order of hundreds of milliseconds or longer, between head echoes and non-specular echoes, which are present in VHF backscatter radar maps, can be a consequence of very dense plasma trails being affected by weak horizontal neutral winds that are smaller than 1 m s−1.

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

The quality of wave measurements is of primary importance for the validation of wave forecasting models, satellite wave calibration and validation, wave physics, offshore operations and design and climate monitoring. Validation of global wave forecasts revealed significant regional differences, which were linked to the different wave buoy systems used by different countries. To fully understand the differences between the wave measurement systems, it is necessary to go beyond investigations of the integral wave parameters height, period and direction, into the frequency spectra and the four directional Fourier parameters that are used to estimate the directional distribution. We here analyse wave data measured from three different sensors (non-directional Datawell Waverider buoy, WaveRadar Rex, Optech laser) operating at the Ekofisk oil production platform located in the central North Sea over a period of several months, with significant wave height ranging from 1 to 10 m. In general, all three sensors provide similar measurements of the integral wave properties and frequency spectra, although there are some significant differences which could impact design and operations, forecast verification and climate monitoring. For example, the radar underestimates energy in frequency bands higher than 8 s by 3–5%, swell (12.5–16 s) by 5–13%, while the laser has 1–2% more energy than the Waverider in the most energetic bands. Lee effects of structures are also estimated. Lower energy at the frequency tail with the radar has an effect on wave periods (they are higher); wave steepness is seen to be reduced by 10% in the wind seas. Goda peakedness and the unidirectional Benjamin-Feir index are also examined for the three sensors.

Radar dual-wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band scanning polarimetric radar (KASPR, 35 GHz), a W-band profiling radar (94 GHz), and a next-generation K-band (24 GHz) micro rain radar (MRRPro) were exploited for ice particle identification using triple-frequency approaches. The results indicated that two of the radar frequencies (K and Ka band) are not sufficiently separated; thus, the triple-frequency radar approaches had limited success. On the other hand, a joint analysis of DWR, mean Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details. We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase in DWR but a 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases in DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction. The study suggests that triple-frequency measurements are not always necessary for in-depth ice microphysical studies and that dual-frequency polarimetric and Doppler measurements can successfully be used to gain insights into ice hydrometeor microphysics.

Dual-pol Shannon Entropy captures the dynamic crop growth parameters. It was used to evaluate biophysical parameters of important crops using Sentinel-1 data. Improved phenology information is vita...

We have developed a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that our method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in semi-arid areas where water resources are limited. Radar observations, available at high resolution and with a high revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared with the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gather soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/2 weeks including aboveground fresh and dry biomasses, vegetation water content based on destructive measurements, the cover fraction, the leaf area index, and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High Resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric-effects-corrected products is also provided. This database, which is the first of its kind made available open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation variable retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely accessible via the following DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a).

In February 2016 and April 2018 the European Space Agency launched the Sentinel-3A and 3B satellites respectively, as part of the European Commission’s multi-satellite Copernicus Programme. Here we process Sentinel-3A waveform data to estimate Arctic sea level anomaly and radar freeboard from November 2017 to April 2018. We compare our results to those from the CryoSat-2 satellite, and find an intermission bias on sea-level anomaly of 2 cm. We also find a mean radar freeboard difference of 1 cm, which we attribute to the use of empirical retrackers to retrieve lead and floe elevations. Ahead of Sentinel-3B waveform data being made available, we use orbit files to estimate the improvement in sampling resolution afforded by the addition of Sentinel-3A and 3B data to the CryoSat-2 dataset. By combining data from the three satellites, grid resolution or time-sampling can be almost tripled compared with using CryoSat-2 data alone.

The accuracy of spatial precipitation estimates with relatively high spatiotemporal resolution is of vital importance in various fields of research and practice. Yet the intricate variability and intermittent nature of precipitation make it very difficult to obtain accurate spatial precipitation estimates. Radars and rain gauges are two complementary sources of precipitation information: the former are inaccurate in general but are valid indicators of the spatial pattern of the rainfall field; the latter are relatively accurate but lack spatial coverage. Several radar–gauge merging techniques that can provide spatial precipitation estimates have been proposed in the scientific literature. Conditional simulation has great potential to be used in spatial precipitation estimation. Unlike commonly used interpolation methods, conditional simulation yields a range of possible estimates due to its Monte Carlo framework. However, one obstacle that hampers the application of conditional simulation in spatial precipitation estimation is the need to obtain the marginal distribution function of the rainfall field with sufficient accuracy. In this work, we propose a method to obtain the marginal distribution function from radar and rain gauge data. A conditional simulation method, random mixing (RM), is used to simulate rainfall fields. The radar and rain gauge data used in the application of the proposed method are derived from a stack of synthetic rainfall fields. Due to the full control over the stochastic process, the accuracy of the estimates is verified comprehensively. The results from the proposed approach are compared with those from three well-known radar–gauge merging techniques: ordinary Kriging, Kriging with external drift, and conditional merging, and the sensitivity of the approach to two factors – the number of rain gauges and the random error in the radar estimates – is analysed in the same experimental context.

Context. The space control radar stations carry out the extremely important functions, therefore, they must be extremely reliable, and ensuring reliability is one of the key requirements at all stages of their life cycle. Objective. The goal of the work is the analyzing some aspects of ensuring reliability, caused by the peculiarities of the process of developing and manufacturing radar stations monitoring space, justifying the possibility of using the experience of a domestic developer in solving reliability problems in new samples. Method. A comparative analysis of the methods of development, manufacturing and testing of complex technical systems, existing standards and the standard of the Chief Designer are used in the work. Results. Some of the characteristic features of space control radar stations are given: uniqueness, single production, low repeatability and a large range, the impossibility of manufacturing and testing functionally complete systems directly under the manufacturer’s conditions. A number of features of design and manufacturing according to the standard of the Main Designer, which are absent in the existing standards, are considered, the necessity of developing new regulatory documents that take into account the specifics of the design and manufacture of space control radar stations is justified. From an applied point of view, a number of specific scientific and practical solutions for ensuring the reliability of space control radar stations of a domestic developer which may be used in further developments are presented. It has been established that many terms in the modern theory of reliability do not have an applied focus and do not allow increasing the accuracy of classifying the state of an object. It is shown that the creation of new generations of space monitoring radar stations is accompanied by the emergence of new scientific and practical tasks unknown to science, the solution of which is connected with the use of new scientific ideas and with the own developments of component elements. It is shown that the use of co-temporal elemental base in the design of space monitoring radar creates difficulties in ensuring the reliability of hierarchical levels, since testing by existing standards is possible only for lower levels of hierarchy. It is established that the existing standards of general technical requirements and methods of control and testing for products and radar stations are not always identical and it is impossible to ensure, that applying a highly reliable element base, we shall receive the same radar station reliability. Obviously, the new stage of ensuring reliability must be associated with the individual reliability of the elements themselves. It justifies the importance of the scientific task associated with the choice of control parameters of the modern generation of radar stations in the conditions of the impossibility of applying traditional control, which requires the interruption of their operation. The temperature as a function of time is proposed as a parameter for estimating the state of products. A number of ways to ensure the reliability of new generations of space monitoring radar stations based on the use of new information signals, as well as reliability models built into the element base for creating a system for collecting, storing and analyzing information — the same for all stages of the radio electronic equipment. Conclusions. Taking into account the features of the design and manufacture of space monitoring radar stations and the practical experience of a domestic developer to solve reliability problems will ensure the creation of technology of highly reliable generations of space control radars and the development of stations of “high factory readiness”.

In the past 25 years, radar altimeters have been designed and operated primarily for ocean observation. However, over the past decade there has been a growing interest for altimetry measurements over inland waters. Studying lakes, reservoirs and rivers water level is of prime importance for the hydrology community to assess the Earth’s global resources of fresh water. Satellite altimetry is key to providing such global and continuous datasets of water surface height. Indeed, much progress has been made in altimeters capability to acquire quality measurements over inland waters. In this paper we present an overview of major technical evolutions of the tracking function of altimeters, from Jason-2/POS3 and SARAL/AltiKa to Jason-3/POS3B and Sentinel-3/SRAL and the improvements brought by the Open-Loop Tracking Command (OLTC) to extend the altimeter observation domain to inland waters. The OLTC is an on-board feature used to set the altimeter waveforms reception window, containing a priori elevation information built from a dedicated database of hydrological targets. This paper focuses on the use of OLTC for acquiring reliable water signal rather than discussing the accuracy of the retrieved height, which constitutes a further step in the altimeter data processing chain. We show how Jason-3 and Sentinel-3 altimetry missions are currently able to observe and monitor about 70,000 hydrological targets over the globe and how it is contributing in building a global dataset of inland waters level, in preparation for future missions such as Sentinel-6 and the Surface Water Ocean Topography mission (SWOT).

Summarization: Time is the fundamental measurement in satellite altimetry and the key parameter in building and keeping up a long-term, consistent, and reliable record for monitoring changes in sea level. Over the years, different time scales, although interconnected, have been used in altimetry and also in satellite positioning. This sometimes leads to inexplicit descriptions and ambiguous use of time and orbit coordinates as well as of their transformations between various reference and measuring systems. Altimetry satellites, like Sentinel-3, CryoSat-2, Jason-3, HY-2A/-2B, IceSat-2, etc., observe and practically realize ranges by measuring time differences between the transmission and reception of an electromagnetic wave (either radar or laser at present). Similar principles apply for global navigation satellite systems and for their terrestrial reference systems upon which altimetry is linked and tied together. Yet, the “meter” of any terrestrial reference systems is also defined by time. This work seeks to establish a standard reference system for “time” and “coordinates” in an effort to reach uniform and absolute standardization for satellite altimetry. It describes various errors generated from differences, discontinuities and interactions in time, frequency, range, time tagging, and reference coordinate frames. A new approach, called “fiducial reference measurements for altimetry”, is here given to examine ways to connect errors with metrology standards in order to improve the estimation of uncertainty budgets in ocean and water level observation by altimetry. Finally, the geocentric inertial reference system and the international atomic time are proposed in this paper for satellite altimetry observations and products. Presented on: Advances in Space Research

The Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign (ATOMIC), part of the larger experiment known as Elucidating the Role of Clouds-Circulation Coupling in Climate (EUREC4A), was held in the western Atlantic during the period 17 January–11 February 2020. This paper describes observations made during ATOMIC by the US National Oceanic and Atmospheric Administration's (NOAA) Lockheed WP-3D Orion research aircraft based on the island of Barbados. The aircraft obtained 95 h of observations over 11 flights, many of which were coordinated with the NOAA research ship R/V Ronald H. Brown and autonomous platforms deployed from the ship. Each flight contained a mixture of sampling strategies including high-altitude circles with frequent dropsonde deployment to characterize the large-scale environment, slow descents and ascents to measure the distribution of water vapor and its isotopic composition, stacked legs aimed at sampling the microphysical and thermodynamic state of the boundary layer, and offset straight flight legs for observing clouds and the ocean surface with remote sensing instruments and the thermal structure of the ocean with in situ sensors dropped from the plane. The characteristics of the in situ observations, expendable devices, and remote sensing instrumentation are described, as is the processing used in deriving estimates of physical quantities. Data archived at the National Center for Environmental Information include flight-level data such as aircraft navigation and basic thermodynamic information (NOAA Aircraft Operations Center and NOAA Physical Sciences Laboratory, 2020, https://doi.org/10.25921/7jf5-wv54); high-accuracy measurements of water vapor concentration from an isotope analyzer (National Center for Atmospheric Research, 2020, https://doi.org/10.25921/c5yx-7w29); in situ observations of aerosol, cloud, and precipitation size distributions (Leandro and Chuang, 2020, https://doi.org/10.25921/vwvq-5015); profiles of seawater temperature made with Airborne eXpendable BathyThermographs (AXBTs; NOAA Physical Sciences Laboratory, 2020a, https://doi.org/10.25921/pe39-sx75); radar reflectivity, Doppler velocity, and spectrum width from a nadir-looking W-band radar (NOAA Physical Sciences Laboratory, 2020c, https://doi.org/10.25921/n1hc-dc30); estimates of cloud presence, the cloud-top location, and the cloud-top radar reflectivity and temperature, along with estimates of 10 m wind speed obtained from remote sensing instruments operating in the microwave and thermal infrared spectral regions (NOAA Physical Sciences Laboratory, 2020b, https://doi.org/10.25921/x9q5-9745); and ocean surface wave characteristics from a Wide Swath Radar Altimeter (Prosensing, Inc., 2020, https://doi.org/10.25921/qm06-qx04). Data are provided as netCDF files following Climate and Forecast conventions.

Ionospheric irregularities can adversely affect the performance of Global Navigation Satellite System (GNSS). However, this opens the possibility of using GNSS as an effective ionospheric remote sensing tool. Despite ionospheric monitoring has been undertaken for decades, these irregularities in multiple spatial and temporal scales are still not fully understood. This paper reviews Virginia Tech’s recent studies on multi-scale ionospheric irregularities using ground-based and space-based GNSS observations. First, the relevant background of ionospheric irregularities and their impact on GNSS signals is reviewed. Next, three topics of ground-based observations of ionospheric irregularities for which GNSS and other ground-based techniques are used simultaneously are reviewed. Both passive and active measurements in high-latitude regions are covered. Modelling and observations in mid-latitude regions are considered as well. Emphasis is placed on the increased capability of assessing the multi-scale nature of ionospheric irregularities using other traditional techniques (e.g., radar, magnetometer, high frequency receivers) as well as GNSS observations (e.g., Total-Electron-Content or TEC, scintillation). Besides ground-based observations, recent advances in GNSS space-based ionospheric measurements are briefly reviewed. Finally, a new space-based ionospheric observation technique using GNSS-based spacecraft formation flying and a differential TEC method is demonstrated using the newly developed Virginia Tech Formation Flying Testbed (VTFFTB). Based on multi-constellation multi-band GNSS, the VTFFTB has been developed into a hardware-in-the-loop simulation testbed with external high-fidelity global ionospheric model(s) for 3-satellite formation flying, which can potentially be used for new multi-scale ionospheric measurement mission design.

Most of the part of India is already under water-stressed condition. In this regard, the continuous monitoring of the water levels (WL) and storage capacity of reservoirs, lakes, and rivers is very important for the estimation and utilization of water resources effectively. The long term ground observed WL of many of the water bodies is not easily available, which may be very critical for proper water resources management. Satellite radar altimetry is the remote sensing technique, which is being used to study sea surface height for the last 25 years. The advancement in radar technology with time has provided the opportunity to exploit the technique to retrieve the WL of inland water bodies. In the current study, an attempt has been made to generate long term time series on WL of around 29 geometrically complicated inland water bodies in India. These water bodies are mainly large reservoirs namely Ban Sagar, Balimela, Bargi, Bhakra, Gandhi Sagar, Hasdeo, Indravati, Jalaput, Kadana, Kolab, Mahi Bajaj, Maithon, Massanjore, Pong, Ramganga, Ranapratap Sagar, Rihand, Sardar Sarovar, Shivaji Sagar, Tilaiya, Ujjani, and Ukai. The WL of these water bodies was retrieved for around two decades using the European Remote-Sensing Satellite – 2 (ERS-2), ENVISAT Radar Altimeter – 2 (ENVISAT RA-2), and Saral-AltiKa altimeters data through Ice-1 retracking algorithm. Further, an attempt has also been made to estimate the WL of gauged/ungauged lakes namely Mansarovar, Pangong, Chilika, Bhopal, and Rann of Kutch over which Saral-AltiKa pass was there. As after July 2016, the SARAL-AltiKa is operating in the drifting orbit, systematic repeated observation of WL data of all reservoirs was not possible. The data of drifted tracks of Saral-AltiKa were tested for WL estimation of Ban Sagar reservoir. As the ERS-2, ENVISAT RA-2 and Saral-AltiKa all were having almost the same passing tracks, a long term WL series of these lakes could be generated from 1997 - 2016. However, at present only Sentinel – 3 is in orbit, the continuous altimeter based WL monitoring of some of these reservoirs (Gandhi Sagar, Nathsagar, Ranapratap, Ujjani, and Ukai) was attempted through Sentinel-3A satellite data from 2016 to 2018. The accuracy of the retrieved WL was than validated against the observed WL. In most of the reservoirs, a systematic bias was found due to the different characteristics and geoid height of each reservoir. The coefficient of determination, R2, value for a majority of reservoirs was as good as 0.9. In the case of ERS-2, the values of R2 varied for 0.44 to 0.97 with root mean square error (RMSE) in the range of 0.63 to 2.72m. These statistics improved with the ENVISAT RA-2 data analysis, the R2 value reached more than 0.90 for around 11 reservoirs. The highest, 0.99, for Hasdeo and Shivaji Sagar Reservoirs with RMSE of 0.44 and 0.56, respectively. Further, the accuracy improved with the analysis of Saral-AltiKa data. The R2 was always more than 0.9 for each reservoir and the lowest RMSE reduced to 0.03. Therefore, it can be said that the accuracy and consistency of WL retrieval through satellite altimetry has improved with time. Furthermore, the altimeter based retrieved WL may be used in hydrological studies and can contribute to better water resources management.

Two- and three-dimensional rock-penetrating-radar data were acquired on the wall of a pillar in an underground limestone mine. The objective was to test the ability of radar to image fractures and karst voids and to characterize their geometry, aperture, and fluid content, with the goal of mitigating mining hazards. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Large pillar wall topography was included in the steep-dip Kirchhoff migration algorithm because standard elevation corrections are inaccurate. The depth-migrated 250 MHz radar images illuminate fractures, karst voids, and the far wall of the pillar up to approximately 25 m depth into the rock, with a spatial resolution of

The Surface Water Ocean Topography mission (SWOT), scheduled for launch in 2021, is the first space-borne radar interferometer capable of providing wide-swath height maps of water surfaces with centimetric precision. In addition to its primary objectives in oceanography and hydrography, the SWOT instrument offers opportunities for other applications. Here, we explore the feasibility of sea ice freeboard and sea surface height retrievals in the ice-covered oceans from SWOT data. The quality of SWOT height estimates depends on the backscatter strength and number of samples used for multi-looking. We use near-nadir radar backscatter estimates from sea ice and water over the range of SWOT incidence angles to simulate SWOT height maps and assess the retrieval precision under different backscatter, surface type and roughness conditions. Unlike wind-roughened open water, the available observations suggest that backscatter over sea ice has a moderate dependence on look angle (specularity), and the backscatter of younger, flatter sea ice has a greater degree of specularity than older, more deformed and colder sea ice. To achieve a similar freeboard precision to conventional altimeters (∼3 cm) requires averaging over 15–40 km2 in the near- to mid-swath and 90–175 km2 in the far-swath for lower northern latitudes (

Introduction. Automotive radars are the main tools for providing traffic safety. The development of such radars involve a number of technical difficulties due to the manufacture of high-precision extremely high-frequency (EHF) printed circuit boards. To facilitate the process of creating such devices, the existing algorithms for radar information processing should be debugged using prototypes from manufacturers of mm-band transceivers. However, the parameters of such boards are not known in advance, and the actual operating conditions of the as-produced automotive radars raise new challenges to target tracking algorithms. Therefore, checking the performance of such boards is a relevant research problem.Aim. To evaluate the performance of a millimeter-wave automotive radar prototype and to test target tracking algorithms using this prototype.Materials and methods. An original target tracking method was used, which considers the constraints on the use of additional data sources about the radar carrier movement.Results. An experimental performance evaluation of a 77 GHz automotive radar prototype was carried out. The effectiveness of primary processing for the target class “vehicle” in the millimetre range was checked. Original algorithms for target tracking were proposed and tested.Conclusion. The obtained results show that the prototype board of a transceiver chip is capable of testing tracking algorithms without creating an own automotive radar prototype. Thus, the developmental process can be significantly shortened. Moreover, after creating a hardware solution, the developer obtains a reference device to test and configure an own product without using extremely expensive and rare EHF equipment.

The density–depth relationship of the Antarctic ice sheet is important for establishing a high-precision surface mass balance model and predicting future ice-sheet contributions to global sea levels. A new algorithm is used to reconstruct firn density and densification rate by inverting monostatic radio wave echoes from ground-operated frequency-modulated continuous wave radar data collected near four ice cores along the transect from Zhongshan Station to Dome A. The inverted density profile is consistent with the core data within 5.54% root mean square error. Due to snow redistribution, the densification rate within 88 km of ice core DT401 is correlated with the accumulation rate and varies greatly over horizontal distances of −3is reached decreases and densification rate increases in high-accumulation regions but decreases in low-accumulation regions. This inversion technique can be used to analyse more Antarctic radar data and obtain the density distribution trend, which can improve the accuracy of mass-balance estimations.

The thickness of a supraglacial layer is critical to the magnitude and time frame of glacier melt. Field-based, short pulse, ground-penetrating radar (GPR) has successfully measured debris thickness during a glacier's melt season, when there is a strong return from the ice–debris interface, but profiling with GPR in the absence of a highly reflective ice interface has not been explored. We investigated the performance of 960 MHz signals over 2 km of transects on Changri Nup Glacier, Nepal, during the post-monsoon. We also performed laboratory experiments to interpret the field data and investigate electromagnetic wave propagation into dry rocky debris. Laboratory tests confirmed wave penetration into the glacier ice and suggest that the ice–debris interface return was missing in field data because of a weak dielectric contrast between solid ice and porous dry debris. We developed a new method to estimate debris thicknesses by applying a statistical approach to volumetric backscatter, and our backscatter-based calculated thickness retrievals gave reasonable agreement with debris depths measured manually in the field (10–40 cm). We conclude that, when melt season profiling is not an option, a remote system near 1 GHz could allow dry debris thickness to be estimated based on volumetric backscatter.

Anti-ship missiles are a primary threat to naval targets because sea-skimming tactics enable the missile to approach the vessel while being obscured by sea clutter. In order to counteract this serious danger, the ship’s radar employs moving target indicator processing in order to detect and subsequently start the tracking of the incoming missile. In this paper, we set up a relevant simulation in order to examine the moving target indicator process for a naval scenario at horizon range which gives the greatest counter-reaction time, which usually is for very small grazing angles in a sea cluttered environment. Finally, the results of the simulator are beneficial to radar designers and educational professionals.

A ground-based scatterometer was installed on an alpine meadow over the Tibetan Plateau to study the soil moisture and temperature dynamics of the top soil layer and air–soil interface during the period August 2017–August 2018. The deployed system measured the amplitude and phase of the ground surface radar return at hourly and half-hourly intervals over 1–10 GHz in the four linear polarization combinations (vv, hh, hv, vh). In this paper we describe the developed scatterometer system, gathered datasets, retrieval method for the backscattering coefficient (σ0), and results of σ0. The system was installed on a 5 m high tower and designed using only commercially available components: a vector network analyser (VNA), four coaxial cables, and two dual-polarization broad-band gain horn antennas at a fixed position and orientation. We provide a detailed description on how to retrieve the backscattering coefficients for all four linear polarization combinations σpq0, where p is the received and q the transmitted polarization (v or h), for this specific scatterometer design. To account for the particular effects caused by wide antenna radiation patterns (G) at lower frequencies, σ0 was calculated using the narrow-beam approximation combined with a mapping of the function G2/R4 over the ground surface. (R is the distance between antennas and the infinitesimal patches of ground surface.) This approach allowed for a proper derivation of footprint positions and areas, as well as incidence angle ranges. The frequency averaging technique was used to reduce the effects of fading on the σpq0 uncertainty. Absolute calibration of the scatterometer was achieved with measurements of a rectangular metal plate and rotated dihedral metal reflectors as reference targets. In the retrieved time series of σpq0 for L-band (1.5–1.75 GHz), S-band (2.5–3.0 GHz), C-band (4.5–5.0 GHz), and X-band (9.0–10.0 GHz), we observed characteristic changes or features that can be attributed to seasonal or diurnal changes in the soil: for example a fully frozen top soil, diurnal freeze–thaw changes in the top soil, emerging vegetation in spring, and drying of soil. Our preliminary analysis of the collected σpq0 time-series dataset demonstrates that it contains valuable information on water and energy exchange directly below the air–soil interface – information which is difficult to quantify, at that particular position, with in situ measurement techniques alone. Availability of backscattering data for multiple frequency bands (raw radar return and retrieved σpq0) allows for studying scattering effects at different depths within the soil and vegetation canopy during the spring and summer periods. Hence further investigation of this scatterometer dataset provides an opportunity to gain new insights in hydrometeorological processes, such as freezing and thawing, and how these can be monitored with multi-frequency scatterometer observations. The dataset is available via https://doi.org/10.17026/dans-zfb-qegy (Hofste et al., 2021). Software code for processing the data and retrieving σpq0 via the method presented in this paper can be found under https://doi.org/10.17026/dans-xyf-fmkk (Hofste, 2021).

Forest ecosystems play a crucial role in mitigating global climate change by forming massive carbon sinks. Their carbon stocks and stock changes need to be quantified for carbon budget balancing and international reporting schemes. However, direct sampling and biomass weighing may not always be possible for quantification studies conducted in large forests. In these cases, indirect methods that use forest inventory information combined with remote sensing data can be beneficial. Synthetic aperture radar (SAR) images offer numerous opportunities to researchers as freely distributed remote sensing data. This study aims to estimate the amount of total carbon stock (TCS) in forested lands of the Kizildag Forest Enterprise. To this end, the actual storage capacities of five carbon pools, i.e. above- and below-ground, deadwood, litter, and soil, were calculated using the indirect method based on ground measurements of 264 forest inventory plots. They were then associated with the backscattered values from Sentinel-1 and ALOS-2 PALSAR-2 data in a Geographical Information System (GIS). Finally, TCS was separately modelled and mapped. The best regression model was developed using the HH polarization of ALOS-2 PALSAR-2 with an adjusted R2 of 0.78 (p −1. The map showed that the distribution of TCS was heterogenic across the study area. Carbon hotspots were mostly composed of pure stands of Anatolian black pine and mixed, over-mature stands of Lebanese cedar and Taurus fir. It was concluded that the total carbon stocks of forest ecosystems could be estimated using appropriate SAR images at acceptable accuracy levels for forestry purposes. The use of additional ancillary data may provide more delicate and reliable estimations in the future. Given the implications of this study, the spatiotemporal dynamics of carbon can be effectively controlled by forest management when coupled with easily accessible space-borne radar data.

Very short-term (0~3 h) radar-based quantitative precipitation forecasting (QPF), also known as nowcasting, plays an essential role in flash flood warning, water resource management, and other hydrological applications. A novel nowcasting method combining radar data and a model wind field was developed and validated with two hurricane precipitation events. Compared with several existing nowcasting approaches, this work attempts to enhance the prediction capabilities from two major aspects. First, instead of using a radar reflectivity field, this work proposes the use of the rainfall rate field estimated from polarimetric radar variables in the motion field derivation. Second, the derived motion field is further corrected by the Rapid Refresh (RAP) model field. With the corrected motion field, the future rainfall rate field is predicted through a linear extrapolation method. The proposed method was validated using two hurricanes: Harvey and Irma. The proposed work shows an enhanced performance according to statistical scores. Compared with the model only and centroid-tracking only approaches, the average probability of detection (POD) increases about 25% and 50%, the average critical success index (CSI) increases about 20% and 37%, and the average false alarm rate (FAR) decreases about 14% and 16%, respectively.

In diesem Beitrag wird ein neuartiges Sensorsystem für die Mensch-Roboter-Kollaboration vorgestellt, das auf Millimeterwellen-Radartechnologie basiert und entscheidende Vorteile gegenüber aktuell genutzten Sensortechnologien bietet. Es besteht aus einem Radarsensorring, der beliebig an Roboterachsen angebracht werden kann, einem Sensorknoten und einer Auswertungseinheit. Das System wurde im Zuge des BMBF-geförderten Forschungsprojekts RoKoRa entwickelt und in einem funktionalen Demonstrator realisiert.

Eastern China is one of the most economically developed and densely populated areas in the world. Due to its special geographical location and climate, eastern China is affected by different weather systems, such as monsoons, shear lines, typhoons, and extratropical cyclones. In the near future, the rainfall rate becomes difficult to predict precisely due to these systems. Traditional physics-based methods such as numerical weather prediction (NWP) tend to perform poorly on nowcasting problems due to the spin-up issue. Moreover, various meteorological stations are distributed in this region, generating a large amount of observation data every day, which have great potential for application to data-driven methods. Thus, it is important to train a data-driven model from scratch that is suitable for the specific weather situation of eastern China. However, due to the high degrees of freedom and nonlinearity of machine learning algorithms, it is difficult to add physical constraints. Therefore, with the intention of using various kinds of data as a proxy for physical constraints, we collected three kinds of data (radar, satellite, and precipitation data) in the flood season from 2017 to 2018 in this area and preprocessed them into tensors (256×256) that cover eastern China with a domain of 12.8×12.8∘. The developed multisource data model (MSDM) combines the optical flow, random forest, and convolutional neural network (CNN) algorithms. It treats the precipitation nowcasting task as an image-to-image problem, which takes radar and satellite data with an interval of 30 min as inputs and predicts radar echo intensity with a lead time of 30 min. To reduce the smoothing caused by convolutions, we use the optical flow algorithm to predict satellite data in the following 120 min. The predicted radar echoes from the MSDM together with satellite data from the optical flow algorithm are recursively implemented in the MSDM to achieve a 120 min lead time. The MSDM predictions are comparable to those of other baseline models with a high temporal resolution of 6 min. To solve blurry image problems, we applied a modified structural similarity (SSIM) index as a loss function. Furthermore, we use the random forest algorithm with predicted radar and satellite data to estimate the rainfall rate, and the results outperform those of the traditional, nonlinear radar reflectivity factor and rainfall rate (Z–R) relationships that use logarithmic functions. The experiments confirm that machine learning with multisource data provides more reasonable predictions and reveals a better nonlinear relationship between radar echo and precipitation rate. Apart from developing complicated machine learning algorithms, exploiting the potential of multisource data will yield more improvements.

Radar sounding of the thickness of temperate glaciers is challenged by substantial volume scattering, surface scattering and high attenuation rates. Lower-frequency radar sounders are often deployed to mitigate these effects, but the lack of a global synthesis of their success limits progress in system and survey design. Here we extend a recent global compilation of glacier thickness measurements (GlaThiDa) with the center frequency for radar-sounding surveys. From a maximum reported thickness of ∼ 1500 m near 1 MHz, the maximum thickness sounded decreases by ∼ 500 m per frequency decade. Between 25–100 MHz, newer airborne radar sounders generally outperform older, ground-based ones. Based on globally modeled glacier thicknesses, we conclude that a multi-element, ≤30 MHz airborne radar sounder could survey most temperate glaciers more efficiently.

Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

It was found that a newly developed portable collision alert radar receives reflections from the ground while flying. In this paper a method is developed that uses range and Doppler information fro...

The paper focuses on space system design aspects related to an end-to-end demonstration mission, aiming at showing the feasibility of a Formation Flying Synthetic Aperture Radar (FF-SAR) with microsatellite class platforms (~100 kg). Trajectory design approaches that can fulfil payload requirements are addressed to enable selected FF-SAR applications. The exploitation of these applications relies on suitable combinations of FF-SAR techniques like Signal-to-Noise Ratio (SNR) enhancement, High-Resolution Wide Swath (HRWS) SAR imaging, and Coherence Resolution Enhancement (CRE). In this framework, a cluster of 3 micro-satellites, working in X-band, flying in a Low Earth Orbit (LEO) close-formation, has been designed as a candidate end-to-end system demonstration mission. One satellite embarks a Transmitting-Receiving (Tx/Rx) radar, i.e. it is a monostatic SAR. The other two satellites are Receiving-only platforms. Critical design aspects related to spacecraft subsystems and formation-flying analysis are addressed to confirm the technical feasibility of the spaceborne distributed system implementing the FF-SAR principle.

The mesosphere and lower thermosphere (MLT) is a critical region that must be accurately reproduced in general circulation models (GCMs) that aim to include the coupling between the lower and middle atmosphere and the thermosphere. An accurate representation of the MLT is thus important for improved climate modelling and the development of a whole atmosphere model. This is because the atmospheric waves at these heights are particularly large, and so the energy and momentum they carry is an important driver of climatological phenomena through the whole atmosphere, affecting terrestrial and space weather. The Extended Unified Model (ExUM) is the recently developed version of the Met Office's Unified Model which has been extended to model the MLT. The capability of the ExUM to model atmospheric winds and tides in the MLT is currently unknown. Here, we present the first study of winds and tides from the ExUM. We make a comparison against meteor radar observations of winds and tides from 2006 between 80 and 100 km over two radar stations – Rothera (68∘ S, 68∘ W) and Ascension Island (8∘ S, 14∘ W). These locations are chosen to study tides in two very different tidal regimes – the equatorial regime, where the diurnal (24 h) tide dominates, and the polar regime, where the semi-diurnal (12 h) tide dominates. The results of this study illustrate that the ExUM is capable of reproducing atmospheric winds and tides that capture many of the key characteristics seen in meteor radar observations, such as zonal and meridional wind maxima and minima, the increase in tidal amplitude with increasing height, and the decrease in tidal phase with increasing height. In particular, in the equatorial regime some essential characteristics of the background winds, tidal amplitudes and tidal phases are well captured but with significant differences in detail. In the polar regime, the difference is more pronounced. The ExUM zonal background winds in austral winter are primarily westward rather than eastward, and in austral summer they are larger than observed above 90 km. The ExUM tidal amplitudes here are in general consistent with observed values, but they are also larger than observed values above 90 km in austral summer. The tidal phases are generally well replicated in this regime. We propose that the bias in background winds in the polar regime is a consequence of the lack of in situ gravity wave generation to generate eastward fluxes in the MLT. The results of this study indicate that the ExUM has a good natural capability for modelling atmospheric winds and tides in the MLT but that there is room for improvement in the model physics in this region. This highlights the need for modifications to the physical parameterization schemes used in the model in this region – such as the non-orographic spectral gravity wave scheme – to improve aspects such as polar circulation. To this end, we make specific recommendations of changes that can be implemented to improve the accuracy of the ExUM in the MLT.

In recent years, the concept of multistatic meteor radar systems has attracted the attention of the atmospheric radar community, focusing on the mesosphere and lower thermosphere (MLT) region. Recently, there have been some notable experiments using such multistatic meteor radar systems. Good spatial resolution is vital for meteor radars because nearly all parameter inversion processes rely on the accurate location of the meteor trail specular point. It is timely then for a careful discussion focused on the error distribution of multistatic meteor radar systems. In this study, we discuss the measurement errors that affect the spatial resolution and obtain the spatial-resolution distribution in three-dimensional space for the first time. The spatial-resolution distribution can both help design a multistatic meteor radar system and improve the performance of existing radar systems. Moreover, the spatial-resolution distribution allows the accuracy of retrieved parameters such as the wind field to be determined.

The last decade has witnessed important advancements in the field of computer vision and scene understanding, enabling applications such us autonomous vehicles. Radar is a commonly adopted sensor in automotive industry, but its suitability to machine learning techniques still remains an open question. In this work, we propose a neural network (NN) based solution to efficiently process radar data. We introduce RadarPCNN, an architecture specifically designed for performing semantic segmentation on radar point clouds. It uses PointNet$$++$$ + + as a building-block—enhancing the sampling stage with mean-shift—and an attention mechanism to fuse information. Additionally, we propose a machine learning radar pre-processing module that confers the network the ability to learn from radar features. We show that our solutions are effective, yielding superior performance than the state-of-the-art.

The ability to detect convective regions and to add latent heating to drive convection is one of the most important additions to short-term forecast models such as National Oceanic and Atmospheric Administration's (NOAA's) High-Resolution Rapid Refresh (HRRR) model. Since radars are most directly related to precipitation and are available in high temporal resolution, their data are often used for both detecting convection and estimating latent heating. However, radar data are limited to land areas, largely in developed nations, and early convection is not detectable from radars until drops become large enough to produce significant echoes. Visible and infrared sensors on a geostationary satellite can provide data that are more sensitive to small droplets, but they also have shortcomings: their information is almost exclusively from the cloud top. Relatively new geostationary satellites, Geostationary Operational Environmental Satellite-16 and Satellite-17 (GOES-16 and GOES-17), along with Himawari-8, can make up for this lack of vertical information through the use of very high spatial and temporal resolutions, allowing better observation of bubbling features on convective cloud tops. This study develops two algorithms to detect convection at vertically growing clouds and mature convective clouds using 1 min GOES-16 Advanced Baseline Imager (ABI) data. Two case studies are used to explain the two methods, followed by results applied to 1 month of data over the contiguous United States. Vertically growing clouds in early stages are detected using decreases in brightness temperatures over 10 min. For mature convective clouds which no longer show much of a decrease in brightness temperature, the lumpy texture from rapid development can be observed using 1 min high spatial resolution reflectance data. The detection skills of the two methods are validated against Multi-Radar/Multi-Sensor System (MRMS), a ground-based radar product. With the contingency table, results applying both methods to 1-month data show a relatively low false alarm rate of 14.4 % but missed 54.7 % of convective clouds detected by the radar product. These convective clouds were missed largely due to less lumpy texture, which is mostly caused by optically thick cloud shields above.

Coal mining has an important influence on the soil water content in the vadose zone. In this study, during April, August and October 2015 and June 2016, ground-penetrating radar was used to detect the soil water content of the shallow sand layer (

Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

Meteor phenomena cause ionized plasmas that can be roughly divided into two distinctly different regimes: a dense and transient plasma region co-moving with the ablating meteoroid and a trail of diffusing plasma left in the atmosphere and moving with the neutral wind. Interferometric radar systems are used to observe the meteor trails and determine their positions and drift velocities. Depending on the spatial configuration of the receiving antennas and their individual gain patterns, the voltage response can be the same for several different plane wave directions of arrival (DOAs), thereby making it impossible to determine the correct direction. A low signal-to-noise ratio (SNR) can create the same effect probabilistically even if the system contains no theoretical ambiguities. Such is the case for the standard meteor trail echo data products of the Sodankylä Geophysical Observatory SKiYMET all-sky interferometric meteor radar. Meteor trails drift slowly enough in the atmosphere and allow for temporal integration, while meteor head echo targets move too fast. Temporal integration is a common method to increase the SNR of radar signals. For meteor head echoes, we instead propose to use direct Monte Carlo (DMC) simulations to validate DOA measurements. We have implemented two separate temporal integration methods and applied them to 2222 events measured by the Sodankylä meteor radar to simultaneously test the usefulness of such DMC simulations on cases where temporal integration is possible, validate the temporal integration methods, and resolve the ambiguous SKiYMET data products. The two methods are the temporal integration of the signal spatial correlations and matched-filter integration of the individual radar channel signals. The results are compared to Bayesian inference using the DMC simulations and the standard SkiYMET data products. In the examined data set, ∼ 13 % of the events were indicated as ambiguous. Out of these, ∼ 13 % contained anomalous signals. In ∼ 95 % of all ambiguous cases with a nominal signal, the three methods found one and the same output DOA, which was also listed as one of the ambiguous possibilities in the SkiYMET analysis. In all unambiguous cases, the results from all methods concurred.

Monsoon onset is highly variable year‐to‐year and difficult to predict. Here, Doppler radar data shed light on convective development in the days and weeks around the 2016 monsoon onset at multiple sites across India. We consider this convective development with relation to large‐scale dynamic and thermodynamic changes associated with onset.

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR i...

An improved representation of 3-D air motion and precipitation structure through forecast models and assimilation of observations is vital for improvements in weather forecasting capabilities. However, there are few independent data to properly validate a model forecast of precipitation structure when the underlying dynamics are evolving on short convective timescales. Using data from the JPL Ku/Ka-band Airborne Precipitation Radar (APR-2) and the 2 µm Doppler Aerosol Wind (DAWN) lidar collected during the 2017 Convective Processes Experiment (CPEX), the NASA Unified Weather Research and Forecasting (WRF) Ensemble Data Assimilation System (EDAS) modeling system was used to quantify the impact of high-resolution sparsely sampled DAWN measurements on the analyzed variables and on the forecast when the DAWN winds were assimilated. Overall, the assimilation of the DAWN wind profiles had a discernible impact on the wind field as well as the evolution and timing of the 3-D precipitation structure. Analysis of individual variables revealed that the assimilation of the DAWN winds resulted in important and coherent modifications of the environment. It led to an increase in the near-surface convergence, temperature, and water vapor, creating more favorable conditions for the development of convection exactly where it was observed (but not present in the control run). Comparison to APR-2 and observations by the Global Precipitation Measurement (GPM) satellite shows a much-improved forecast after the assimilation of the DAWN winds – development of precipitation where there was none, more organized precipitation where there was some, and a much more intense and organized cold pool, similar to the analysis of the dropsonde data. The onset of the vertical evolution of the precipitation showed similar radar-derived cloud-top heights, but delayed in time. While this investigation was limited to a single CPEX flight date, the investigation design is appropriate for further investigation of the impact of airborne Doppler wind lidar observations upon short-term convective precipitation forecasts.

Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.4458868 (Sun and Fu, 2021).

Crop type related information is very essential for various planning and decision support activities in everyday life especially for early forecast and monitoring of food production. Though smallholder farming areas are profound food producers, mapping crop types is mainly constrained by their inherent characteristics like fragmentation (small farm size), rugged terrain, and presence of thick clouds in the growing season. More importantly, crops mixed dominance of the landscape coupled with fragmented holdings, crops behave different phenological characteristics which mostly constrains conventional mapping techniques for crop type mapping. Therefore, the main objective of this study was mapping crop types using all-weather time-series Synthetic Aperture Radar (SAR) with time-weighted Dynamic Time Warping that accounts for phenological development of crops. The study has used Sentinel-1 dual polarimetry (VV, VH) and TerraSAR-X single polarimetry (HH) images. Basic Registration of Crop Plots (BRP) dataset was used as a reference for training and validation. Obtained imagery was passed through a series of pre-processing operations. As Sentinel-1 imagery has dual polarimetry bands, derived features (Ratio, Modified Radar Vegetation Index, and Dual Polarimetric Soil Vegetation Index) were computed. Additionally, polarimetric decomposition was also undertaken. Within stated broader objective, a detailed analysis was done to know crop-specific responses for incident radar signal, to understand the capability of Time-Weighted Dynamic Time Warping for crop type mapping, implications of using either only backscatter bands and inclusion of derived features and decomposed polarimetric features on mapping accuracy of crops. In addition to these, under broader dynamic time warping, two further model improvement strategies (Variable Time Weight Dynamic Time Warping and Angular Metric for Shape Similarity) were also tested for performance. More importantly, the study has investigated an ensemble classifier that integrates TerraSAR-X and Sentinel-1 classification outputs for synergistic use of both sensing systems for crop type mapping. From these analyses, the study has come up with promising findings that show potentials of SAR imagery with time-weighted Dynamic Time Warping for crop type mapping. It has also clearly demonstrated predictive capabilities of either using dual polarimetry or single polarimetry SAR datasets for mapping crops in smallholder farming areas. Finally, by considering achieved outputs and existing caveats on this study, to refine the findings, further works were also recommended.