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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.

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.

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...

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).

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.

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

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.

In this paper, based on the fusion of Lidar and Radar measurement data, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. Therefore, the merits of both sensors are combined using the proposed fusion approach to provide both pose and velocity data for objects moving in roads precisely. The Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) clustering algorithm is used to detect objects and estimate their centroids from the lidar and radar raw data. Then, the estimation of the object’s velocity as well as determining its corresponding geometrical shape is performed by the RANdom SAmple Consensus (RANSAC) algorithm. The proposed technique is implemented using the high-performance language C+⁣+ and utilizes highly optimized math and optimization libraries for best real-time performance. The performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians.

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.

How deep neural networks can process millions of weather radar data points to help researchers monitor continental-scale bird migration.

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.

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...

Introduction. Synthetic aperture radars (SAR) are important components of aviation-based systems for remote sensing of the Earth. The current level of such systems allows simultaneous radar surveys in several frequency ranges. Such surveys require complexing of the images formed in each of the frequency channels, which task is yet to be resolved.Aim. To review the formation principles and methods for joint processing of images using space and aviation-based multi-band synthetic aperture radar systems.Materials and methods. The methodology of systems analysis, involving the integral stages of decomposition, analysis and synthesis, was used. Decomposition of integrating multi-band radar images was performed considering the effect of various factors on the characteristics of radar images in different frequency ranges. Such factors include the principles of radar imaging, issues of radar images of multi-band synthetic aperture radars with real characteristics, and complexing levels.Results. According to the classical systems approach, the results of review and analysis are corresponded by appropriate conclusions on the shortcomings of each decomposition element and the synthesis of a proposal for achieving the goal. It was shown that joint processing of multi-band radar images can be carried out at the levels of signals, pixels, features and solutions, as well as their aggregates. Each approach is characterised by its shortcomings, which impede implementation of full integration of multi-band radar images without loss of information, which is due to the absence of information redundancy of radar images, compared to, e.g., optical images.Conclusion. Recommendations on the application of a particular method and the synthesis of a system for radar complexing images based on the texture-fractal approach were formulated. Directions for further work meeting all the requirements for completeness, reliability and information content of remote sensing of the Earth were outlined.

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 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.

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...

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.

Remote sensing techniques have been used to study and track wildfire smoke plume structure and evolution; however, knowledge gaps remain because of the limited availability of observational datasets aimed at understanding fine-scale fire–atmosphere interactions and plume microphysics. Meteorological radars have been used to investigate the evolution of plume rise in time and space, but highly resolved plume observations are limited. In this study, we present a new mobile millimeter-wave (Ka band) Doppler radar system acquired to sample the fine-scale kinematics and microphysical properties of active wildfire smoke plumes from both wildfires and large prescribed fires. Four field deployments were conducted in autumn of 2019 during two wildfires in California and one prescribed burn in Utah. Radar parameters investigated in this study include reflectivity, radial velocity, Doppler spectrum width, differential reflectivity ZDR, and copolarized correlation coefficient ρHV. Observed radar reflectivity ranged between −15 and 20 dBZ in plume, and radial velocity ranged from 0 to 16 m s−1. Dual-polarimetric observations revealed that scattering sources within wildfire plumes are primarily nonspherical and oblate-shaped targets as indicated by ZDR values measuring above 0 and ρHV values below 0.8 within the plume. Doppler spectrum width maxima were located near the updraft core region and were associated with radar reflectivity maxima.

An ability to accurately detect convective regions is essential for initializing models for short-term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high-temporal-resolution data are mostly available over land, and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface or cloud growth that occurs over periods of 30 min to an hour. This study represents a proof of concept that artificial intelligence (AI) is able, when given high-spatial- and high-temporal-resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses a rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds and resulted in a reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

Radar Corner Reflectors (CR) are increasingly used as reference targets for land surface deformation measurements with the Interferometric Synthetic Aperture Radar (InSAR) technique. When co-located with ground-based Global Navigation Satellite Systems (GNSS) infrastructure, InSAR observations at CR can be used to integrate relative measurements of surface deformation into absolute reference frames defined by GNSS. However, CR are also a potential source of GNSS multipath effects and may therefore have a detrimental effect on the GNSS observations. In this study, we compare daily GNSS coordinate time series and 30-second signal-to-noise ratio (SNR) observations for periods before and after CR deployment at a GNSS site. We find that neither the site coordinates nor the SNR values are significantly affected by the CR deployment, with average changes being within 0.1 mm for site coordinates and within 1 % for SNR values. Furthermore, we generate empirical site models by spatially stacking GNSS observation residuals to visualise and compare the spatial pattern in the surroundings of GNSS sites. The resulting stacking maps indicate oscillating patterns at elevation angles above 60 degrees which can be attributed to the CR deployed at the analysed sites. The effect depends on the GNSS antenna used at a site with the magnitude of multipath patterns being around three times smaller for a high-quality choke ring antenna compared to a ground plane antenna without choke rings. In general, the CR-induced multipath is small compared to multipath effects at other GNSS sites located in a different environment (e. g. mounted on a building).

Ground-penetrating radar (GPR) has shown great potential for asphalt concrete density prediction used in quality control and quality assurance. One challenge of continuous GPR measurements is that the measured dielectric constant could be affected by signal stability and antenna height. This would jeopardize the accuracy of the asphalt concrete density prediction along the pavement. In this study, signal instability and shifting antenna height during continuous real-time GPR measurements were identified as main sources of error. After using a bandpass filter to preprocess the signal, a least-square adaptive filter, using gradient descent and least mean square methods, was developed to reconstruct the received signal to improve its stability. In addition, simulations were performed to evaluate the impact of geometric spreading caused by shifting antenna height during testing. A height correction was developed using a power model to correct the height-change impact. The proposed filter and height-correction method were assessed using static and dynamic tests. The least-square adaptive filter improved signal stability by 50% and the height-correction method removed the effect of shifting antenna height almost entirely.

Quantitative precipitation estimation (QPE) is a difficult task, particularly in complex topography, and requires the adjustment of empirical relations between radar observables and precipitation quantities, as well as methods to transform observations aloft to estimations at the ground level. In this work, we tackle this classical problem with a new twist, by training a random forest (RF) regression to learn a QPE model directly from a large database comprising 4 years of combined gauge and polarimetric radar observations. This algorithm is carefully fine-tuned by optimizing its hyperparameters and then compared with MeteoSwiss' current operational non-polarimetric QPE method. The evaluation shows that the RF algorithm is able to significantly reduce the error and the bias of the predicted precipitation intensities, especially for large and solid or mixed precipitation. In weak precipitation, however, and despite a posteriori bias correction, the RF method has a tendency to overestimate. The trained RF is then adapted to run in a quasi-operational setup providing 5 min QPE estimates on a Cartesian grid, using a simple temporal disaggregation scheme. A series of six case studies reveal that the RF method creates realistic precipitation fields, with no visible radar artifacts, that appear less smooth than the original non-polarimetric QPE and offers an improved performance for five out of six events.

Slope correction is important to improve the accuracy of satellite radar elevation measurements by mitigating the slope-induced error (SE), especially over uneven ground surfaces. Although several slope correction methods have been proposed, guidance in the form of stepwise algorithm on how to implement these methods in processing radar altimetric data at the coding level, and the differences among these methods need to be presented and discussed systematically. In this paper, three existing types of slope correction methods—the direct method (DM), intermediate method (IM), and relocation method (RM, further divided into RM1 and RM2)—are described in detail. In addition, their main differences and features for various scientific applications are analyzed. We conduct a systematic experiment with CryoSat-2 Low Resolution Mode (LRM) data in a physically stable area around Dome Argus in East Antarctica, where in-situ measurements were available for comparison. The slope correction is implemented separately using the three methods, with the latest high-accuracy Reference Elevation Model of Antarctica (REMA) as the a-priori topography model. The bias and precision of the slope-corrected CryoSat-2 data results from the RM2 is −0.18 ± 0.86 m based on the comparison with the field Global Navigation Satellite System (GNSS) data. The results from the RM2 indicate higher precision compared to those from the RM1. According to the correlation analysis of the slope-corrected CryoSat-2 data results (RM1 and RM2), the bias enlarges and the precision becomes worse when the surface slope increases from 0 to 0.85°. After a comprehensively comparative analysis, we find that the results from the RM1 and RM2 are superior in precision (0.93 m and 0.86 m) with respect to the GNSS data. The relatively low precision (1.22 m) from the IM is due to the potential error from the a-priori digital elevation model (DEM). The DM has the lowest precision (2.66 m). Another experiment over rough topography in West Antarctica is carried out for comparison, especially between the RM1 (precision of 15.27 m) and RM2 (precision of 16.25 m). In general, the RM is recommended for the SE elimination among the three methods. Moreover, the RM2 is firstly considered over smooth topography due to the superior performance in bias and precision, while the RM1 is more suggested over the rough topography because of the slightly smaller bias and better precision. The IM relies much on the accuracy of the a-prior DEM and is not usually recommended, because of the strict requirement in the sampling time between the radar altimetry data and the a-priori DEM to avoid any surface change over time.

This paper describes a small unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) system using low-cost radar (5–6 GHz), position (GNSS/RTK) and attitude (IMU) sensors for the generation of high-resolution images. Measurements using straight as well as highly curved flight trajectories and varying flight speeds are presented, showing range and cross-range lobe-widths close to the theoretical limits. An analysis of the improvements obtained by the use of attitude angles (roll, pitch, and yaw), to correct for the relative offsets in antenna positions as the UAV moves, is included. A capability to generate SAR images onboard with the back-projection algorithm has been implemented using a GPU accelerated single-board computer. Generated images are transmitted to ground using a Wi-Fi data link.

Seagrass provides numerous valuable ecosystem services across a wide range of climatic regions. However, in terms of area and habitat, this resource is in decline globally and there is an urgent ne...

Cavities under urban roads have increasingly become a great threat to traffic safety in many cities. As a quick, effective, and high-resolution geophysical method, ground-penetrating radar (GPR) has been widely used to detect and image near-surface objects. However, the interpretation of field GPR data remains challenging. For example, it is hard to distinguish reflections caused by road cavities or other urban utilities with a conventional 2D GPR survey. The superiority of 3D GPR in data interpretation is demonstrated by a laboratory experiment. Two pipes and a glass-made cavity buried in a sandpit show similar hyperbolic reflections in the 2D GPR profiles; thus, they are difficult to discriminate. In contrast, their geometric shapes and dimensions are readily identified in the 3D image reconstructed from the synthetic 3D GPR data set. Based on these ideas, we have developed a car-mounted 3D GPR system with two antenna arrays oriented in different polarization directions and have detected more than 100 cavities in three Chinese cities over the past year. The field data of two such cavities are presented. As a result, the cavity depth, horizontal size, and height can be accurately estimated from the 3D GPR data set. Laboratory and field experimental results indicate that 3D GPR possesses potential in the detection and recognition of road cavities and utilities in the complex urban environment.

Missing information in ground-penetrating radar (GPR) data is a commonly encountered phenomenon during field measurements and it highly affects the subsequent processing steps of the GPR data, such...

This work asses a way to improve the detection of pedestrians, workers or cyclists, but also other static elements on the road. These elements due to its low radar cross-section can be masked by other larger objects close to them, when they are illuminated by an automotive radar. Rotating backscatter is proposed to enhance the detection with automotive FMCW radars due to the introduction of a differentiable micro-Doppler signature. A comprehensive theory of operation is explained in this paper. Simulated and experimental results are presented at 24 GHz for transponder detection in presence of strong clutter in range-velocity maps.

In this paper we describe a technique for measuring the tropospheric wind profile using a 35-GHz Doppler profiler. The parameters of the equipment and the accuracy of measurements of the wind parameters are given. An algorithm for estimating wind pulsations at various altitudes is described. The measurement results are compared with the data obtained on the meteorological mast. Examples of measurements under various meteorological conditions, including the registration of jet streams, are given. Differences between experimental and theoretical results when measuring the radar reflectivity of clear sky signals are discussed. Statistical data on the probability of registering radar reflections in various seasons of the year are presented.

This paper describes an innovative method for processing nadir altimeter data acquired in Synthetic Aperture Radar (SAR) mode, enhancing the system performances over open ocean. Similarly to the current SAR data processing scheme, the so-called LR-RMC (Low Resolution with Range Migration Correction) method, originally designed by Phalippou and Demeester (2011), includes Doppler beam forming, Doppler shift correction and range correction. In LR-RMC, however, an alternative and less complex averaging (stacking) operation is used so that all the Doppler beams produced in a radar cycle (4 bursts of 64 beams for the open-burst Sentinel-3-mode altimeter) are incoherently combined to form a multi-beam echo. In that manner, contrarily to the narrow-band SAR technique, the LR-RMC processing enlarges the effective footprint to average out the effects of surface waves and particularly those from small sub-mesoscale structures (

In this paper, a tailoring process for a High Frequency Surface Wave Radar (HFSWR) transmitter antenna array is presented. The process is done in order to achieve optimal HFSWR performance in equatorial environments, defined by a low altitude coastline, frequent storms, and heavy rainfall which lead to frequent site flooding. Simultaneously, the price of the final design needed to be as low as possible in order to make it affordable for developing countries, since the majority of countries in the equatorial area fits in that category. Initially, the tailoring process was started with mathematical formulations, continued in EM simulation software, and then tested in the field. Based on the findings obtained during the tailoring process, the transmitting antenna arrays are installed and the HFSWRs are operationally used afterward. Final confirmation of the tailoring process presented in this paper came through everyday usage of the installed HFSWRs during a period that lasted several years in the Equatorial environment, namely the Gulf of Guinea.

The paper considers the implementation of algorithms for automatic search for signals scattered by meteor trails according to EKB ISTP SB RAS radar data. In general, the algorithm is similar to the algorithms adopted in specialized meteor systems. The algorithm is divided into two stages: detecting a meteor echo and determining its parameters. We show that on the day of the maximum Geminid shower, December 13, 2016, the scattered signals detected by the algorithm are foreshortening and correspond to scattering by irregularities extended in the direction of the meteor shower radiant. This confirms that the source of the signals detected by the algorithm is meteor trails. We implement an additional program for indirect trail height determination. It uses a decay time of echo and the NRLMSIS-00 atmosphere model to estimate the trail height. The dataset from 2017 to 2019 is used for further testing of the algorithm. We demonstrate a correlation in calculated Doppler velocity between the new algorithm and FitACF. We present a solution of the inverse problem of reconstructing the neutral wind velocity vector from the data obtained by the weighted least squares method. We compare calculated speeds and directions of horizontal neutral winds, obtained in the three-dimensional wind model, and the HWM-14 horizontal wind model. The algorithm allows real-time scattered signal processing and has been put into continuous operation at the EKB ISTP SB RAS radar.

Oil spills cause great environmental damage and the ability to identify them accurately is important to prompt oil spill response. A power fitting method of radar echo is proposed in this paper to ...

Assessing landslide activity at large scales has historically been a challenging problem. Here, we present a different approach on radar coherence and normalized difference vegetation index (NDVI) analyses – metrics that are typically used to map landslides post-failure – and leverage a time series analysis to characterize the pre-failure activity of the Mud Creek landslide in California. Our method computes the ratio of mean interferometric coherence or NDVI on the unstable slope relative to that of the surrounding hillslope. This approach has the advantage that it eliminates the negative impacts of long temporal baselines that can interfere with the analysis of interferometric synthetic aperture (InSAR) data, as well as interferences from atmospheric and environmental factors. We show that the coherence ratio of the Mud Creek landslide dropped by 50 % when the slide began to accelerate 5 months prior to its catastrophic failure in 2017. Coincidentally, the NDVI ratio began a near-linear decline. A similar behavior is visible during an earlier acceleration of the landslide in 2016. This suggests that radar coherence and NDVI ratios may be useful for assessing landslide activity. Our study demonstrates that data from the ascending track provide the more reliable coherence ratios, despite being poorly suited to measure the slope's precursory deformation. Combined, these insights suggest that this type of analysis may complement traditional InSAR analysis in useful ways and provide an opportunity to assess landslide activity at regional scales.

Geophysical techniques can be successfully applied towards the detection of buried explosive devices, the ground-penetrating radar (GPR) being an example of one such method. This technology works t...

Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from quasi-vertical profiles (QVPs). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined, and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the National Centre for Atmospheric Science (NCAS) X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations.

This study investigates the impact of the October 2016 flash floods in urban areas located in selected catchments along the Nile Valley and Red Sea regions for the assessment and the proposal of feasible mitigation measures. Optical and radar satellite images acquired before and shortly after flash flood events were analyzed to detect and map the active contributing channels, the interfering urban areas, the response of natural depressions and storage areas, and the conveying channels to the flash floods. The quantitative estimates of October 2016 flash floods parameters including total rainfall depths, the active channel cross-sectional areas, and the spatial extent of storage areas of the accumulated flood water were measured. Various hydrological parameters were extracted using the Digital Elevation Model (DEM) for the estimation of water volumes of largely inundated depressions. It was determined that the October 2016 flash floods were devastating in the Ras Gharib area; however, these floods were properly managed at both the New El Minya and New Qena urban areas. In contrast, the flash floods at the Al-Saaf and Atfih urban area require urgent mitigation measures given that wastewater depressions and sanitary facilities are prevalent in the downstream reaches of the studied drainage basins.

We present a modified methodology for phase-resolved surface wave reconstruction from incoherent X-band marine radar images. The method is based on the linear wave theory and uses the linear dispersion relation to extract the valuable signals associated with gravity waves. A parameter optimization of the proposed modification is performed based on simulated synthetic radar images. The quantitative comparisons in the accuracy of the standard and modified reconstruction methods are made for both simulated and real radar images. The correlation coefficient between reconstructed and true wave elevations is improved up to 0.9–0.92 for the present modified method from 0.69 to 0.74 for the standard method for the simulated sea surfaces. The wave spectra reconstructed from the real X-band radar measurements are in good agreement with those obtained from the independent point measurement by Miros RangeFinder for both unimodal and bimodal seas.

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation, but also on its type. Uncertainty related to determination of the precipitation type (PT) leads to financial losses in many areas of human activity, such as the power industry, agriculture, transportation, and many more. In this study, we use machine learning (ML) algorithms with the data fusion approach to more accurately determine surface PT. Based on surface synoptic observations, ERA5 reanalysis, and radar data, we distinguish between liquid, mixed, and solid precipitation types. The study domain considers the entire area of Poland and a period from 2015 to 2017. The purpose of this work is to address the question: “How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?” Despite testing 33 parameters, it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0–1000 m above ground level (AGL) layer contains most of the signal needed to determine surface PT. The accrued probability of detection for liquid, solid, and mixed PTs according to the developed Random Forest model is 98.0%, 98.8%, and 67.3%, respectively. The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

Canada’s Earth-observing RADARSAT Constellation Mission (RCM) is intended to serve operational users. The users’ main objectives were to have routinely available high-quality quantitative informati...

The TanDEM-X satellite mission has revolutionized DEM generation from spaceborne synthetic aperture radar. In addition to the primary objective of generating a consistent digital elevation model with global coverage and unprecedented accuracy, the mission has acquired time series of topographic data over several volcanoes, providing an excellent opportunity to test the use of this innovative dataset for volcano monitoring and research. In this article, we review the utilization of the single-pass TanDEM-X data for studying various kinds of volcanic activity, such as basaltic lava flows, the formation and destruction of lava domes and related pyroclastic density currents, and subsurface magma withdrawal and intrusion. We then consider the uses of these data for hazard assessment and forecasting. Our goal is to highlight the importance of timely and repeated topographic information in volcanology, and to suggest the development of future spaceborne bistatic synthetic aperture radar satellite missions, such as ESA's Earth Explorer 10 candidate mission, “Harmony.”