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This is the third of three articles that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of 1 min) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit (LEO) radiometers maintaining a separation of 1 or 2 min can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed. [ABSTRACT FROM AUTHOR]

Radar sounders (RSs) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface (SS) structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have been developed with enhanced capabilities for emulating the RS acquisition process. However, the raw simulated radargrams obtained from e.m. simulators are difficult to interpret and analyze without a focusing operation, which results in an underestimation of the RS detection performance. While frequency methods for range and azimuth compression of real RS data are well-established, their use on simulated data is not addressed in the literature and requires major modifications. This article presents a novel method that implements azimuth compression using unfocused and focused processing on simulated raw data. The proposed method is based on an adaptation of the range-Doppler algorithm to the case of raw data generated by a coherent RS simulator. The method is demonstrated in three case studies to show the similarity between simulated and real data processing: 1) simple geometries; 2) a simulated SHAllow RADar (SHARAD) radargram compared with the real data product; and 3) a real application scenario for supporting the design of a new RS instrument. The results indicate higher fidelity of the focused simulated data with the real data product and the target structure, confirming the usefulness of the proposed approach in obtaining realistic processing of simulated radargrams. [ABSTRACT FROM AUTHOR]

Spaceborne radar sounders are high frequency (HF)/very high frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an array of orbiting sensors in formation flight synthesizing a narrow radar antenna beam. In this article, we assess the capability of distributed radar sounding to discriminate clutter from subsurface returns by exploiting direction of arrival (DOA) estimation techniques. This is achieved by first outlining an approach for designing and evaluating the distributed radar sounder DOA estimation performance as function of the radar system parameters (e.g., intersensor distance) and external noise factors such as ionospheric scintillations. Then, the theory is complemented by radar simulations of several acquisitions over Greenland assuming a variety of subsurface geometries. The simulations confirm that clutter discrimination through DOA estimation is a viable approach to further improve the array capability in disambiguation of subsurface echoes from surface ones. [ABSTRACT FROM AUTHOR]

Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial–temporal sequence prediction tasks and plays a prominent role in severe convection and precipitation nowcasting. However, existing extrapolation methods mainly focus on a defective echo-motion extrapolation paradigm based on finite observational dynamics, neglecting that the actual echo sequence has a more complicated evolution process that contains both nonlinear motions and the lifecycle from initiation to decay, resulting in poor prediction precision and limited application ability. To complement this paradigm, we propose to incorporate a novel long-term evolution regularity memory (LERM) module into the network, which can memorize long-term echo-evolution regularities during training and be recalled for guiding extrapolation. Moreover, to resolve the blurry prediction problem and improve forecast accuracy, we also adopt a coarse–fine hierarchical extrapolation strategy and compositive loss function. We separate the extrapolation task into coarse and fine two levels which can reduce the downsampling loss and retain echo fine details. Except for the average reconstruction loss, we additionally employ adversarial loss and perceptual similarity loss to further improve the visual quality. Experimental results from two real radar echo datasets demonstrate the effectiveness of our methodology and show that it can accurately extrapolate the echo evolution while ensuring the echo details are realistic enough, even for the long term. Our method can further be improved in the future by integrating multimodal radar variables or introducing certain domain prior knowledge of physical mechanisms. It can also be applied to other spatial–temporal sequence prediction tasks, such as the prediction of satellite cloud images and wind field figures. [ABSTRACT FROM AUTHOR]

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this article proposed a homogeneous sample selection method based on a novel concept of generalized spatial spectrum density function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of digital elevation model (DEM) data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy, but the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM), thereby enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than the other contrast methods. [ABSTRACT FROM AUTHOR]

This article presents a new method to retrieve dynamic information from spaceborne Doppler radar observations. The method is based on a complex convolution Doppler resampling (CConDoR) formulation, which links the spaceborne pulse-pair (PP) correlation measurements to high-resolution PP products that are not affected by the spacecraft motion. The CConDoR formalism allows to easily simulate Doppler products via convolution products. The CConDoR representation enables also retrievals by deconvolution. This approach is illustrated using a Wiener deconvolution algorithm. Results are shown for simulations of the radar of the Earth Cloud Aerosol Radiation Explorer (EarthCARE; developed by the European and Japanese space agencies). The proposed corrections improve the accuracy of the measured mean velocities (by correcting for nonuniform beam-filling (NUBF) biases) and spectral widths (by correcting for the spectral broadening), which to the best of the authors’ knowledge is novel. [ABSTRACT FROM AUTHOR]

Lake ice thickness (LIT), a thematic product of lakes as an essential climate variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ , is based on the physical modeling of the conventional radar echoes (also called low-resolution mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow–ice and ice–water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013–2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of a few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions. [ABSTRACT FROM AUTHOR]

A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a nonnegligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal (PES) model to describe the radar echoes for high-speed targets in a space–air-based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity, and acceleration. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intrapulse and interpulse integration. A PES model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the PES model-based Keystone transform (PES-KT) and inner-pulse chirp Fourier transform (CFT). The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space–air-based bistatic radar. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System-Reflectometry (GNSS-R) is a promising field with a diverse set of applications. Polarimetric GNSS-R has been shown to be sensitive to different land parameters, such as freeze/thaw, crop growth, or soil moisture. In this article, the polarimetric scenario of a GNSS reflection is defined, and the theoretical basis and the algorithm to retrieve the Stokes parameters of a GNSS-R signal are proposed. The presented algorithms are validated using data collected by the two linear orthogonal polarization channels of the Soil Moisture Active Passive (SMAP) radar antenna working as GNSS-R, known as SMAP-Reflectometry (SMAP- $R$). The Stokes parameter retrieval is presented for the SMAP- $R$ case, and the receiver phase offset and intensity are calibrated. The same calibration procedure proposed by the CYclone GNSS (CYGNSS) mission is applied to the SMAP- $R$ first Stokes parameter, allowing the retrieval of the normalized bistatic radar cross Section (NBRCS) of such measurement. Finally, the SMAP- $R$ data are compared to the CYGNSS NBRCS over the ocean, showing an unbiased root-mean-square error of 3.3 dB and a small bias. [ABSTRACT FROM AUTHOR]

Polarization information can greatly improve the ability of detection, parameter retrieval, and classification for entomological radar. In recent, the instantaneous fully polarimetric measurement technique is used in entomological radar, so as to relax the restriction of invariant orientation during observation compared to the typical vertical-looking radar with linear-polarized rotation configuration. However, the fully polarimetric measurement is based on a multichannels’ radar system, and the imbalance and crosstalk between channels, namely, polarimetric errors, will severely affect the measurement of the polarimetric scattering matrix (PSM). Thus, polarimetric calibration is essential to obtain the accurate polarization information of targets. In contrast to inefficient and manual calibration methods, a data-driven polarimetric calibration method is proposed based on the assumption of reciprocity and bilateral symmetry of insects in this article. The polarimetric errors are elaborately decomposed into two components, which could be estimated by reciprocity and bilateral symmetry, respectively. In addition, because the proposed method is data-driven, the calibration process could be automatically executed to compensate for the temporal-variant polarimetric errors induced by temperature change and, thus, suitable for the long-term operation of entomological radar. Simulations and experiments are carried out to evaluate the performance of the proposed calibration method. Results show that it could achieve high accuracy when the number of insects used as calibrators is large enough. The proposed method has been applied in Ku-band high-resolution fully polarimetric entomological radars for cross-border migratory insect observation in Yunnan province, China. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage the convolutional recurrent neural network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses, and the results tend to have inaccurate appearances, shapes, and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization and systematically compare four types of generative adversarial networks (GANs), which are the combinations of GAN/Wasserstein GAN (WGAN) and its multiscale version. Extensive experiments on a real-world radar dataset and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority of circumstances. Moreover, we find that the WGAN regularization often delivers better results than the GAN regularization due to its robustness, and the multiscale WGAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at https://github.com/luochuyao/MultiScaleGAN and the test system at http://39.97.217.145:80/. [ABSTRACT FROM AUTHOR]

The total mean square slope (mss) of large-scale (in comparison with the scale of radar wavelengths) ocean waves can describe the roughness of the ocean surface. This important parameter has found many applications and can be determined by the dependence of large-scale mss on azimuthal angles. The Surface Waves Investigation and Monitoring instrument onboard the China France Oceanography Satellite (CFOSAT) can provide a 2-D normalized backscattering radar cross section (NRCS) for 2-D global ocean-surface wave detection. In this article, the 2-D NRCS is used to calculate the total large-scale mss via two methods. The first method is constructed from regression, while the second scheme is derived from a three-solution method. Finally, the total large-scale mss is calculated using these methods, and the map of the distribution of the global total mss is presented for the first time. Our results show that the two sources of total large-scale mss obtained are consistent with each other. CFOSAT can provide a 2-D total large-scale mss map and be used as a new data resource for global applications. [ABSTRACT FROM AUTHOR]

Exploring polarimetric diversity of synthetic aperture radar (SAR) data is directly applicable to conventional monostatic cases. For this, the most used convention is the backscatter alignment. While establishing important advantages for the monostatic case (possibility to have equal values on the cross-polarimetric channels), it has been proven to introduce some difficulties for the bistatic case. This appears in relation to the so-called conjugate similarity operation, when (mathematically) asymmetric scattering matrices occur. In this article, we propose the detailed algorithm which provides a solution to the conjugate similarity operation, in the case of general scattering matrices. The proposed algorithm is based on the real representation (RR) matrix transformation. Furthermore, we investigate the characterization of canonical bistatic scatterers (three elementary targets). Raw bistatic polarimetric signals are obtained using simulations with a computationally electromagnetic (EM) software, capable of complete EM analysis. The eigenvalue classification illustrates the potential of additional information brought using the proposed RR scattering matrix (RRSM). The presence of complex eigenvalues is investigated in relation to the bistatic angle and one nonreciprocity parameter. [ABSTRACT FROM AUTHOR]

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS)-based passive bistatic radar (PBR) has many advantages benefiting from the bistatic configuration and the excellent properties of the GNSS signals. In this article, the possibility of moving target indication (MTI) using a GNSS-based PBR is analyzed and illustrated by experiments. The power budget is first evaluated to investigate the capabilities and limitations of the GNSS-based PBR. Due to the low power density of GNSS signals, long-time integration is required to achieve long-range surveillance. In our previous work, a Radon Fourier transform (RFT)-based long-time integration algorithm has been proposed for GNSS-based PBR. However, the RFT-based methods are computationally inefficient. In this article, a novel MTI algorithm based on high frame rate image sequence (HFRIS) is introduced to the GNSS-based PBR. Benefiting from the avoiding of the iterative multidimensional parameter searching, it has a higher computational efficiency than the RFT-based methods. At the same time, the possibility of multistatic positioning for moving targets using the GNSS-based PBR is analyzed. It is shown that the absolute position of the target in three dimensions could be determined if at least three satellites are utilized to measure the range delay of the target return. To confirm our analysis, experiments are conducted which detects the airplanes using the GPS signals as illuminators of opportunity. The collected data are processed by both the RFT-based method and the HFRIS-based method. Both methods have successfully detected the airplane target at a distance of about 5 km and the HFRIS-based method shows a far better performance in terms of processing efficiency. The multistatic positioning experiment is also conducted and the estimated position of the target is well consistent with the values acquired by the flight record, which shows the potential of the GNSS-based PBR on multistatic operations. [ABSTRACT FROM AUTHOR]

Bistatic scattering from rough surfaces is typically approached through the analysis of the scattered field in the conventional H and V polarization basis, which coincides with the zenith and azimuth unit vectors in a spherical reference frame. This study delves into the impacts of different choices of the transmit and receive linear basis on the performance and design of a synthetic aperture radar (SAR) mission receive-only companion. This article formalizes the rotation of the scattered wave orientation at the antenna axes of the companion with respect to the transmitted one and introduces a novel set of linear polarizations, named principal polarizations, in transmit and receive, deemed more suited to represent the scattering mechanisms of rough surfaces. Such a set is defined by the polarization bases that maximize the radar cross section. It is shown that the theoretical estimates from the proposed geometrical framework provide a good agreement with analytical and numerical simulations, performed considering state-of-the-art numerical solutions. In addition, this article promotes the hypothesis that a bistatic radar configuration, defined through the conventional H and V linear basis, presents a strong similarity, from a target information retrieval standpoint, to a monostatic compact $\varphi $ -pol mode, i.e., with the transmission of a linear polarization rotated by an angle $\varphi $. The rotation $\varphi $ varies over the swath and as a function of satellite separation. For baselines of 250–300 km, such as those envisioned by the European Space Agency (ESA) Harmony Earth Explorer candidate, and for steep incidence angles, an equivalent $\pi /8$ -pol can be achieved for rough surfaces. [ABSTRACT FROM AUTHOR]

This is the first presentation of quasi-synchronous spaceborne synthetic aperture radar (SAR) high-resolution images acquired from C-band Radar Constellation Mission (RCM) and RADARSAT-2 consisting of quad-polarization (HH + HV + VH + VV) wide-swath observations of Hurricane Epsilon. These measurements clearly show that the denoised HV- and VH-polarized normalized radar cross sections (NRCSs) have great consistency. NRCS values at HV- and VH-polarizations are more sensitive to wind speeds and less sensitive to incidence angles or wind directions than those at HH and VV for hurricane force winds. For large incidence angles and high wind speeds, the sensitivity of HH-polarized NRCS to wind speed is higher than that of VV. HH- and VV-polarized NRCSs gradually lose wind direction dependency at high winds. It is notable that the time interval between the two SAR acquisitions is only 3 min. This allows for a direct comparison of HV- and VH-polarized images to investigate the variations in high-resolution backscattering within the hurricane vortex, thereby revealing the most dynamical areas. An asymmetric dynamic is observed around the eye of Hurricane Epsilon, based on positive and negative differences (VH–HV) in the western and eastern parts of the eye. The impacts of rain on quad-polarized NRCS are also examined using collocated rain rates from the Global Precipitation Mission (GPM) and wind speeds from the Soil Moisture Active Passive (SMAP). Significant rain-induced NRCS attenuations are about 1.7 dB for HH and VV, and 2.2 dB for HV and VH, when the rain rate is 20 mm/h. These attenuations are associated with rain-induced turbulence and atmospheric absorption. This work shows that the collocated RCM and RADARSAT-2 hurricane observations provide a unique analysis of synoptic and joint C-band measurements of the ocean surface in quad-polarization; this is noteworthy in view of preparations for the next generation of dual-polarization scatterometer (SCA) onboard second-generation meteorological operational satellite program (MetOp-SG). [ABSTRACT FROM AUTHOR]

A new algorithm is proposed, which estimates two parameters of the particle size distribution (PSD) at each range bin from the global precipitation measurement’s (GPM’s) dual-frequency precipitation radar (DPR) data. The equation that expresses the relationship of the PSD parameters between adjacent range bins is derived. By including the attenuation effect within the bin in the discretized equation, the new algorithm alleviates the double-solution problem when attenuation within the bin is sufficiently large. The stability of the solutions to the equation depends on the value of the mean diameter $D_{m}$ and its gradient with respect to the range in the case of liquid precipitation. There is a critical diameter above which the backward processing of the equation provides a stable or moderately diverging solution, unlike the forward processing that often gives unstable solutions. To provide a set of initial conditions without using the surface reference technique (SRT) in the backward processing, an initialization method using the Hitschfeld–Bordan (HB) attenuation correction method is proposed and tested. The proposed algorithm may provide a tool for investigating the assumptions used in various algorithms. [ABSTRACT FROM AUTHOR]

It is challenging to estimate satellite targets’ absolute attitude and size with limited observational data. This article proposes an innovative way to jointly estimate satellite targets’ absolute attitude and size in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation with only one image. By taking advantage of the rectangular solar panels commonly equipped on satellites, this article extracts solar panel’s principal components, line features, and phase features of single ISAR imagery with principal component analysis (PCA), radon transform (RT), and minimum-entropy (ME)-based autofocus method, respectively. The projection relationship between these features and the absolute attitude and size of the satellite are established separately. Through multi-features fusion, a joint parameter estimation optimization function is established. This optimization is solved iteratively by the quasi-Newton method. The attitude and size parameters can be estimated simultaneously and rapidly, realizing the satellite state estimation under limited observation data. The excellent performance of the proposed algorithm is verified through different experiments. [ABSTRACT FROM AUTHOR]

Accurate and timely estimation of precipitation is critical for issuing hazard warnings (e.g., for flash floods or landslides). Current remotely sensed precipitation products have a few hours of latency, associated with the acquisition and processing of satellite data. By applying a robust nowcasting system to these products, it is (in principle) possible to mitigate this latency and improve their applicability, value, and impact. However, the development of such a system is complicated by the chaotic nature of the atmosphere, lack of sufficient knowledge about the evolution of precipitation systems based on previous observations, and the consequent rapid changes that can occur in the structures of precipitation systems. In this work, we develop two approaches (hereafter referred to as NowCasting-nets) that use recurrent and convolutional deep neural network (DNN) structures to address the challenge of precipitation nowcasting. A total of five models are trained using global precipitation measurement (GPM) Integrated MultisatellitE Retrievals for GPM (IMERG) precipitation data over the Eastern contiguous United States (CONUS) and then tested against independent data for the Eastern and Western CONUS. The models were designed to provide forecasts with a lead time of up to 1.5 h, and by using a feedback loop approach, the ability of the models to extend the forecast time to 4.5 h was also investigated. The performance of the models was compared against the random forest (RF) and linear regression (LR) machine learning (ML) methods, a persistence benchmark (BM) that uses the most recent observation as the forecast, and optical flow (OF). Independent IMERG observations were used as a reference, and experiments were conducted to examine both overall statistics and case studies involving specific precipitation events. Overall, the forecasts provided by the NowCasting-net models are superior, with the convolutional NowCasting-net (CNC) achieving 42%, 24%, 18%, and 16% improvement on the test set mean squared error (MSE) over the BM, LR, RF, and OF models, respectively, for the Eastern CONUS. Results of further testing over the Western CONUS (which was not part of the training data) are encouraging and indicate the ability of the proposed models to learn the dynamics of precipitation systems without having explicit access to motion vectors and other auxiliary features and then to generalize to different hydro-geo-climatic conditions. [ABSTRACT FROM AUTHOR]

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data. [ABSTRACT FROM AUTHOR]

Due to the small size of foreign object debris (FOD) and varied complex weather conditions, the detection of FOD on airport runways is a great challenge. Radar is an important method for detecting FOD targets. However, almost all the existing systems are based on real apertures, which have disadvantages such as low azimuth resolution and susceptibility to rain interference. Here, an innovative FOD detection radar system based on arc-scanning synthetic aperture radar (AS-SAR) technology, the AS-SAR based FOD detection system (AS-FODR), achieves omnidirectional coverage with a very high azimuth resolution and the suppression of flicker clutter, such as rain drops in severe weather. According to the radar imaging simulation of a scene under rainy conditions and the information processing analysis of field experiments, a prototype system was built, and an efficient data process flow was proposed. In short, a one centimeter FOD target was detected on the runway more than 250 m away, proving that the use of AS-FODR is feasible and effective. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (NNs) have proven their efficiency for image processing and are routinely used for image classification. In this article, we use them to convert radar measurements into water distance and ultimately into water levels of inland waterbodies. The measurements used are the successive echoes of the spaceborne radar altimeter signal on a waterbody, the radargram. We show that by using forward modeling with an accurate altimetry simulator, we can generate a sufficient amount of radargrams and train a deep NN accurately enough to obtain water level series from radargrams in a hydrology context. The method is validated at selected waterbodies by comparing these water level time series with in situ measurements on rivers whose width varied between 50 m and 4 km. The correlation of these time series with in situ data was over 0.95 with root mean square error (RMSE) between 26 and 43 cm. The results were also more robust than the Offset Center of Gravity (OCOG)/Ice-1 retracker time series of the same data. The validation shows that this automatic method performs generally as well as a carefully tuned manual method for removing outliers from the ranges provided by the state of the art classical retrackers used by the spatial hydrology community. This new tool is a big step toward a generic, global, and automated method to retrieve inland water levels from altimetry measurements. This goal is especially important in the context of continuously declining number of in situ measurements and of utmost importance for adequate water resources management at the global scale. [ABSTRACT FROM AUTHOR]

Observations and analysis from the first airborne deployment of a G-band atmospheric radar, the Jet Propulsion Laboratory’s vapor in-cloud profiling radar (VIPR), are presented with a focus on ocean surface scatterometry and water vapor retrievals using the differential absorption radar (DAR) technique. We assess transceiver cross-calibration stability between the two DAR frequency channels using a combination of ground-based calibration measurements and airborne cloud observations that are validated by independent sensors. By comparing calibrated, unattenuated ocean surface backscatter measurements with existing models, we demonstrate the need for improvements in treating ocean surface scattering at G-band frequencies for future in-flight radar calibration applications. During science-oriented flights off the coast of California, we demonstrate partial column integrated water vapor (IWV) measurement capabilities with 0.3-mm precision in clear sky by performing a descent maneuver over an operational radiosonde (RS) launching site and comparing with the coincident in situ profiles to find a root mean square error (RMSE) of 0.5 mm. Furthermore, we present water vapor profiling and IWV measurements in the presence of clouds and precipitation when flying through a frontal weather system in the vicinity of Bodega Bay. By comparing with coincident RS profiles, we find an RMSE for in-cloud humidity profile retrievals of 1.1 ${\rm gm}^{-3}$ and precision of 0.6 ${\rm gm}^{-3}$ while the IWV retrievals reveal a bias from differential hydrometeor extinction of 5 mm. We provide a critical assessment of cloud-scattering-induced biases in the water vapor retrievals, as well as discuss a potential method for eliminating systematic error from the DAR measurements. [ABSTRACT FROM AUTHOR]

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site. [ABSTRACT FROM AUTHOR]

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method. [ABSTRACT FROM AUTHOR]

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available at https://github.com/huizhangyang/BSF. [ABSTRACT FROM AUTHOR]

Ionospheric Faraday rotation distorts satellite radar observations of the Earth’s surface. While its impact on radiometric observables is well understood, the errors in repeat-pass interferometric synthetic aperture radar (InSAR) observations and hence in deformation analysis are largely unknown. Because Faraday rotation cannot rigorously be compensated for in nonquad-pol systems, it is imperative to determine the magnitude and nature of the deformation errors. Focusing on distributed targets at L-band, we assess the errors for a range of land covers using airborne observations with simulated Faraday rotation. We find that the deformation error may reach 2 mm in the copol channels over a solar cycle. It can exceed 5 mm for intense solar maxima. The cross-pol channel is more susceptible to severe errors. We identify the leakage of polarimetric phase contributions into the interferometric phase as a dominant error source. The polarimetric scattering characteristics induce a systematic dependence of the Faraday-induced deformation errors on land cover and topography. Also, their temporal characteristics, with pronounced seasonal and quasi-decadal variability, predispose these systematic errors to be misinterpreted as deformation. While the relatively small magnitude of 1–2 mm is of limited concern in many applications, the persistence on semiannual to multiannual time scales compels attention when long-term deformation is to be estimated with millimetric accuracy. Phase errors induced by uncompensated Faraday rotation constitute a nonnegligible source of bias in interferometric deformation measurements. [ABSTRACT FROM AUTHOR]

In this article, the wideband ambiguity function (WBAF) of monostatic radar and that of bistatic radar, both operating with ultrawideband (UWB)-throb signals, are presented and used to calculate Cramér–Rao lower bounds (CRLBs) on the estimation accuracy of time delay and Doppler stretch in the case of monostatic radar and CRLBs on the estimation accuracy of receiver-to-target range and relative-velocity of a moving point target in the case of bistatic radar. Performance analysis is carried out to investigate the effects of the parameters that characterize the UWB-throb signal, i.e., pulse scaling factor, initial pulse repetition frequency (PRF), throb rate, and signal duration, and the parameters defining the bistatic-radar geometry, i.e., receiver-to-target range, baseline distance, and elevation angle, on the estimation accuracy of the monostatic-CRLBs and on that of the bistatic-CRLBs. Computer plots demonstrate that the estimation accuracy of the monostatic-CRLBs and the bistatic-CRLBs is influenced by the aforementioned radar parameters. It is also shown that target relative velocity has no major effects on the estimation performance of monostatic-CRLBs and bistatic-CRLBs. [ABSTRACT FROM AUTHOR]

In this article, an effective clutter suppression algorithm is presented for the space-borne azimuth multiantenna high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the null-space technique. First, the echo of bistatic geosynchronous-low earth orbit (GEO-LEO) azimuth multiantenna HRWS SAR-ground moving target indication (GMTI) system is utilized to deduce the coarse-focused image of moving targets and clutter, where the Chirp Fourier transform (CFT) in azimuth is involved. Then, the matrix form can be utilized to describe the coarse-focused image of the multiantenna SAR system and the corresponding covariance matrix can be estimated. After that, the null-space is constructed using the covariance matrix corresponding to clutter, where at least a redundant channel freedom is required. Since the null-space vector is orthogonal to signal-space vector, it can be used to suppress the clutter. As an equivalent phase is brought by the slant velocity, the moving targets’ echo can be preserved during clutter suppression. Then, the optimization and suboptimization vectors for clutter suppression are introduced. It is worth noting that the proposed clutter suppression algorithm is robust for the antenna mismatch, that is, the antenna mismatch in phase and the corresponding position error. Finally, the theoretical investigations are validated using some simulation experiments, where the experiments for bistatic GEO-LEO and single-platform azimuth multiantenna HRWS SAR-GMTI system are both included. In addition, the real measured single-platform azimuth multiantenna HRWS SAR data experiments are also performed. [ABSTRACT FROM AUTHOR]

This article develops and examines methods for the production of real-world, very high-resolution imagery using a high-frequency drone-borne synthetic aperture radar (SAR) operating at short ranges. The significance of motion errors which lead to space-invariant/variant phase errors is discussed. Subsequently, an imaging algorithm capable of handling these errors is proposed and presented. The validity of the approach is tested through both simulation and experiment. We present novel short-range, fine-resolution imagery (less than 2 cm in cross-range) of an extended target area generated using a low-cost, drone-borne vehicular frequency-modulated continuous-wave (FMCW) radar operating at 77 GHz, without using a dedicated inertial navigation system (INS) or Global Positioning System (GPS). [ABSTRACT FROM AUTHOR]

Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the in situ significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and in situ wind components are included in the input layer of the neural network. [ABSTRACT FROM AUTHOR]

We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales. [ABSTRACT FROM AUTHOR]

Spaceborne multistatic synthetic aperture radar (M-SAR) has extensive applications, including multiangle imaging, digital beamforming (DBF) and cross- and along-track interferometry. However, it is difficult for classical satellite formations to meet the multimission (e.g., cross-track interferometry and along-track interferometry, i.e., XTI and ATI) requirements of M-SAR concurrently. Therefore, superpolyhedron (SP), an innovative satellite formation based on the dual-helix and pendulum formations, is proposed to maximize the coverage ratio of the effective cross- and along-track interferometric baselines at the same time. A method of constructing the SP formation is detailed. The method is a three-step procedure, in which the first two steps aim at obtaining an initial formation via exploiting the geometry of the formation dynamics; the last step solves an optimization problem. Then, the design of a supertetrahedron (ST) formation, a special case of the SP formation, is investigated as a numerical example. The merit of SP formation is represented as the coverage ratios of effective cross- and along-track interferometric baselines. The result is compared with those of the classical satellite formations. It shows that only the coverage ratios of the ST formation are greater than 70% simultaneously. Therefore, the ST formation can realize both effective XTI and ATI. It implies that the SP formation has the potential to be used in future spaceborne M-SAR interferometry. [ABSTRACT FROM AUTHOR]

Ground-based synthetic aperture radar (GBSAR) systems are widely used for monitoring slopes, especially in quarries and open pits. Unfortunately, the movement of the machinery (cranes, trucks, shovels, and excavators) in the radar field of view often prevents to correctly focus large circular sections of the radar images, producing a characteristic artifact, often named “blurring.” Therefore, the aim of this article is to propose both an acquisition modality and an image processing technique able to mitigate the blurring due to moving clutter. The proposed acquisition modality (named “free-running” or “on-the-fly”) allows to filter out the high-frequency clutter, which is often noticeable in GBSAR images, but it is not effective with low-frequency clutter caused by slow movement of machinery like heavy trucks or large cranes. For this reason, the authors propose even a suitable processing method that can be used in combination with high-frequency sampling. The application of these two methods has been proved to be very effective in realistic quarry scenario. [ABSTRACT FROM AUTHOR]

Accurate measurement and monitoring of precipitation is crucial for many applications, such as flood and drought risk assessment and management. Conventional meteorological devices for estimating precipitation (i.e., rain gauges, disdrometers, active and passive remote sensors, be they ground-based, spaceborne, or airborne) have their own strengths and weaknesses. The latter are often related to time and space resolution, coverage, and cost. In the last two decades, several studies have been carried out to exploit opportunistic signals of terrestrial microwave communication links to improve precipitation estimation capability. This study describes and evaluates a method to retrieve rainfall rate from the signal-to-noise ratio obtained from commercial interactive digital video broadcasting (DVB) receivers [referred as to smart low-noise blocks (smartLNBs)]. During a 1-year measurement campaign carried out in Tuscany (Italy) with purposely deployed instruments, the precipitation values estimated from a set of SmartLNBs were compared with measurements from co-located rain gauges and disdrometer. The normalized mean absolute error (NMAE) and root-mean-square error (RMSE) obtained comparing the total cumulative precipitation from a SmartLNB and a disdrometer are 48.8% and 7.46 mm, respectively. Encouraging results also come from comparing the total precipitation amounts as measured by the SmartLNBs and by the rain gauges, with values of NMAE (respectively, RMSE) ranging between 44% and 82% (respectively, 5.2 and 11.5 mm). [ABSTRACT FROM AUTHOR]

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses. [ABSTRACT FROM AUTHOR]

The ground-based radar quantitative precipitation estimation (QPE) faces various challenges including the overestimation caused by the bright band (BB) in the stratiform region and the underestimation in mountainous areas when the terrain-enhanced precipitation occurs at the levels below ground-based radar measurements. The vertical precipitation structure provided by spaceborne radars, i.e., the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement mission (GPM) Dual-frequency PR (DPR), is valuable for mitigating the above problems. Since the spaceborne radars and ground-based radars usually operate in different frequencies, e.g., the TRMM PR and the KuPR of GPM DPR work in Ku-band (13.8 and 13.6 GHz, respectively) and the ground-based radars in western China work in C-band (5.4 GHz), the reflectivity conversion from Ku-band to C-band is necessary before the vertical profile of reflectivity (VPR) measured by spaceborne radars can be utilized to improve the ground-based radar QPE in western China. This study presents a conversion method using GPM DPR measurements, i.e., the drop size distribution (DSD) for different precipitation types (the stratiform with/without BB and the convective cases) and particle phases (the solid, melting, and liquid). Using the T-matrix method, the reflectivity difference between Ku-band and C-band is found and the Ku-band to C-band conversion relations are derived with the linear regression. These conversion relations have been validated by matching and comparing the converted C-band reflectivity with the C-band ground-based radar measurements. The results demonstrate the effectiveness and reliability of the conversion. This method can be extended for the reflectivity conversion in other frequencies and can facilitate the incorporation of reflectivity measurements from various instruments. [ABSTRACT FROM AUTHOR]

In the context of spaceborne synthetic aperture radar (SAR) imaging, high resolution and wide swath are inherently conflicting requirements. These may, however, be simultaneously satisfied by advanced imaging modes with multichannel architectures in elevation and/or azimuth. This article elaborates on a new mode based on multiple elevation beams and a simple pulse repetition interval (PRI) variation scheme which allows high-resolution wide-swath imaging. It is shown to use the illumination time more efficiently than ScanSAR and yet to be simpler than staggered SAR. Good SAR imaging performance is achieved with a rather compact antenna design. The proposed imaging mode is suitable for spaceborne SAR systems with planar and reflector antennas. In order to improve the imaging performance, a reflector antenna architecture with a multichannel feed in both elevation and azimuth is considered. [ABSTRACT FROM AUTHOR]

Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range migration must be considered during the design and operation of the radar. Pulse compression for spaceborne weather applications introduces additional interdependence between the radar system and the operations when compared with traditional pulsed radar systems, primarily as a result of the large platform velocities. Pulse compression signals for weather radar can be simulated with high fidelity to predict and optimize the radar’s performance. In this article, we evaluate the pulse compression performance of RainCube, a Ka-band precipitation radar in a CubeSat, through analysis and comparison of observations and radar simulations. Through these comparisons, design and operational considerations for pulse compression weather radar are discussed. This work shows that the optimal pointing angle for RainCube to achieve the finest vertical resolution is not at nadir, but when pointing forward approximately 2.25°, in the direction of the spacecraft’s orbit. [ABSTRACT FROM AUTHOR]

This article addresses the problem of radar pulse compression. First, a matched filter at the receiver is assumed and a method to design a transmit waveform characterized by an autocorrelation function with a narrow mainlobe and low sidelobes is proposed. After that, a mismatched filter is considered and a method to design the optimum linear filter receiver is proposed. In both cases, the pursued objectives are minimum sidelobe energy and peak level through a strategy based on quadratic and nonlinear optimization. In this article, we focus particularly on the case of ground weather radars, where the problem of pulse compression is particularly challenging. [ABSTRACT FROM AUTHOR]

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands. [ABSTRACT FROM AUTHOR]

We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds. [ABSTRACT FROM AUTHOR]

This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70–500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented. [ABSTRACT FROM AUTHOR]

Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking. [ABSTRACT FROM AUTHOR]

Modeling L-band backscatter from a corn canopy continues to be a challenge due to the complex dynamics in both plant phenology and the underlying soil. An experiment has been conducted to better understand the relationship between L-band backscatter and canopy parameters such as soil moisture, vegetation water content, dew, and periodic rows. The experiment consists of field measurements that take into account plant phenology and are concurrent with L-band backscatter returns from a corn canopy over a full growing season. The field measurements of the corn plants’ constituents highlight modeling complexities, such as an inhomogeneity in the dielectric constant of the stalk and cobs. A simple method to replace the stalk and cob with a homogeneous dielectric constant is validated. Using the field measurements in a scattering model developed at George Washington University (GW), both coherent and incoherent backscatter are computed. The results show coherent effects contributing to enhanced backscatter by up to 2.7 dB for both HH-pol and VV-pol. The coherent model and the detailed measurements, especially, the dielectric constant of the stalks, resulted in good agreement with the measurements. These measurements have an average root mean square difference (RMSD) with the results from the coherent model of around 1 dB for both HH-pol and VV-pol over the entire growing season. The incoherent mode does not perform as well. [ABSTRACT FROM AUTHOR]

During the descent of the Tropical Rainfall Measuring Mission satellite at the end of its mission, three types of special experiments were conducted; wide swath, 90° yaw (90Y: along-track scanning), and dense sampling. In this article, the results from the analysis of 90° yaw experimental data are shown in terms of a normalized radar cross section (NRCS) of the surface. The advantage of the 90Y experiment is that obtaining the incident angle dependency of the NRCS enables deduction of the conditions at the surface, especially over the ocean. The slope and intercept are calculated from the NRCS–incident angle plot except for angles near nadir. The slope correlates well with the sea-surface wind speed and therefore the rain attenuation can be estimated from the intercept and compared with the attenuation estimated from the radar reflectivity factor of the rain profile. The effect of the rain drops striking the sea surface is also examined by comparing the wind speed–slope relationship. [ABSTRACT FROM AUTHOR]

A new method for separating convective, intermediate, and stratiform rainfall based on the wavelet scale analysis is presented. A robust classification has resulted from the two choices: 1) the transformation of radar reflectivities to rain rates and 2) the inclusion of the intermediate class. The method uses á trous wavelet transform (WT) to separate heterogeneous convective features from the relatively smooth stratiform field. The heterogeneous region is further split into a convective class for the large wavelet coefficients and an intermediate class for the small wavelet coefficients. There is an improvement in the estimation of convective-stratiform fraction and frequency distribution of rain rates in comparison to the well-established Steiner method. The new method correctly reclassified a significant fraction of Steiner’s convective regions, which had low mean rain rates of 5.5 $\mathrm {mm}\cdot \mathrm {h}^{-1}$ , as stratiform regions. Similarly, Steiner’s stratiform regions, which had high mean rain rates of $15\,\,\mathrm {mm}\cdot \mathrm {h}^{-1}$ , are classified as convective by the wavelet method. The method is tested and evaluated over the Indian region during monsoon, and it is found that the three wavelet classes have distinct raindrop-size distributions. [ABSTRACT FROM AUTHOR]

Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas–Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms. [ABSTRACT FROM AUTHOR]

The recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model. [ABSTRACT FROM AUTHOR]