Radar Error Research Articles

Automated convective and stratiform precipitation estimation in a small mountainous catchment using X-band radar data in Central Spain

For the purposes of weather nowcasting, flood risk monitoring and water resources assessment, it is often difficult to achieve a reliable spatio-temporal representation of rainfall due to a low rain gauge network density. However, quantitative precipitation estimation (QPE) has acquired new prospects with the introduction of weather radars, thanks to their higher spatio-temporal resolution. Although a wide number of QPE algorithms are available for using C-band radar data, only a few studies have employed X-band radar. In this study the microscale rainfall variability in a small catchment is automatically measured using short-range X-band radar variograms and classifying precipitation into convective and stratiform types with a recently published index. The aim is to apply a straightforward geostatistical algorithm, named ordinary kriging of radar errors (OKRE), to integrate X-band radar and rain gauge measurements in a mountainous catchment (15 km2) in central Spain. As expected, convective events presented higher estimation errors due to their complex spatial and temporal variability. Despite this fact, errors are sufficiently small and results are reliable rainfall estimations. The two main contributions of this work are the adaptation of the OKRE method to small spatial scales and its application automatically differentiating between convective and stratiform events.

A residual range cell migration correction algorithm for bistatic forward-looking SAR

For bistatic forward-looking synthetic aperture radar (BFSAR), images are often blurred by uncompensated radar motion errors. To get refocused images, autofocus is a useful postprocessing technique. However, a severe drawback of the autofocus algorithms is that they are only capable of removing one-dimensional azimuth phase errors. In BFSAR, motion errors and approximations of imaging algorithms introduce residual range cell migration (RCM) on BFSAR data as well. When residual RCM is within a range resolution cell, it can be neglected. However, the residual migration, which exceeds a range cell, is increasingly encountered as resolution becomes finer and finer. A novel residual RCM correction method is proposed in this paper. By fitting the low-frequency phase difference of adjacent azimuth cells, residual RCM of each azimuth cell can be corrected precisely and effectively. Simulations and real data experiments are carried out to validate the effectiveness of the proposed method.

Assessing spatially dependent errors in radar rainfall estimates for rainfall-runoff simulation

Weather radar been widely employed to measure precipitation and to predict flood risks. However, it is still not considered accurate enough because of radar errors. Most previous studies have focused primarily on removing errors from the radar data. Therefore, in the current study, we examined the effects of radar rainfall errors on rainfall-runoff simulation using the spatial error model (SEM). SEM was used to synthetically generate random or cross-correlated errors. A number of events were generated to investigate the effect of spatially dependent errors in radar rainfall estimates on runoff simulation. For runoff simulation, the Nam River basin in South Korea was used with the distributed rainfall-runoff model, Vflo™. The results indicated that spatially dependent errors caused much higher variations in peak discharge than independent random errors. To further investigate the effect of the magnitude of cross-correlation among radar errors, different magnitudes of spatial cross-correlations were employed during the rainfall-runoff simulation. The results demonstrated that a stronger correlation led to a higher variation in peak discharge up to the observed correlation structure while a correlation stronger than the observed case resulted in lower variability in peak discharge. We concluded that the error structure in radar rainfall estimates significantly affects predictions of the runoff peak. Therefore, efforts to not only remove the radar rainfall errors, but to also weaken the cross-correlation structure of the errors need to be taken to forecast flood events accurately.

FIR‐filter‐based method for the calibration of model errors in wideband digital array radar

Wideband digital array radar (WDAR) has the potential of achieving high resolution and providing multi-function simultaneously, but it is limited in practical use due to the effects of frequency-dependent array model errors. A method to jointly calibrate gain-phase perturbations and mutual coupling effects in WDRA is presented. First, a sparse-based method is deployed to calculate the narrowband calibration matrices at a set of discrete frequencies. Moreover, using a group of finite-impulse response filters, a wideband calibration matrix is constructed, which is capable of compensating the imperfections of array at any frequency in the band. The proposed method is simple and feasible for actual applications, which can be applied to arbitrary array geometries. Simulations and experimental results with measured data demonstrate the validity and effectiveness of the proposed method.

Cross-eye gain in multiloop retrodirective cross-eye jamming

The simultaneous use of multiple retrodirective cross-eye jammers is analyzed for both the case where the jammer loops point in different directions and when they point in the same direction. In both cases, the use of multiple cross-eye jammer loops is shown to lead to significantly increased angular errors in the threat radar under certain conditions. Alternatively, the sum-channel return can be increased to reduce the jammer-to-signal ratio requirements for each jammer loop.

Research on Effect of Radar Fixing Error in Navigation on Delay of Plane Flow

Fixing error of radar introduces some deviation into measuring value from the true. Regular interval between consecutive planes must be extended to accommodate air traffic control regulation. Mathematical relationship between fixing error and buffer time was deduced. Then, the variation of plane flow delay was researched in different condition of fixing error by means of simulation and practical calculation. It is shown that the effect of fixing error on plane flow delay is apparent compared with the determined situation. Also, the amount of delay is not only related to fixing error but also to average arrival rate. So, the fixing error factor should be considered when scheduling and delay analyzing is implemented. At the same time, it is indicated that decreasing the radar detecting error is necessary to reduce the plane flow delay.

Assessing SARAL/AltiKa Data in the Coastal Zone: Comparisons with HF Radar Observations

We present an initial assessment of SARAL/AltiKa data in the coastal band. The study focuses on the Ibiza Channel where the north-south water exchanges play a key role in controlling the circulation variability in the western Mediterranean. In this area, the track 16 of SARAL/AltiKa intercepts the domain covered by a coastal high-frequency (HF) radar system, which provides surface currents with a range up to 60 km. We evaluate the performance of the SARAL/AltiKa Ssalto/Duacs delayed-time along-track products compared to the HF radar surface velocity fields. SARAL/AltiKa data are retrieved at a distance of only 7 km from the coast, putting in evidence the emerging capabilities of the new altimeter. The derived velocities resolved the general features of the seasonal mesoscale variability with reasonable agreement with HF radar fields (significant correlations of 0.54). However, some discrepancies appear, which might be caused by instrumental hardware radar errors, ageostrophic velocities as well as inaccurate corrections and editing in the altimeter data. Root mean square (rms) differences between the estimated SARAL/AltiKa and the HF radar velocities are about 13 cm/s. These results are consistent with recent studies in other parts of the ocean applying similar approaches to Topex/Poseidon and Jason-1 missions and using coastal altimeter corrections.

The Impacts of Representing the Correlation of Errors in Radar Data Assimilation. Part II: Model Output as Background Estimates

Abstract In data assimilation, analyses are generally obtained by combining a “background,” taken from a previously initiated model forecast, with observations from different instruments. For optimal analyses, the error covariance of all information sources must be properly represented. In the case of radar data assimilation, such representation is of particular importance since measurements are often available at spatial resolutions comparable to that of the model grid. Unfortunately, misrepresenting the covariance of radar errors is unavoidable as their true structure is unknown. This two-part study investigates the impacts of misrepresenting the covariance of errors when dense observations, such as radar data, are available. Experiments are performed in an idealized context. In Part I, analyses were obtained by using artificially simulated background and observation estimates. For the second part presented here, background estimates from a convection-resolving model were used. As before, analyses were generated with the same input data but with different misrepresentation of errors. The impacts of these misrepresentations can be quantified by comparing the two sets of analyses. It was found that the correlation of both the background and observation errors had to be represented to improve the quality of analyses. Of course, the concept of “errors” depends on how the “truth” is considered. When the truth was considered as an unknown constant, as opposed to an unknown random variable, background errors were found to be biased. Correcting these biases was found to significantly improve the quality of analyses.

Error influence of radar rainfall estimate on rainfall-runoff simulation

Radar systems have been widely employed to measure precipitation and predict flood risks. However, radar as a rainfall measuring device and the produced rainfall estimate contain uncertainties and errors resulting from sources such as mis-calibration, beam blockage, anomalous propagation, and ground clutter. Previously, these radar errors have been individually studied. However, in practical applications, separating and estimating these errors are not possible. In the current study, to analyze the effects of radar rainfall errors, especially for their effect on the peak discharge, through a synthetic runoff simulation, a spatial error model based on univariate Gaussian random numbers was employed. Furthermore, a Monte Carlo simulation, one of the most widely used techniques for intensive simulation toward obtaining practical results, was performed. The results indicated that the variability of the peak discharge increases as the assumed true rainfall increases. In addition, the higher standard deviation of the tested radar rainfall error leads to a higher peak discharge bias. To investigate the cause of this bias, an additional simulation was performed. This simulation revealed that the regression line for the peak discharge corresponding to rainfall amount increases quadratically. The results show that the higher bias is a result of the higher deviation of peak discharges in the cells, with a greater than mean rainfall, even with the same number of cells for lower and higher rainfall amounts.

국지 우량계 보정 방법의 개선에 관한 연구

Spatial distribution of precipitation has been estimated based on the local gauge correction (LGC) with a fixed inverse distance weighting (IDW), which is not optimized in taking effective radius into account depending on the radar error. We developed an algorithm, improved local gauge correction (ILGC) which eliminates outlier in radar rainrate errors and optimize distance power for IDW. ILGC was statistically examined the hourly cumulated precipitation from weather for the heavy rain events. Adjusted radar rainfall from ILGC is improved to 50% compared with unadjusted radar rainfall. The accuracy of ILGC is higher to 7% than that of LGC, which resulted from a positive effect of the optimal algorithm on the adjustment of quantitative precipitation estimation from weather radar.

Impact of radar systematic error on the orthogonal frequency division multiplexing chirp waveform orthogonality

Orthogonal frequency division multiplexing (OFDM) chirp waveform, which is composed of two successive identical linear frequency modulated subpulses, is a newly proposed orthogonal waveform scheme for multiinput multioutput synthetic aperture radar (SAR) systems. However, according to the waveform model, radar systematic error, which introduces phase or amplitude difference between the subpulses of the OFDM waveform, significantly degrades the orthogonality. The impact of radar systematic error on the waveform orthogonality is mainly caused by the systematic nonlinearity rather than the thermal noise or the frequency-dependent systematic error. Due to the influence of the causal filters, the first subpulse leaks into the second one. The leaked signal interacts with the second subpulse in the nonlinear components of the transmitter. This interaction renders a dramatic phase distortion in the beginning of the second subpulse. The resultant distortion, which leads to a phase difference between the subpulses, seriously damages the waveform’s orthogonality. The impact of radar systematic error on the waveform orthogonality is addressed. Moreover, the impact of the systematic nonlinearity on the waveform is avoided by adding a standby between the subpulses. Theoretical analysis is validated by practical experiments based on a C-band SAR system.

Improving Precision Based on Radar Rectification Principle of Neural Network

This dissertation introduces the radar rectification principle of Neural network, and derivation of the radar errors registration algorithm based on regression neural network algorithm , the result of simulation shows that the algorithm can obtain higher effect by eliminating a variety of errors and improving precision of target.

Offset Error Research of Self-Triggering Pulsed Mini Laser Radar System

The paper researches on the offset error of self-triggering pulsed mini laser radar. It theoretically analyzes the variation of space charge region of PN junction in receiving photodiode. The variation value is affected by the different incident light power. One of conclusion is the laser pulse time-of-flight affected by the variation of incident light power. Another conclusion is the laser pulse width affected by the variation of incident light power as well. The paper proposes the basic equations to describe this phenomenon. In addition, the new designed experimental devices are used to prove that the theoretical analysis is true. This new system can effectively solve the offset error problems, and the negatively impact of offset error will be eliminated on the receiver of self-triggering pulsed mini laser radar system.

Uncertainty Analysis of Soil Moisture and Vegetation Indices Using Aquarius Scatterometer Observations

Simple functions of radar backscatter coefficients have been proposed as indices of soil moisture and vegetation, such as the radar vegetation index, i.e., RVI, and the soil saturation index, i.e., ms. These indices are ratios of noisy and potentially miscalibrated radar measurements and are therefore particularly susceptible to estimation errors. In this study, we consider uncertainty in satellite estimates of RVI and ms arising from two radar error sources: noise and miscalibration. We derive expressions for the variance and bias in estimates of RVI and ms due to noise. We also derive expressions for the sensitivity of RVI and ms to calibration errors. We use one year (September 1, 2011 to August 31, 2012) of Aquarius scatterometer observations at three polarizations ( σHH, σVV, and σHV) to map predicted error estimates globally, using parameters relevant to the National Aeronautics and Space Administration Soil Moisture Active and Passive satellite mission. We find that RVI is particularly vulnerable to errors in the calibration offset term over lightly vegetated regions, resulting in overestimates of RVI in some arid regions. ms is most sensitive to calibration errors over regions where the dynamic range of the backscatter coefficient is small, including deserts and forests. Noise induces biases in both indices, but they are negligible in both cases; however, it also induces variance, which is large for highly vegetated regions (for RVI) and areas with low dynamic range in backscatter values (for ms). We find that, with appropriate temporal and spatial averaging, noise errors in both indices can be reduced to acceptable levels. Areas sensitive to calibration errors will require masking.

Geostatistical radar–raingauge merging: A novel method for the quantification of rain estimation accuracy

Compared to other estimation techniques, one advantage of geostatistical techniques is that they provide an index of the estimation accuracy of the variable of interest with the kriging estimation standard deviation (ESD). In the context of radar–raingauge quantitative precipitation estimation (QPE), we address in this article the question of how the kriging ESD can be transformed into a local spread of error by using the dependency of radar errors to the rain amount analyzed in previous work. The proposed approach is implemented for the most significant rain events observed in 2008 in the Cévennes-Vivarais region, France, by considering both the kriging with external drift (KED) and the ordinary kriging (OK) methods. A two-step procedure is implemented for estimating the rain estimation accuracy: (i) first kriging normalized ESDs are computed by using normalized variograms (sill equal to 1) to account for the observation system configuration and the spatial structure of the variable of interest (rainfall amount, residuals to the drift); (ii) based on the assumption of a linear relationship between the standard deviation and the mean of the variable of interest, a denormalization of the kriging ESDs is performed globally for a given rain event by using a cross-validation procedure. Despite the fact that the KED normalized ESDs are usually greater than the OK ones (due to an additional constraint in the kriging system and a weaker spatial structure of the residuals to the drift), the KED denormalized ESDs are generally smaller the OK ones, a result consistent with the better performance observed for the KED technique. The evolution of the mean and the standard deviation of the rainfall-scaled ESDs over a range of spatial (5–300km2) and temporal (1–6h) scales demonstrates that there is clear added value of the radar with respect to the raingauge network for the shortest scales, which are those of interest for flash-flood prediction in the considered region.

Impact of Carrier Movement on Landing Guidance Radar Precision

In order to study the affect of carrier landing guide radar caused by movement of the aircraft carrier, established a kind of wave model according to p-m spectral, and established a kind of attitude model for aircraft carrier according to the Connolly linear theory. Select the appropriate target for computer simulation. Drawn: the azimuth error of radar has linear relationship with yawing of aircraft carrier, has nothing relationship with pitching angle of aircraft carrier, the pitch error has relationship with roll angle and pitch angle of aircraft carrier, distance error has a little relationship with the translational motion along the axis of aircraft carrier.

Analyzing the Uncertainty of Biomass Estimates From L-Band Radar Backscatter Over the Harvard and Howland Forests

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for the estimation of forest parameters from radar instruments, in particular, use backscatter intensity, interferometry, and polarimetric interferometry. This paper analyzes the uncertainty in biomass estimates derived from single-season L-band cross-polarized (HV) radar backscatter over temperate forests of the Northeastern United States. An empirical approach is adopted, relying on ground-truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry, is characterized for the field sites. A single-season radar data set from the National Aeronautics and Space Administration Jet Propulsion Laboratory's L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model.

Modeling and Sensing the Vertical Structure of the Atmospheric Path Delay by Microwave Radiometry to Correct SAR Interferograms

The vertical structure of the atmospheric water vapor induces phase errors in interferometric synthetic aperture radar (SAR) data. This paper presents a simulation study to investigate whether spaceborne submillimeter radiometric observations, which can be realized with fairly high spatial resolution, are able to derive the vertical structure of the atmospheric wet delay. The accuracy of the retrieved zenith wet delay (ZWD) trend as a function of surface height is assessed in order to correct the associated height dependence of the interferometric phase error in a SAR interferogram. Using a simulated benchmark, we evaluate the errors associated with the use of both a linear and an exponential model of the behavior of ZWD as a function of the surface height. This paper shows a fairly accurate reconstruction of the trend parameters estimated from radiometer brightness temperature images, with respect to realistic atmospheric profiles provided by radiosounding observations (RAOBs). The trend parameters that we consider in this paper are the slope K for the linear model and scale height H for the exponential one. An overall better accuracy is found for the exponential model, which is more representative of the actual behavior of ZWD with height, resulting in a residual uncertainty in the path delay due to the atmospheric stratification of approximately 0.2-0.3 cm and nearly zero bias, as compared to RAOBs.

Motion Compensation of Airborne Synthetic Aperture Radar

This paper addresses the problem of the compensation of the motion errors in an airborne synthetic aperture radar (SAR). The motion errors are caused by the sensor's unintended translational and rotational motion. The motion errors cause amplitude and phase modulation of the echo, thus making the image defocused and geometrically distorted. A method of estimating the motion error based on the movement of the line-of-sight unit vector is proposed. The range-Doppler algorithm is used for processing the SAR raw data. A motion compensation algorithm is also proposed and is applied after the range compression to compensate for the effective range error in single-step. Effectiveness of the proposed algorithm is evaluated using the experimental data acquired by Disaster Management SAR system developed in Space Applications Centre, ISRO, Ahmedabad, India.

Unified Registration Model for Both Stationary and Mobile 3D Radar Alignment

For mobile radar, offset biases and attitude biases influence radar measurements simultaneously. Attitude biases generated from the errors of the inertial navigation system (INS) of the platform can be converted into equivalent radar measurement errors by three analytical expressions (range, azimuth, and elevation, resp.). These expressions are unique and embody the dependences between the offset and attitude biases. The dependences indicate that all the attitude biases can be viewed as and merged into some kind of offset biases. Based on this, a unified registration model (URM) is proposed which only contains radar “offset biases” in the form of system variables in the registration equations, where, in fact, the “offset biases” contain the influences of the attitude biases. URM has the same form as the registration model of stationary radar network where no attitude biases exist. URM can compensate radar offset and attitude biases simultaneously and has minor computation burden compared with other registration models for mobile radar network.