Adaptive digital beamforming (DBF) techniques offer a promising solution for mitigating signals originating from antenna sidelobes, such as clutter echoes from the ground, in phased array weather radars (PAWRs). However, adaptive DBF is sensitive to amplitude and phase errors of reception signals from each antenna element, and its performance can be degraded if antenna elements output large errors. In the present study, we demonstrate a calibration method of amplitude and phase errors by detecting aircraft as a hard target. Obtained calibration parameters are applied to fair weather and precipitation observation data acquired with an X-band single-polarized PAWR at Osaka University, Japan. Compared to the conventional Fourier (nonadaptive) method, the sidelobe level is suppressed by more than 40 dB, even suppressed by more than 10 dB compared to the adaptive DBF without the amplitude and phase correction. Moreover, this method is useful to detect malfunctioning antenna elements with large phase errors which are difficult to detect during normal operation. This study demonstrates that the amplitude and phase corrections are necessary to maintain the best performance of adaptive DBF, and the present method facilitates a periodical calibration.

Mitigating ground-clutter signals is one of the major topics for the development of phased array weather radars (PAWRs). Adaptive digital beamforming (DBF) methods have been proposed to reduce ground-clutter effects by making null beam patterns toward the ground. On the other hand, adaptive DBF methods require much higher computational costs than the non-adaptive Fourier DBF method. In the present study, we propose the “precomputed method” for weather radar applications. In this method, a weighting matrix of DBF is once calculated by an adaptive DBF method with observation data recorded in fair weather, and is applied to real-time processing. Since the weighting matrix is not updated in real-time, the computational cost becomes 30 times less than the adaptive DBF, the same level as the Fourier method. At azimuth angles where ground clutter is modest, the results of the Fourier method may be better than those of the proposed method. In those cases, the Fourier method can be used selectively for less ground-clutter regions. This study demonstrates a practical method to reduce the ground-clutter effect of PAWRs by DBF.

In this paper, we propose a sparsity and smoothness regularized model for the estimation of nonnegative power spectral densities (PSDs) of complex-valued random processes from mixtures of realizations. The proposed model is designed to jointly estimate frequency components of the realizations and the PSDs. The PSDs are estimated by the nonnegative variable in the proposed model, which enables that the sparsity and the smoothness can be exploited via convex optimization. Numerical experiments on the phased array weather radar, which is an advanced weather radar system, show that the proposed approach achieves superior performance to the existing sparse estimation models combined with post-smoothing.

For weather radar, although a phased array system is useful for rapid scanning, its development cost is several times higher than that of radar with a dish-type antenna, and it requires relatively more antenna elements, analog-to-digital converters, and phase shifters. To reduce antenna elements with the devices of phased array systems, array geometry in a random or triangular pattern is often used. However, relatively fewer antenna elements result in observation accuracy degradation due to the increased sidelobe level or the wider beamwidth. We employ compressive sensing (CS) processing [i.e., L1 minimization (L1) and basis pursuit denoising (BPDN)] to reduce the number of antenna elements. We conducted numerical simulations for point and distributed like targets and discussed the observation accuracy using L1 and BPDN. Compared to the estimations made using a full array antenna, where BPDN can be estimated with extremely high accuracy given a 25% reduction in antenna elements, we also applied CS to real measurement data obtained using the PAWR. The BPDN was also very effective for measurement data. The novelty of the study is highlighted in its discussion of the feasibility of applying CS in observation data with the PAWR. The study findings provide a reference for development cost reduction and the mass production of PAWR.

We proposenonlinearbeamforming for phased array weather radars (PAWRs). Conventional beamforming islinearin the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. Fordistributed targetssuch as raindrops, however, the number of scatterers is significantly large, differently from the case ofpoint targetsthat are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of aperiodogramof each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of thepower spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, 1) major components of the periodogram are concentrated in the vicinity of themean Doppler frequencyand 2) frequency indices of the major components are similar between adjacent elevations. These are expressed asgroup-sparsitiesof the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately but requires more time to solve. Both problems are solved with thealternating direction method of multipliers(ADMM) includingnonlinearmappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.

This article proposes a method for estimating the Doppler power spectrum (DPS) of a weather radar via minimum mean square error (MMSE). In order to detect severe weather phenomena that mostly occur within the lowest few kilometers of the atmosphere, weather radars have to direct their beams at low elevation angles, and the received signals from such observations usually contain reflections from the ground and buildings, so-called ground clutter. The MMSE estimator, which is an adaptive spectral estimator, allows weather radar DPSs to be obtained with excellently reduced ground clutter contaminations. The MMSE estimator was examined by numerical simulations, which supposed various precipitation and ground clutter scenarios and DPS estimation parameter values. The MMSE estimator provided DPSs almost as accurate as those from the traditional Fourier and windowed Fourier estimators in simulations with no ground clutter and much better DPSs than those estimators in the presence of ground clutter. Furthermore, the MMSE estimator gave better suppression of ground clutter contamination than the Capon estimator, which is another adaptive spectral estimator. As a result of statistical evaluations, ground clutter signals with a strong clutter-to-noise ratio of 70 dB appeared only in a narrow velocity range of the MMSE DPS, from −2 to 2 m/s, and caused degradation of the mean and standard errors outside this velocity range by just 1 dB. The MMSE estimator was also applied to signals received by actual weather radar, and DPS estimation of precipitation signals with similarly low ground clutter contamination was demonstrated.

The comb beam transmission (TX) approach, which forms a power antenna radiation pattern with multiple mainlobes, was studied for application to phased array weather radars. Combining the use of comb TX and a digital beamforming receiver enables a weather radar to observe multiple directions simultaneously. Numerical simulations show that the two-way antenna radiation patterns formed by comb TXs have properties comparable to conventional weather radar observation: almost the same mainlobe widths are achieved, and with reference to the mainlobe peak, the maximum sidelobe level is less than −37 dB, and the sidelobe level reaches −60 dB at an angle of 13.77°. These properties are superior to the wide TXs that are normally utilized by phased array weather radars. A simulation that applied the comb TX approach to a current C-band weather radar showed that the volume scan time can be reduced from almost 500 s to 1 min.

The X-band phased-array weather radar (PAWR) operated by the Osaka University, performs a full-volume scan every 30 s within a 60-km range. For the waves received by the array antenna of the PAWR, digital beamforming is used only in the elevation angles. The sidelobes of the beam pattern cause errors in spectral moments in the higher elevation angles because of ground clutter. For clutter reduction, a Capon beamformer techniques with diagonal loading (CPDL) with a neural network (NN) is applied to the PAWR. In comparison with the Fourier transform beamforming method, the effectiveness of the CPDL with NN method for ground clutter reduction is discussed, using numerical simulation, and actual PAWR measurement data. Based on the simulation results and measured data, we established that the CPDL with NN accurately estimates point and distributed scatterers, which simulate ground clutter and precipitation, respectively.

The first X-band dual polarized phased array weather radar (DP-PAWR), which simultaneously transmits pulses of horizontal and vertical polarized radiation, was developed and installed at Saitama University, Japan, in December 2017. The DP-PAWR uses mechanical and electronic scanning at azimuth and elevation angles, respectively. It provides polarimetric precipitation measurements via 3-D volume scanning with an update rate between 10 and 60 s, for a range of up to 80 km. Here, we describe the initial DP-PAWR observation results. To evaluate the DP-PAWR observation accuracy, we compared the observational data with radar variables derived from Parsivel disdrometer data. In comparison with the disdrometer, the relative observation accuracy for the DP-PAWR radar reflectively factor had a standard deviation of 1.1 dB and mean value of 0.4 dB. We also conducted detailed observations of a developing thunderstorm using a specific differential phase (Kdp) column, focusing on the Kdpcore during the storm. The Kdp core movements provided useful information about the convection flow during the storm.

To improve rain rate estimation accuracy of the networked phased-array weather radars (PAWRs) installed in Osaka and Kobe, Japan, we employ a rain rate estimation method using a sequentially varying radar reflectivity and rain rate (Z -R) relationship. To calculate the time variation in the (Z -R) relationship, the rain rate of the existing X-band radar network comprising four dual-polarization radars and the radar reflectivity factor of the PAWRs are used. The proposed method is compared with estimation methods using the Marshall-Palmer relationship and the attenuation coefficient (k). From the comparisons between the rain rate with the three different estimation methods and the rain rate of the rain gauges installed in an analyzed area, we discuss the estimation accuracy. From the comparisons, the proposed method shows significantly better performance than do the other two methods even when weather conditions include heavy or violent rain.

A phased-array weather radar (PAWR) with fast scanning and wide elevation coverage capabilities has been developed. The PAWR transmits elevationally broad beams by feeding power to a limited number of its antenna elements, and receives signals reflected by precipitation media using all 128 antenna elements, each of which is connected to an analog-to-digital converter (ADC). After ADC sampling, digital processing of beamforming is applied to the received signals to accomplish receptions simultaneously over multiple angles. The PAWR can, thereby, make observations at 100-m range increments out to 60 km at 1° azimuth angle intervals over a range of 360° and at about 1° elevation-angle intervals from 0°-90° within the short time of 30 s. Reflectivity factors measured by the PAWR were compared with those from a collocated C-band radar, and were found to have a bias of 0.53 dB and a standard deviation of 3.68 dB, which indicates sufficient accuracy for observing precipitation. Furthermore, in a sample observation of convective rain, the PAWR detected a precipitation core 9 min before it reached the ground by its 30-s fast scanning and wide elevation coverage. It, thereby, became evident that the fast scanning and wide elevation coverage capabilities of the PAWR give earlier detection and a higher detection probability, which will enable earlier and more accurate warnings of severe weather phenomena and a better potential of mitigating the damage.

Osaka University, Toshiba and the Osaka Local Government started a new project to develop the Osaka Urban Demonstration Network. The main sensor of the Osaka Network is a 2-node Phased Array Radar Network and lightning location system. Data products that are created both in local high performance computer and Toshiba Computer Cloud, include single and multi-radar data, vector wind, quantitative precipitation estimation, VIL, nowcasting, lightning location and analysis. These new products are transferred to Osaka Local Government in operational mode and evaluated by several section in Osaka Prefecture.

Weather radar commonly uses a matched filter (MF) method to improve the range resolution and signal-to-noise ratio. A X-band phased array weather radar (PAWR), which is capable of 3-D precipitation observations in less than 30 s, is in operation at the Osaka University. The PAWR uses the MF method. In weather radar systems, the magnitude of the range sidelobes is an important topic because it can cause overestimation of the received power from a target, such as precipitation or ground clutter echoes. We propose a minimum mean square error (MMSE)-based pulse compression method to reduce the range sidelobes of the PAWR. We evaluated an MF, an MF with a raised-cosine window, and MMSE methods using numerical simulations and actual measurement data obtained from the PAWR. The results show that the MMSE method is clearly superior to the MF and MF with a raised-cosine filter methods when considering the reduction in the range sidelobes.

In this paper, the development of a polarimetric phased array weather radar, which consists of a dual-polarized antenna with 2-D circular planar phase-array elements, is discussed. The radar is capable of measuring the 3-D rainfall distribution in less than several tens of seconds. Digital beamforming (DBF) is an important component in the development process of the phased array radar. In this paper, precipitation radar signal simulations are performed taking into consideration radar concepts in order to discuss the estimation accuracy of polarimetric precipitation profiles (differential reflectivity, specific differential phase, and copolar correlation coefficient) with two DBF methods that are based on Fourier and minimum mean-square error (MMSE) methods. A comparison of the performance of the two methods indicates that MMSE is superior in accuracy because of the effect of a stable and a robust main lobe and adaptively suppressed side lobes. MMSE also provides precipitation measurements eliminating the directional dependence of a beam pattern for improving the accuracy of measurements. It is also shown that the estimated accuracies of the precipitation profiles are almost independent of the number of pulses.

The X-band phased array weather radar (PAWR) at Osaka University has a rapid scanning rate and is capable of high-density observations in elevation; it produces approximately 100 plan position indicator radar images with a 60-km range, at different elevation angles, in less than 30 s. The PAWR uses a fan-shaped beam with a narrow beamwidth (1.2°) in azimuth and a wider beamwidth (from 5° to 10°) in elevation. With digital beamforming (DBF), the elevation beamwidth can be reduced to 1.2°, using 128 antenna elements arranged in tandem. Although the fan-shaped beam is useful for rapid scanning, the received signals tend to be affected by ground clutter. In this paper, we investigate the clutter suppression capability of common DBF methods: Fourier, Capon, and minimum mean-square error (MMSE) beamforming. Furthermore, to improve performance when the PAWR data contain errors-such as lacking data caused by mechanical problems-a correction method is proposed. The effect of clutter suppression using MMSE is shown to be greatly improved if used together with the proposed correction method. The resulting method is shown to sufficiently suppress clutter in all elevation angles above a few degrees, even in the presence of strong clutter; its clutter reduction performance is compared and found to be superior to the ones of the analyzed conventional DBF methods.

We report an initial investigation of the new location method of a very high frequency (VHF) radiation source, using signals recorded at the International Space Station. A VHF interferometer (VITF) has two VHF sensors. Locating lightning with VHF bands is useful to locate the position of the charge distribution in the thunderstorm. The location method of a radio source proposed used two direction-of-arrival estimation techniques. One is the interferometric technique, and another is based on the ionospheric propagation delay measurement of received signals. The combination of the two techniques provides two angular positions of the radiation source. When an altitude of a radiation source is assumed, we can determine two possible positions. One of the two positions was associated with the radiation source, while the other was not. In this paper, we compared the position of lightning and sprite imager (LSI) data, which are simultaneously captured during a lightning emission, with the locating position near the emission. The data set of the VITF within 100 ms of the optical lightning emission captured with the LSI was used. The temporally simultaneous event seems to be associated with the same lightning event. The estimated radiation positions were spatially in close agreement with the optical lightning positions captured with LSI, under nighttime ionosphere conditions. From statistical analysis, the spatial difference of the standard deviation changed from 15.3 to 30.8 km depending on the installation direction of the VHF sensors. The usefulness and limitations of the method are also discussed.

Learning to program is an important subject in computer science courses. During programming exercises, plagiarism by copying and pasting can lead to problems for fair evaluation. Some methods of plagiarism detection are currently available, such as sim. However, because sim is easily influenced by changing the identifier or program statement order, it fails to do enough to support plagiarism detection. In this paper, we propose a plagiarism detection method which is not influenced by changing the identifier or program statement order. We also explain our method's capabilities by comparing it to the sim plagiarism detector. Furthermore, we reveal how our method successfully detects the presence of plagiarism.