Coherent Doppler lidar (CDL) for measuring wind by transmitting frequency-modulated pulse or continuous wave was proposed and demonstrated. A theoretical study showed that, while typical CDL uses single-frequency short-duration pulse by which distance and velocity resolutions are in a trade-off relationship, the new CDL allows us to specify the two resolutions independently. By using a prototype which can work as both typical and new CDLs, observations to clear or cloudy sky were carried out. Analyses of the observed data revealed that the independency was realized. Specifically, about five times finer velocity resolution than typical CDL was confirmed with no sacrifice of distance resolution.

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.

The performance simulation theory of low-level wind shear (LLWS) detection using an airborne coherent Doppler lidar (CDL) is shown. The simulation theory that this performance is based on is analogous to the turbulence and wind shear detections with an airborne Doppler radar, as specified in the radio technical commission for aeronautics (RTCA) document (DO)-220. Prior theoretical studies of CDL regarding: 1) signal-to-noise ratio (SNR) equation; 2) the relation between SNR and wind sensing performance; and 3) atmospheric parameters are fully utilized. An example simulation result under a practical condition satisfies the requirements to specify minimum operational performance standards (MOPS) in DO-220. Further, the simulation result is well aligned with past experimental results on the detectable range of LLWS. The results of this study can be utilized to establish MOPS for LLWS detections using an airborne CDL in the future.

The theoretical performance of an airborne coherent Doppler lidar (CDL) for turbulence detections is shown. Irregular wind fluctuation which causes aircraft vertical acceleration is defined as turbulence. The detection method is based on spectral width estimation of a heterodyne-detected signal. The equations for simulation are derived by referring the one for a Doppler radar. Performance theory on a CDL is additionally utilized to derive the equations. Probabilities of missed and false hazard indication are simulated with an example set of input parameters. The simulated turbulence detectable range roughly agrees with past experimental results. The contents of this paper can contribute to the future establishment of minimum operational performance standards (MOPS) for turbulence detection using an airborne CDLs.

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.

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.

A new method for three-axis wind field retrieval on multiple radar environment is proposed. The proposed method is designed especially to retrieve z-axis velocities in low altitudes with high accuracy by considering spatial correlation of wind velocities. A numerical simulation showed its advantage that the proposed method estimated z-axis velocities at a low altitude of 1500 m where the traditional method output an ambiguous wind field. It is expected that, even at low altitudes, the proposed method retrieves updrafts which are a key sign of thunderstorm initiation.

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.

A probabilistic attenuation correction technique for differential reflectivity ZDR, based on the Bayesian theory, in a dual polarization networked environment is proposed. The proposed technique assumes a proportional relationship between specific differential attenuation ADPand specific differential phase KDP, and a power law relationship between backscattering differential phase δcoand ZDR. The algorithm maximizes a likelihood function by minimizing a cost function, and derives coefficients in the two relationship appropriately as well as a ZDRprofile. To evaluate the proposed technique, one dimensional simulation on X-band using S-band radar data is performed. In this paper, details of the algorithm of the proposed precipitation attenuation correction technique and evaluation results of the simulation are described.

A probabilistic attenuation correction technique for a single-polarization networked radar environment is proposed. The proposed technique, based on the Bayesian theory, makes a maximization of a likelihood function of Hitschfeld-Bordan (HB) reflectivity obtained by each radar node. A variance of the HB reflectivity (σHB2) is defined and regarded as instability of each HB reflectivity in the proposed technique. In the X-band simulation based on S-band real radar data, it is revealed that the corrected reflectivity obtained by the proposed technique has good accuracy, and the proposed technique works more stably than the HB technique. The proposed technique is also performed using CASA IP-1 dual-polarization radar network observations, and the corrected reflectivity by the proposed technique has a good agreement with differential phase (ΦDP)-based corrected reflectivity.

A fast-scanning phased array weather radar (PAWR) with a digital beam forming receiver is under development. It is important in beam forming for weather radar observation with temporally high resolution to form a stable and robust main lobe and adaptively suppress sidelobes with a small number of pulses in order to accurately estimate precipitation profiles (reflectivity, mean Doppler velocity, and spectral width). A minimum mean square error (MMSE) formulation with a power constraint, proposed in this paper, gives us adaptively formed beams that satisfy these demands. The MMSE beam-forming method is compared in various precipitation radar signal simulations with traditional beam-forming methods, Fourier and Capon methods, which have been applied in atmospheric research to observe distributed targets such as precipitation, and it is shown that the MMSE method is appropriate to this fast-scanning PAWR concept.

Ku-band broadband radar is a short-range (15 or 20 km) high-resolution (range and temporal resolution of several meters and 1 min per volume scan with 30 elevations) weather radar to resolve fine structures of precipitation from the near surface (several tens of meters), and a networked observation with several Ku-BBRs covers the troposphere accurately and efficiently. In this paper, two latest observation results of the Ku-BBR and the Ku-BBR network are described. One is an observation of a small-scale tornado in Shonai airport by single radar, in which a fine structure of the small tornado is resolved in 4-D. The other is preliminary results of a networked observation with three Ku-BBRs in north Osaka area, in which fine structures of precipitation are resolved simultaneously and multi-directionally.

Raindrop size distribution (DSD) retrieval algorithm for an X-band dual-polarization weather radar is proposed. In this algorithm, DSD range profile is estimated to match the dual-polarization measurements, where the forward and back scatters are formulated simultaneously to avoid the two-step process of attenuation correction and DSD retrieval. For the optimization, the iterative maximum likelihood is applied, in which a posterior PDF of DSD parameters are calculated and then extended to radar network environment. Estimation accuracies of log(Nw) (Nw; mm-1m-3) and D0(mm) derived from single-radar numerical simulation are a mean bias (MB) of -0.02 and a standard deviation (SD) of 0.23, and an MB of 0.01 and an SD of 0.10, respectively.

Raindrop size distribution (DSD) retrieval algorithm for a weather radar network consisting of X-band dual-polarization weather radars is proposed. This algorithm is based on a DSD retrieval method for a single-radar (SRR), which is elaborated in our paper of the SRR, “RAINDROP SIZE DISTRIBUTION (DSD) RETRIEVAL FOR X-BAND DUAL-POLARIZATION RADAR” in this conference. The SRR outputs an ML solution of the two DSD parameters of the normalized Gamma DSD, Nwand D0. The proposed algorithm calculates posterior probability of the DSD profile with the use of a prior probability, and then, the posterior probabilities on different polar coordinates are integrated to that on a common Cartesian grid by Bayesian theorem. An example of the numerical simulation shows that the fluctuated DSD profiles of the SRRs are improved to less fluctuated DSD profiles on a common Cartesian grid with spatially homogeneous quality.

A radar network with several Ku-band broadband radars (BBR), which are short-range (15 km) and ground-based Doppler radar with remarkably high range and temporal resolution of about several meters and 1 min per volume scan, respectively, observes multi-directionally and simultaneously hazardous small-scale phenomena such as tornadoes and microbursts with high resolution and accuracy. In this paper, initial observation results of the BBR network are presented. Two BBRs observes significantly similar patterns of precipitation due to the high temporal resolution, and integrated reflectivity between the two BBRs showed impressively high quality images of precipitation.

A new high-resolution Doppler radar, called Ku-band broadband radar (BBR), with fast scanning capability for meteorological application has been developed. Due to the new system design, the BBR can accurately measure the radar reflectivity factor with a range resolution of several meters and a time resolution of 55 s per volume scan from the nearest range of 50 m to 15 km for 10-W power using pulse compression. In this paper, the basic concepts, configuration, and signal processing of the BBR are described. In the initial observation, the observation accuracy of reflectivity is evaluated using Joss–Waldvogel disdrometer (JWD). As a result, the reflectivity of the BBR is in fairly good agreement with that of JWD. In addition, in the spiral observation, a fine structure of a thunderstorm obtained by the BBR is presented.

Hazardous weather such as severe storms, tornadoes and hurricanes are small scale weather phenomena whose detailed profiles are important practically and scientifically. Despite this, conventional radars cannot resolve them, because of their low resolution capability. In this study, we developed a new high resolution Ku-band Doppler radar with scanning capability for meteorological applications. With the new radar system design, the radar can accurately measure the radar reflectivity factor with the range resolution of 4 m and the time resolution of 1 min per 1 volume scan from the nearest range of 50 m to about 15 km for 10 W power using pulse compression technique. In this paper, the details of the system design and the signal processing algorithm are described. To demonstrate the accuracy of this system, the radar reflectivity measurements are compared with 2D-video disdrometer measurements. The initial observation results of spiral mode are also shown.