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.

Real-time 30-second-refresh numerical weather prediction (NWP) was performed with exclusive use of 11,580 nodes (~7%) of supercomputer Fugaku during Tokyo Olympics and Paralympics in 2021. Total 75,248 forecasts were disseminated in the 1-month period mostly stably with time-to-solution less than 3 minutes for 30-minute forecast. Japan's Big Data Assimilation (BDA) project developed the novel NWP system for precise prediction of hazardous rains toward solving the global climate crisis. Compared with typical 1-hour-refresh systems, the BDA system offered two orders of magnitude increase in problem size and revealed the effectiveness of 30-second refresh for highly nonlinear, rapidly evolving convective rains. To achieve the required time-to-solution for real-time 30-second refresh with high accuracy, the core BDA software incorporated single precision and enhanced parallel I/O with properly selected configurations of 1000 ensemble members and 500-m-mesh weather model. The massively parallel, I/O intensive real-time BDA computation demonstrated a promising future direction.

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.

An accurate estimation of precipitation with high temporal and spatial resolution on a global basis are useful for not only scientific research but also flood warning, hydrological applications, weather forecast and so on. GSMaP provides global precipitation with 0.1 degree and 1 hour resolution and has been used widely in more than thousands of organizations for operational use. This GSMaP system has several products including GSMaP_ MVK and GSMaP_Gauge and the GSMaP_Gauge is the best product among them in terms of accuracy though it has the latency of a few days. In this study, some algorithm modifications in the GSMaP_Gauge algorithm are reported, focusing on the precipitation over the desert in central Australia. In the current algorithm, sometimes precipitation over desert and an artificial precipitation pattern are detected. Precipitation distribution over desert in Australia from GSMaP _MVK, GSMaP_Gauge and ground based measurement are compared in this study, and some modifications in the GSMaP_Gauge algorithm are discussed. In the new version, it is shown that the modified results show more realistic pattern and it is compared with the ground rain gauge measurements.

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.

Global Satellite Mapping of Precipitation (GSMaP) is a system to produce global surface precipitation field with 0.1 degree and 1 hour resolution from geostationary infrared imager and microwave radiometer in low earth orbit (Ushio et al. 2019, Mega et al. 2018). On this system, the input data sets are precipitation rate retrieved from the microwave radiometers and infrared images to compute the moving vector fields. Based on the moving vector fields calculated from the successive IR images, precipitation fields are propagated and refined on the Kalman filter model which uses the relationship between the infrared brightness temperature and surface precipitation rate. Because of this methodology, basically the resolution of the GSMaP products is limited on the resolution of the geostationary infrared images.

A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth's system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests.

The importance of accurately measuring global precipitation is increasingly being recognized in terms of improving the forecast of extreme rainfall events that cause disasters, monitoring the Earth system, and understanding the Earth's water and energy cycle. To meet this demand, Global Satellite Mapping of Precipitation (GSMaP) (Kubota et al., 2007; Ushio et al., 2009) in Japan supplies products that have resolution of 0.1 degrees and less than 1 hour as a part of the GPM (Global Precipitation Measurements) project and the products are widely used by agencies around the world. Among the GSMaP products, gauge adjusted GSMaP (GSMaP _ Gauge) has been recently developed and some validation studies are going on. In this study, recent updates on the GSMaP _ Gauge algorithm are presented.

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.

As one of Japanese Global Precipitation Measurement (GPM) products, Global Satellite Mapping of Precipitation (GSMaP) product has been provided by Japan Aerospace Exploration Agency (JAXA) to distribute hourly global precipitation map with 0.1×0.1 deg. lat/lon grid. The GSMaP products are composed of “standard product”, “near-real-time product”, “real-time product”, and “reanalysis product”. The standard products are processed 3 days after observation, and the near-real-time products are processed 4 hours after observation. In addition, the real-time version, GSMaP_NOW has been provided by the JAXA over the geostationary satellite “Himawari-8” region since Nov. 2015. Recently, the GSMaP algorithms were majorly updated on September 2014 and January 2017. The current paper describes overviews of the GSMaP products and recent progress of the GSMaP product.

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.

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.

This paper describes the rain gauge adjusted algorithm for the Global Satellite Mapping of Precipitation (GSMaP_Gauge_NRT) that estimates the surface rainfall rate with the resolution of 0.1 degree and 1 hour resolution over the globe in near realtime. Precipitation is one of the most important parameters on the earth system, and the global distribution of precipitation and its change are essential data for modeling the water cycle, maintaining the ecosystem environment, agricultural production, improvements of the weather forecast precision, flood warning and so on. In the GPM (Global Precipitation Measurement) project, the integrated products of high resolution mapping of precipitation from microwave measurements from constellation satellite and infrared radiometers from geo-stationary orbit are developed and supplied to the public. However, sometimes such high resolution product such as GSMaP_NRT underestimates the surface precipitateon and causes large error for hydrological modeling. In this paper, global gauge data set are combined with GSMaP_NRT. After describing and evaluating the GSMaP_Gauge which is a gauge adjusted product of the GSMaP_MVK, the methodology of a new combined algorithm (GSMaP_Gauge_NRT) is described.

Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the “big data” from next-generation sensors and big simulation. Therefore, we propose “big data assimilation” (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era.

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.

Phased array weather radars, particularly with high temporal resolution, essentially need a robust and fast beamformer to accurately estimate precipitation profiles such as reflectivity and Doppler velocity. In this paper, we introduce a neural-network-based beamformer to address this problem. In particular, the optimum weight vector is computed by modeling the problem as a three-layer radial basis function neural network (RBFNN), which is trained with I/O pairs obtained from the optimum Wiener solution. The RBFNN was chosen because of its characteristic of accurate approximation and good generalization, and its robustness against interference and noise. The proposed RBFNN beamforming method is compared with traditional beamforming methods, namely, Fourier beamforming (FR), Capon beamforming, and the flower pollination algorithm (FPA), which is a recently proposed nature-inspired optimization algorithm. It is shown that the RBFNN approach has nearly optimal performance in various precipitation radar signal simulations relative to the rival methods. The validity of the RBFNN beamformer is demonstrated by using real weather data collected by the phased array radar (PAR) at Osaka University, and compared with, in addition to the FR and FPA methods, the minimum mean square error beamforming method. It is shown that the RBFNN method estimates the reflectivity of the PAR at Osaka University with less clutter level than those of the other three methods.

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.

The Global Satellite Mapping of Precipitation (GSMaP) product, which was developed toward the Global Precipitation Measurement (GPM), is operating and distributed in near-real-time basis at the Japan Aerospace Exploration Agency (JAXA). In this paper, we will introduce development status of the latest GSMaP algorithm, which will be distributed as one of JAXA GPM product.

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.

A new high-resolution Ku-band Doppler radar for meteorological applications has been developed. With the new system design, the radar can accurately measure the radar reflectivity factor with 4-m resolution over a range from 40 m to several kilometers for 100-mW power using a pulse compression technique. Details of the system design, signal processing algorithm, and data acquisition procedures are described. To demonstrate the accuracy of the system, the radar reflectivity measurements are compared with the Joss-Waldvogel disdrometer measurements, and fairly good agreement is shown. The ability of the system to capture the backscattered signal and Doppler spectrum from rain volume at low altitude with high resolution is demonstrated for both convective- and stratiform-type rain events.

This paper documents the production and validation of retrieved rainfall data obtained from satellite-borne microwave radiometers by the Global Satellite Mapping of Precipitation (GSMaP) Project. Using various attributes of precipitation derived from Tropical Rainfall Measuring Mission (TRMM) satellite data, the GSMaP has implemented hydrometeor profiles derived from Precipitation Radar (PR), statistical rain/no-rain classification, and scattering algorithms using polarization-corrected temperatures (PCTs) at 85.5 and 37 GHz. Combined scattering-based surface rainfalls are computed depending on rainfall intensities. PCT85 is not used for stronger rainfalls, because strong depressions of PCT85 are related to tall precipitation-top heights. Therefore, for stronger rainfalls, PCT37 is used, with PCT85 used for weaker rainfalls. With the suspiciously strong rainfalls retrieved from PCT85 deleted, the combined rainfalls correspond well to the PR rain rates over land. The GSMaP algorithm for the TRMM Microwave Imager (TMI) is validated using the TRMM PR, ground radar [Kwajalein (KWAJ) radar and COBRA], and Radar Automated Meteorological Data Acquisition System (AMeDAS) precipitation analysis (RA). Monthly surface rainfalls retrieved from six microwave radiometers (GSMaP_MWR) are compared with the gauge-based dataset. Rain rates retrieved from the TMI (GSMaP_TMI) are in better agreement with the PR estimates over land everywhere except over tropical Africa in the boreal summer. Validation results of the KWAJ radar and COBRA show a good linear relationship for instantaneous rainfall rates, while validation around Japan using the RA shows a good relationship in the warm season. Poor results, connected to weak-precipitation cases, are found in the cold season around Japan.

Sampling errors of space-time average rain rate in 1-30 day integration times and 0.1/spl deg/-5.0/spl deg/ space domains, for simulated GPM (Global Precipitation Measurement) eight-satellite group, were estimated using 3-year radar-AMeDAS composites data around Japan. Moreover, we formulated sampling error RMS (%) as a function of space scale A (degree), time scale T (day), and true average rain rate R (mm/h).

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.

Precipitation is one of the most important parameters on the earth system, and the global distribution of precipitation and its change are essential data for modeling the water cycle, maintaining the ecosystem environment, agricultural production, improvements of the weather forecast precision, flood warning and so on. In the GPM (Global Precipitation Measurement) project, the microwave radiometers observing microwave emission from rain will be placed on many low-orbit satellites, to reduce the interval to about 3 hours in observation time for each location on the earth. Although the GPM can provide the global precipitation fields with 3 hour resolution, the precipitation map with higher resolution (< 1 hour) is required for some operational users such as flash flood warning systems and also the monthly based precipitation map is required from the climatology studies. In this presentation, the GSMaP_MVK which is a product of surface rainfall rate with 0.1 degree and 1 hour resolution on a global basis and GSMaP_Gauge which is a gauge adjusted product to the GSMaP_MVK for climatological studies are introduced, focusing particularly on structure and performance of the algorithm and some initial evaluation tests.

Polarimetric measurements of scattering properties of vegetation were performed using the 35 GHz polarimetric scatterometer. The miniature forest is constructed inside the experimental laboratory to measure the scattering dependence on forest biomass at Ka-band by changing the degree of density of trees. A soil is underlying the trees and soil moisture content is also changed to find out if soil moisture content can be observed to what extent leaf area index (LAI) becomes large. As a result of increasing density of trees, the volume scattering in vegetation layer becomes larger as LAI increases and the scattered wave from soil is reasonably more attenuated. Polarization ratio HH/VV of received power is calculated to study polarization difference in attenuation. The results show that HH polarized electric field penetrates into vegetation and reaches soil surface more strongly than VV polarized electric field.

Polarimetric measurements of scattering coefficient /spl sigma//sup 0/ of a bare soil were performed changing a roughness of a soil surface and a soil moisture content using the 35 GHz polarimetric scatterometer. At Ka band, a few experimental results of /spl sigma//sup 0/ data are available. One of the purpose of this experiment is to obtain the surface backscattering characteristics to evaluate surface clutter interference with precipitation measurement from space using the Dual frequency Precipitation Radar (DPR, 13.8 GHz and 35.5 GHz), which is planned to be onboard the Global Precipitation Measurement (GPM) Mission core satellite to observe precipitation globally. In evaluation of surface clutter interference, /spl sigma//sup 0/ data for various surface conditions are needed, especially /spl sigma//sup 0/ dependence on soil moisture content and surface roughness. Another purpose of this experiment is to apply the measured /spl sigma//sup 0/ data to estimate a soil moisture content globally after the launch of the GPM core satellite. An angular scan range of the DPR is from nadir direction though the incidence angle of 8.4 degrees (with the 35.5 GHz radar) or 17 degrees (with 13.6 GHz radar), so the measured data by the DPR is useful to observe the earth surface condition globally. In this study, the roughness of the soil surface was measured with a laser profile meter to determine the roughness dependence of the scattering coefficient. A soil moisture content was measured comparing the weight of the soil before and after the heating of the soil. The soil was heated enough to contain no water. To increase the number of the independent samples for each experimental conditions (soil moisture content/surface roughness/incidence angle), azimuthal angle is changed like clockwork using the turntable of 2 m diameter. The system of the scatterometer is network analyzer based polarimeter.