Real-time weather radar imaging technology is required for generating short-time weather forecasts. Moreover, such technology plays an important role in critical-weather warning systems that are based on vast Doppler weather radar data. In this study, we propose a weather radar imaging method that uses multi-layer contour detection and segmentation based on MAP-MRF estimation. The proposed method consists of three major steps. The first step involves generating reflectivity and velocity data using the Doppler radar in the form of raw data images of sweep unit in the polar coordinate system. Then, contour lines are detected on multi-layers using the adaptive median filter and modified Canny's detector based on curvature consistency. The second step interpolates contours on the Cartesian coordinate system using 3D scattered data interpolation and then segments the contours based on MAP-MRF prediction and the metropolis algorithm for each layer. The final step involves integrating the segmented contour layers and generating PPI images in sweep units. Experimental results show that the proposed method produces a visually improved PPI image in 45% of the time as compared to that for conventional methods.

This study demonstrates the ability of a portable X-band Doppler weather radar (XDR) to measure Doppler velocity (Vd). Existing portable X-band weather radars are housed in a container and hence have to be carried by a truck. Therefore they have limitations in their installation places. For installations at small areas where the existing X-band weather radars cannot be installed (e.g., rooftop area of small building), XDR is designed to be carried by a cart. Components of the outdoor unit (a parabolic antenna with a diameter of 1.2 m, magnetron transmitter, and radio frequency (RF) and intermediate frequency (IF) analog components) are housed in a compact body with a weight less than 300 kg. The radar operation, IF digital processing, and data storage are carried out by a desktop computer having a commercial IF digital receiver. In order to attain the required portability and reduced purchase and running costs, XDR uses a magnetron transmitter. Because XDR is the first that utilizes an IF digital receiver for the signal processing specific to magnetron transmitters (i.e., the phase correction of received signals due to the randomness of the transmitted pulse phase), Vd measured by XDR (hereafter VdXDR) was assessed. Using the dataset collected from 25 to 26 October 2009 at the Shigaraki MU Observatory (3451'N, 13606'E), the equivalent radar reflectivity factor (Ze) and VdXDR were assessed using Ze and Vd measured by a Micro Rain Radar and a L-band Doppler radar named LQ-7. The results using correlation coefficients and regression lines demonstrate that XDR measured Ze and Vd accurately. The results also show that IF digital receivers are useful for providing magnetron weather radars with the function of Vd measurement, and further suggest that a combination of IF digital receiver and magnetron transmitter contributes to future development of Doppler weather radars, because high cost performance is strongly required for a precipitation monitoring radar network.

We have developed a high-temperature superconducting (HTS) filter with narrow bandwidth characteristic for receiver of weather radar in order to reduce interference between adjacent radar channels. To realize a filter with which a narrow bandwidth and low insertion loss are compatible, resonators with high unloaded Q (Qu) value are required. Hairpin microstrip resonators with 1.5 times wavelength were adopted to suppress the radiation loss and achieve a high Qu value. The developed HTS filter has 8-pole quasi-elliptic function response for sharp skirt characteristic. The measured frequency response of the developed filter shows center frequency of 5370 MHz, insertion loss of 2.04 dB and maximum return loss of 15 dB, which agrees with the designed responses.

The purpose of this study is the real-time estimation of Doppler spectral moments for precipitation in the presence of ground clutter overlap. The proposed method is a frequency domain approach that uses a Gaussian model both to remove clutter spectrum and to estimate weather spectrum. The main advantage of this method is that it does not use processes like several fitting procedures and enables to estimate profiles of precipitation in a short processing time. Therefore this method is efficient for real-time radar observation with high range and time resolution. The performance of this method is evaluated based on simulation data and the observation data acquired by the Ku-band broad band radar (BBR) [1].

A C-band polarimetric radar on Okinawa Island successfully observed large-scale bird migrations over the western Pacific Ocean. The birds generated interesting polarimetric signatures. This paper describes the signatures and speculates bird behavior.

This paper introduces an automatic and multi-instrument snowfall observation system and proposes techniques that could be used in the estimation of snowfall characteristics. The instruments used in this study include two microwave radars, an optical lidar, a CCD camera based imaging system and high-accuracy electrical balances for reference data. The emphasis has been on obtaining good temporal resolution and synchronization accuracy of separate datasets. In most research done so far, this has not been a principal point, either because only very long snowfall events have been measured, or wide area estimates were desired, or due to limitations in manual sampling methods and other technical issues. The measurements were also contained in a small area to make sure that all instruments record data from the same target. One radar and the optical lidar recorded an atmospheric profile up to 6000 m, while the other radar, the imaging system and the two balances recorded snowfall on the ground level. The combination of optical, microwave and direct visual observations of snowfall show that a change in cloud conditions can result in snowfall having different characteristics. The lidar backscatter was used as main indicator of transitions in cloud conditions. A direct visual evaluation of snowflake size distribution using a CCD camera shows that it is extremely helpful in order to interpret radar data. The camera observed velocity distribution showed no large variations between snowfall events, however, it could be useful in detecting graupel and hail precipitations which have much faster terminal velocities. This paper will conclude with a discussion on further elaborating the use of lidar and visual data to complement radar observations of snowfall.

A novel method is proposed to retrieve image sequences with the goal of forecasting complex and time-varying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retrieval method that supports weather forecasters in predicting local precipitation. To characterize the radar echo patterns, an appearance-based representation of the echo pattern, and its velocity field are employed. Temporal texture features are introduced to represent local pattern features including non-rigid complex motion. Furthermore, the temporal development of a sequence is represented as paths in eigenspaces of the image features, and a normalized distance between two sequences in the eigenspace is proposed as a dissimilarity measure that is used in retrieving similar sequences. Several experiments confirm the good performance of the proposed retrieval scheme, and indicate the predictability of the image sequence.

Winter thundercloud on the coast of the Sea of Japan is difficult for the detection and forecast. This paper reports the new method of thundercloud detection using C-Band weather radar data and meteorological data from rawin sonde. This method includes the thunderclouds echo tracking and chronological evaluation of thundercloud life-cycle stages. The detection probability of critical area for cloud-to-ground lightning is over 90% on winter cloud echo data. This method is workable on realtime processing in work-station computer.

Several major aircraft accidents have been attributed to low-altitude wind shears, which are normally caused by microbursts or gust fronts. Terminal Doppler Weather Radar (TDWR) systems are being installed near major airports for the detection of low-altitude wind shears. In order to develop a TDWR system further, low-altitude wind shears were numerically simulated in this study. The basic equations, which contain prognostic equations for air velocity, pressure, temperature, water vapor, and rainwater, were solved using a finite difference scheme. A terrain-following coordinate transformation was employed to simulate terrain effects. The simulation results are presented in this paper.

Weather clutter was measured using an L-band long-range (200 nmi.) air-route surveillance radar (ARSR), from the stored data of five scans. It is shown that weather clutter obeys a Weibull distribution, and moreover such Weibull-distributed weather clutter also obeys a Weibull distribution after passing through a moving target indicator (MTI) processor realized by a double canceller. Some adaptive methods for suppressing Weibull-distributed clutter are summarized.