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形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2017-10-16, 資料番号: PA1820030000

In Japan, severe weather phenomena such as heavy rains and tornados sometimes cause meteorological disasters. In many cases, these are micro scale phenomena in the sense of spatial and temporal resolutions, which make it difficult to detect them with conventional meteorological radars due to their insufficient spatial and temporal resolutions. Therefore, we have been developing meteorological radars with high resolution and accuracy such as phased array radar PAR and Ku-band broad band radar BBR, and radar network systems consisting of multiple PARs and BBRs to realize further enhancement of the radar performance in terms of efficiency and accuracy. These high-resolution radars, however, definitely produce large-volume data, which is unacceptable in a current backbone information network. In order to solve this problem, in this paper, we tackle the compression of the large-volume radar data by using Compressed sensing CS, which can realize highly efficient data compression for sparse signals. When using CS, the radar data are compressed by projecting it onto a randomly generated subspace, and the compressed data are reconstructed by solving a simple i¾?1 optimization problem. We apply the CS-based data compression scheme to measured radar reflectivity factor, and evaluate the relation between compression ratio and reconstruction accuracy. For the compression ratio of 0.3, rainfall rate calculated from the reconstructed radar reflectivity factor has a mean error of -0.89 mm/h with more than 30 dBZ precipitation.

This chapter outlines the existing cultivation systems, protocols, management approaches as well as issues encountered in plant factory operations. Comparisons, suggestions and potential solutions based on academic data and practical experiences are introduced. Those who are going to start a business or research in a plant factory and those who are already operating a plant factory have a general idea about the basic requirements of plants in various environmental conditions and the acceptable set points of such conditions. These experiences may not always maximize production but will reduce the risks of failure and loss in operation. More advanced technologies for the next generation of plant factories are expected on the basis of the current situation.

Phased array weather radar (PAWR) is capable of spatially and temporally high resolution observation. This means that a PAWR generates a huge amount of observation data, say 500 megabytes in every 30 seconds. To transfer this big data in a public internet line, this paper proposes a fast 3D compressive sensing method for PAWR. The proposed method reconstructs the original data, from compressed data, as the minimizer of a convex function which evaluates the local similarity in the spatial domain and the sparsity in the frequency domain. By combining blockwise optimization with Nesterov's acceleration, we obtain an approximate solution of the above convex optimization problem at high speed. Numerical simulations show that the proposed method outperforms conventional reconstruction methods.

This paper proposes an effective three-dimensional compressive sensing method for the phased array weather radar (PAWR), which is capable of three-dimensional observation with spatially and temporally high resolution. Because of the large amount of observation data, which is approximately 1 gigabyte per minute, data compression is an essential technology to conduct a network observation by multiple PAWRs. Even though many conventional studies applied compressive sensing (CS) to weather radar measurements, their reconstruction quality should be further improved. To this end, we define a cost function for a three-dimensional recovery exploiting not only local similarity but also global redundancy of weather radar measurements. Since the cost function is convex, we can derive an efficient algorithm based on the standard convex optimization techniques. Simulation results show that the proposed method achieves normalized errors less than 10% for 25% compression ratio with outperforming conventional two-dimensional methods.

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.

This paper proposes a compressive sensing method for the phased array weather radar (PAWR), which is capable of three-dimensional observation with high spatial resolution in 30 seconds. Because of the large amount of observation data, which is more than 1 gigabyte per minute, data compression is an essential technology to operate PAWR in the real world. Even though many conventional studies applied compressive sensing (CS) to weather radar measurements, their reconstruction quality should be further improved. To this end, we define a new cost function that expresses prior knowledge about weather radar measurements, i.e., local similarities. Since the cost function is convex, we can derive an efficient algorithm based on the so-called convex optimization techniques, in particular simultaneous direction method of multipliers (SDMM). Simulation results show that the proposed method outperforms the conventional methods for real observation data with improvement of 4% in the normalized error.

A probabilistic attenuation correction technique for differential reflectivity Z DR , based on the Bayesian theory, in a dual polarization networked environment is proposed. The proposed technique assumes a proportional relationship between specific differential attenuation A DP and specific differential phase K DP , and a power law relationship between backscattering differential phase δ co and Z DR . The algorithm maximizes a likelihood function by minimizing a cost function, and derives coefficients in the two relationship appropriately as well as a Z DR profile. 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.

The CO2 balance of a commercial closed system with artificial lighting (CSAL), in which lettuce plants (Lactuca sativa L. ‘Early Impulse’, ‘King Crown’, and ‘Cos Lettuce’) were produced every day and CO2 was added to the air by gas cylinders and workers’ respiration, was analyzed. In the experiment, 95% of the CO2 supplied from cylinders was apparently assimilated by the lettuce plants in the commercial CSAL, suggesting that the supplied CO2 was used efficiently. The amounts of CO2 assimilated by the lettuce plants and loss resulting from leakage, respectively, accounted for 78% and 22% of the total amount of CO2 supplied. The amounts of CO2 supplied by the cylinders and by the workers’ respiration, respectively, accounted for 83% and 17% of the total amount of CO2 supplied. Based on the analysis, a relatively high CO2 utilization efficiency of 78% was observed in the experiment despite the operation rate of 33%, which is defined as the percentage of the culture beds with plants. If the operation rate could be increased to 100%, the CO2 utilization efficiency would reach 92%. These results showed that CO2 supplied by the workers’ respiration helped to reduce the amount of CO2 supplied by the cylinders and hence the CO2 cost in a commercial CSAL.

Conventional meteorological radar systems with high power transmitters at S-, or C-band have long coverage areas. On the other hand, dense radar network systems with low power transmitters at X-, or Ku-band has been proposed ( Junyent et al. , J. Atmos. Oceanic Technol., 27, 2010, pp. 61 – 78.) (S. Shimamura et al., 144, 36th Conference and Radar Meteorology, Breckenridge CO USA, Sep., 2013) which can fill in observation gaps in low altitudes because of earth curvature and have good accuracy and resolution. A weather radar, especially with transmitting short-wavelength pulses, is affected by precipitation attenuation. Various methods for precipitation attenuation correction have been developed in the literature. A network based attenuation correction technique in a multiple radar network has been suggested (Chandrasekar and Lim, J. Atmos. Oceanic Technol., 25, 2008, pp.1755 – 1767). In this paper, we suggest a probabilistic technique for precipitation attenuation correction based on a dual polarization radar network by using the dual-polarization attenuation correction technique ( Lim and Chandrasekar, IEEE Transactions on Geoscience and Remote Sensing, Vol.44, No.4, April 2006, pp. 1011 – 1021) (Liu, et al., Geoscience and Remote Sensing Symposium, IGARSS 2006, pp. 1910–1913).

A new Phased Array Radar (PAR) system for meteorological application has been developed by Toshiba Corporation and Osaka University under a grant of NICT, and installed in Osaka University, Japan in 2012. The phased array radar system developed has the unique capability of scanning the whole sky with 100m and 10 to 30 second resolution up to 60 km. The system adopts the digital beam forming technique for elevation scanning and mechanically rotates the array antenna in azimuth direction within 10 to 30 seconds. The radar transmits a broad beam of several degrees with 24 antenna elements and receives the back scattered signal with 128 elements digitizing at each elements. Then by digitally forming the beam in the signal processor, the fast scanning is realized. After the installation of the PAR system in Osaka University, the initial observation campaign was conducted in Osaka urban area with Ku-band Broad Band Radar (BBR) network, C-band weather radar, and lightning location system. The initial comparison with C band radar system shows that the developed PAR system can observe the behavior of the thunderstorm structure in much more detail than any other radar system. The observed high temporal resolution images of the severe thunderstorm are introduced, showing the potential capabilities of the PAR system. The correlation coefficient of the reflectivity in PAR with C band radar ranges from 0.6 to 0.9 as a function of the distance from the PAR.

A small-baseline weather radar network consisting of the Ku-band broadband radars (BBR) for meteorological application has been developed. The BBR is a remarkably high-resolution close-range Doppler radar designed for detecting and analyzing rapidly evolving weather phenomena such as severe thunderstorms, tornadoes, and downbursts, which often cause damage to our lives seriously. A radar network with several BBRs (the BBR network) observes multi-directionally and simultaneously these severe phenomena with high resolution in space and time, and with high accuracy. In this presentation, the concept of the project and the initial observation results of the BBR network are presented.