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104 results on '"Nishiyama, Koji"'

14. Model analysis of radar echo split observed in an artificial cloud seeding experiment

Author

Tomine Kikuro, Nishiyama Koji, and Shimada Masaki

Subjects
010504 meteorology & atmospheric sciences, Computer simulation, Meteorology, Echo (computing), Cloud seeding, 010502 geochemistry & geophysics, Snow, Atmospheric sciences, 01 natural sciences, law.invention, law, Weather Research and Forecasting Model, Environmental science, Seeding, Radar, Graupel, 0105 earth and related environmental sciences
Abstract

An artificial cloud seeding experiment was performed over the Japan Sea in winter to show how massive seeding could be effective to mitigate heavy snowfall damage. The results showed that 20 min after cloud seeding, a portion of the radar echo beneath the seeding track was weakened to divide the radar echo into two parts. In order to analyze the results, a numerical simulation was conducted by using the Weather Research and Forecasting model verion 3.5.1. In this simulation, the seeding effects were represented as phenomena capable of changing rain particles by accreting cloud ice and snow to form graupel particles and by changing cloud liquid water to snow particles. The graupel particles fell rapidly, thus temporarily intensifying the rainfall, which subsequently decreased. Therefore, the weakened radar echo in the field experiment is deemed to have been caused by the increase in rapidly falling graupel particles.

Published
2016

16. An example of canal formation in a thick cloud induced by massive seeding using liquid carbon dioxide

Author

Tomine Kikuro, Shimada Masaki, Wakimizu Kenji, and Nishiyama Koji

Subjects
Meteorology, business.industry, Cloud seeding, Cloud computing, Wind direction, Snow, Atmospheric sciences, law.invention, law, Weather modification, Liquid carbon, Environmental science, Seeding, Radar, business
Abstract

The purpose of this experiment is to show that massive cloud seeding is effective in mitigating the damage caused by heavy snowfall. In order to show its effect, we attempted to form a canal in a thick convective cloud by massive seeding, and left the parts that were not influenced by the seeding as a reference to show that the canal was formed by the massive seeding only. The seeding was carried out by using an aircraft. The seeding rate and air speed of the aircraft were 35 g s−1 and 115 m s−1, respectively. The flight course for seeding was selected to be parallel to the wind direction to ensure that the dispersed liquid carbon dioxide did not influence both sides of the course. The results show that a part of the radar echo observed from onboard beneath the seeding track was weakened and divided the radar echo into two parts 20 minutes after the cloud top and the bottom were seeded, and distribution of rainfall rate on the ground from the target cloud was confirmed to be divided into two parts 24 minutes after the seeding. The target cloud was torn along the seeding track, and we could see the sea surface through the break in the cloud. Canal formation occurred in the cloud along the seeding track. Clouds and snowfall were left on both sides of the canal. The results show that supercooled liquid cloud particles along the seeding track evaporated to form larger precipitable particles which grew and fell rapidly.

Published
2015

18. Comparison of Pattern Extraction Capability between Self-Organizing Maps and Principal Component Analysis

Author

Iseri, Yoshihiko, Matsuura, Tomonori, Iizuka, Satoshi, Nishiyama, Koji, and Jinno, Kenji

Subjects
PCA, Pattern extraction, Comparison, SOM, Simulation
Abstract

Principal Component Analysis (PCA) has been extensively used for multivariate data analysis including climate data analysis. However, recent studies suggest the possible use of Self-Organizing Maps (SOM) for climate data analysis. In order to adequately utilize the PCA and SOM, it is crucial to clarify the difference of both methods. This study compared the pattern extraction capability of SOM and PCA in order to clarify the difference of both methods. Comparisons of the methods were performed by conducting two kinds of simulations. The first stimulation confirmed SOM can extract non-orthogonal patterns while the patterns extracted by PCA are mutually orthogonal. The second simulation investigated how the patterns extracted by each method would change when new inputs were added. When new inputs were added, PCA did not extract the pattern in the original inputs and additional inputs. On the other hand, SOM extracted the pattern in original and additional inputs.

Published
2009

20. Rainfall Estimation in the Chikugo River Basin by Atmospheric Downscaling Using Artificial Neural Networks

Author

Ishikawa, Izumi, Olsson, Jonas, Jinno, Kenji, Kawamura, Akira, Nishiyama, Koji, and Berndtsson, Ronny

Subjects
Artificial neural network, correlation analysis, Atmospheric downscaling, wind speeds, precipitable water, GPV data
Abstract

For the proper water resources management of the Chikugo River basin, the prediction of both drought and heavy rainfall needs to be carried out by the conventional and engineering method which can be useful to for the practitioners who work on the water r

Published
2002

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