@article {Hanaka:2017:0736-2935:4935,
title = "Changes in over-ground lateral attenuation determined from short-term measurements dependent on topographical conditions",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2017",
volume = "255",
number = "3",
publication date ="2017-12-07T00:00:00",
pages = "4935-4945",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2017/00000255/00000003/art00111",
author = "Hanaka, kazuyuki and Shinohara, Naoaki and Makino, Koichi and Yamamoto, Ippei",
abstract = "This paper describes a result of examination how topographical conditions affect over-ground lateral attenuation, by comparing estimations obtained from short-term noise measurements at two airports. In a previous paper, we reported results of an experimental examination of over-ground
lateral attenuation using data of both short-term measurements repeatedly carried out and long-term continuous monitoring of aircraft noise at narita Airport. According to the results, lateral attenuation greatly changed dependent on meteorological conditions and did not match calculations
from existing empirical equations of lateral attenuation. Besides, the trend was different among measurement points. We guessed that the uneven ground surface below the sound propagation path may have affected the estimation. The visibility of the sound source was also different among measurement
points. Therefore, we carried out another short-term measurement at sendai Airport surrounded with flat and uniform rice paddy fields and we repeated analysis of over-ground lateral attenuation using the same method as before. As a result, we confirmed that lateral attenuation increased smoothly
with the distance and the trend was similar each other among measurement points, unlike the result at Narita. Based on this result, we considered how to construct equations of lateral attenuation for aircraft noise prediction modeling.",
}