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利用中尺度模型对日本淀川流域及其周边地区进行雾模拟。

Fog simulation using a mesoscale model in and around the Yodo River Basin, Japan.

作者信息

Hikari Shimadera, Kundan Lal Shrestha, Akira Kondo, Akikazu Kaga, Yoshio Inoue

机构信息

Graduate School of Engineering, Osaka University, Yamada-oka 2-1, Suita, Osaka 565-0871, Japan.

出版信息

J Environ Sci (China). 2008;20(7):838-45. doi: 10.1016/s1001-0742(08)62135-x.

Abstract

In this study, fog simulations were conducted using the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) in and around the Yodo River Basin, Japan. The purpose is to investigate the MM5 performance of fog simulation for long-term periods. The simulations were performed for January, February, March, and July, 2005 with a coarse 3-km and a nested fine 1-km grid domains. Results of the simulations were compared with data from ten meteorological observatories, fog sampling site in Mt. Rokko, and visibility measurement sites along the Second Meishin Expressway. At the meteorological observatories, the MM5 predictions agreed well with the observed temperature and specific humidity. In the Mt. Rokko region, MM5 generally reproduced the occurrence of relatively thick fog events but tended to overestimate liquid water content (LWC) of fog (by factors of 2.2-3.3 in terms of monthly mean LWC). In the Second Meishin Expressway region, while MM5 identified the specific sites at which fog either frequently or seldom occurs, the model underestimated the monthly fog frequencies by factors of more than 1.5. Overall, MM5 reproduced the general trend of fog events, and the model performance may be improved by using more adequate land surface data and suitable physics options for our study.

摘要

在本研究中,利用美国国家大气研究中心/宾夕法尼亚州立大学的第五代中尺度模式(MM5),对日本淀川河流域及其周边地区进行了雾模拟。目的是研究MM5对长期雾模拟的性能。模拟在2005年1月、2月、3月和7月进行,采用了3公里粗网格和1公里嵌套细网格区域。将模拟结果与来自十个气象观测站、六甲山雾采样点以及第二名神高速公路沿线能见度测量点的数据进行了比较。在气象观测站,MM5的预测与观测到的温度和比湿吻合良好。在六甲山地区,MM5总体上再现了相对浓雾事件的发生,但往往高估了雾的液态水含量(按月平均液态水含量计算,高估了2.2 - 3.3倍)。在第二名神高速公路地区,虽然MM5识别出了雾频繁或很少出现的特定地点,但该模型低估了月雾频率,低估因子超过1.5。总体而言,MM5再现了雾事件的总体趋势,通过使用更合适的地表数据和适合我们研究的物理选项,模型性能可能会得到改善。

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