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Statistical Downscaling of Urban-scale Air Temperatures Using an Analog Model Output Statistics Technique

机译:使用模拟模型输出统计技术的城市规模空气温度统计缩小

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摘要

This study was conducted to evaluate the suitability of an analog model output statistics (MOS) downscaling technique for urban-scale meteorology research and compares this MOS-Analog technique with the sliding window technique. We downscaled air temperatures forecasted for the Seoul metropolitan area from 1.5 km resolution (using data from the Unified Model-Local Data Assimilation and Prediction System, UM-LDAPS) to 25 m resolution using the analog MOS technique described in the paper. The support vector machine (SVM) technique was employed for empirical computational modeling, using urban surface parameters calculated using the Climate Analysis Seoul (CAS) workbench and automated weather station (AWS) observational data as training data. The comparison of the downscaled prediction results with the AWS observations for the periods of July/August 2016 and 2017 resulted in a lower root mean square error (RMSE) and higher correlation coefficients (CC) than those obtained for the LDAPS prediction results. The prediction performance was also stable for September, during which precipitation episodes and seasonal fluctuations occurred. The results of this study demonstrate that the proposed technique, which overcomes the limitations of the sliding window technique, is applicable to urban-scale meteorology research and potentially applicable other areas.
机译:进行该研究以评估模拟模型输出统计(MOS)城市规模气象研究缩减技术的适用性,这MOS-模拟技术与滑动窗技术进行比较。我们缩减的预测从1.5公里分辨率(使用从统一模型局部数据同化和预测系统,UM-LDAPS数据)到25μm的分辨率利用在论文中描述的模拟MOS技术首都圈空气温度。被用于经验计算建模的支持向量机(SVM)技术,使用利用气候分析尔(CAS)和工作台自动天气站(AWS)的观测数据作为训练数据来计算城市表面参数。按比例缩小的预测结果与七月的周期的AWS观察该比较/ 8 2016和2017导致了较低的根均方误差(RMSE)和比对LDAPS预测结果所获得的那些更高的相关系数(CC)。预测性能也稳定九月份,在此期间,沉淀发作和季节性波动发生。这项研究的结果表明,所提出的技术,它克服了滑动窗口技术的局限性,是适用于城市规模的气象研究和潜在应用等领域。

著录项

  • 作者

    Yire Shin; Chaeyeon Yi;

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  • 年度 2019
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  • 原文格式 PDF
  • 正文语种 eng
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