| 天津港秋冬季低能见度数值释用预报研究 |
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| 引用本文： | 吴彬贵,张建春,李英华,王亚男,徐梅,陈靖,王雪莲,郭晓军,邱晓滨.天津港秋冬季低能见度数值释用预报研究[J].气象,2017,43(7):863-871. |
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| 作者姓名： | 吴彬贵 张建春 李英华 王亚男 徐梅 陈靖 王雪莲 郭晓军 邱晓滨 |
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| 作者单位： | 天津市气象科学研究所，天津 300074,中国民用航空华北地区空中交通管理局，天津 300074,天津市气象科学研究所，天津 300074,天津市气象科学研究所，天津 300074,天津市气象科学研究所，天津 300074,天津市气象科学研究所，天津 300074,天津市气象科学研究所，天津 300074,天津市气象科学研究所，天津 300074,天津市气象科学研究所，天津 300074 |
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| 基金项目： | 国家自然科学基金项目(41675018和41075004)、天津市自然科学基金项目(17JCYBJC23400)、天津市海洋局科技兴海项目(KJXH2012 25)以及天津市气象局项目(BSJJ201504)共同资助 |
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| 摘 要： | 本文利用近5年(2009—2013年)天津港资料,分析了该地区大气能见度的分级特征。采用7年秋、冬季NCEP(2006—2012年)和地面资料,通过相关分析给出了对港口低能见度天气有高影响的高、低空物理量因子;排除沙尘和降水天气,针对不同区间的能见度样本,利用BP神经网络方法分类训练了3个统计模型;并与WRF天气模式产品对接,采用分步筛选法,研发了天津港秋、冬季72 h时效的逐时能见度BP释用预报产品。经过3年业务运行,检验结果表明:对逐时能见度而言,BP释用预报对10 km以下低能见度比WRF模式的预报技巧显著提高,达到10.5%~35.4%;其中对0.5 km大雾的预报技巧总体相当,但当WRF预报有降水时,WRF模式预报结果略优;对0.5~1 km的大雾预报,WRF模式的预报技巧1%,BP释用预报提高到了14%~21%。日最低能见度的检验表明:对小于1 km的大雾过程,BP释用预报的TS评分平均达到75%,比WRF预报技巧提高了24%;对1~10 km的低能见度过程,比WRF的预报技巧平均提高了60%。
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| 关 键 词： | 雾，低能见度，人工神经网络，数值释用，分步筛选法，天津港 |
| 收稿时间： | 2015/12/29 0:00:00 |
| 修稿时间： | 2017/4/26 0:00:00 |
Research on Numerical Interpretative Forecast for Low Visibility at Tianjin Port in Autumn and Winter |
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| WU Bingui,ZHANG Jianchun,LI Yinghu,WANG Yanan,XU Mei,CHEN Jing,WANG Xuelian,GUO Xiaojun and QIU Xiaobin.Research on Numerical Interpretative Forecast for Low Visibility at Tianjin Port in Autumn and Winter[J].Meteorological Monthly,2017,43(7):863-871. |
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| Authors: | WU Bingui ZHANG Jianchun LI Yinghu WANG Yanan XU Mei CHEN Jing WANG Xuelian GUO Xiaojun and QIU Xiaobin |
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| Institution: | Tianjin Institute of Meteorological Sciences, Tianjin 300074,Air Traffic Management Bureau in North China of CAAC, Tianjin 300074,Tianjin Institute of Meteorological Sciences, Tianjin 300074,Tianjin Institute of Meteorological Sciences, Tianjin 300074,Tianjin Institute of Meteorological Sciences, Tianjin 300074,Tianjin Institute of Meteorological Sciences, Tianjin 300074,Tianjin Institute of Meteorological Sciences, Tianjin 300074,Tianjin Institute of Meteorological Sciences, Tianjin 300074 and Tianjin Institute of Meteorological Sciences, Tianjin 300074 |
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| Abstract: | The visibility features are analyzed using automatic hourly visibility observations at Tianjin Port from 2009 to 2013. Based on the NCEP reanalysis data and Tianjin Port observations in the autumn and winter from 2006 to 2012, higher impact meteorological factors are given wich influence low visibility at port area through the correlation analysis. Three statistical models to different visibility samples have been established with artificial neurological network method, excluding dust and rain weather. Moreover, the statistical models are linked to output products in WRF and are applied in operational forecasting on visibility at Tianjin Port in autumn and winter through progressive screening method according to the three statistical models. So visibility forecast products of 72 h period are provided every day. The test results show that, for the hourly visibility forecast, the forecasting techniques score (TS) of BPTFP (back propagation three filter product) for less than 10 km visibility is from 10.5% to 35.4%, higher than the WRF. The TS is a comparable level for visibility less than 0.5 km, but WRF model forecast is a little better when it forecasts precipitation. For forecasting 0.5-1 km fog, the TS of WRF is less than 1%, and the BPTFP is from 14% to 21%. The average tests of daily minimum visibility show that the TS of BPTFP is 75%, which is 24% higher than WRF for the fog process of less than 1 km. Moreover, for the fog process from 1 to 10 km, the TS of BPTFP is 60% higher than WRF. |
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| Keywords: | fog low visibility ANN (artificial neurological network) numerical interpretative forecast progressive screening method Tianjin Port |
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