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文章摘要
引用本文:董颜,尤焕玲,郭文利,闵晶晶.2017.基于BJ-RUC模式预报产品的北京冬半年道面温度预报模型.气象,43(10):1241-1248.
基于BJ-RUC模式预报产品的北京冬半年道面温度预报模型
Models of Road Surface Temperature in the Beijing Region in the Winter Half Year Based on the BJ RUC Forecast Product
投稿时间:2016-06-30  最后修改时间:2017-06-16
DOI:10.7519/j.issn.1000-0526.2017.10.008
中文关键词: 道面温度,北京快速更新循环数值预报系统(BJ RUC)预报,回归模型
英文关键词: road surface temperature, Beijing Rapid Update Cycle (BJ RUC) forecast, regression model
基金项目:北京市科技计划项目(Z151100002115040)和北京市自然科学基金项目(8174083)共同资助
作者单位
董颜 北京市气象服务中心北京 100089 
尤焕玲 北京市气象服务中心北京 100089 
郭文利 北京市气象服务中心北京 100089 
闵晶晶 北京市气象服务中心北京 100089 
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中文摘要:
      选用2012年11月1日至2013年3月30日3 km分辨率BJ RUC模式输出的气象要素与5个道面站数据(A1027,A1325,A1412,A1414,A1512)温度进行统计分析,按不同起报时次(08、14和05时)分别建立三类逐步回归统计模型预报未来24 h逐时道面温度,选出最优模型预报2013年11月至2014年3月道面温度。结果表明:道面温度与RUC输出的2 m温度、短波辐射显著相关,与长波辐射、湿度次相关;有显著气象因子参与的回归模型预报的道面温度好于仅加入前一天对应时刻道面温度的回归模型,预报准确度可提高25%以上,误差减少1℃以上;滚动筛选不同起报时次预报时段可将模型预报误差控制在±3℃以内,且预报早高峰温度好于晚高峰,白天好于夜间,晴天好于其他天气类型。
英文摘要:
      In this paper we made a statistical analysis of the road surface temperature based on observations of the selected five road stations (A1027, A1325, A1412, A1414, A1512) and the meteorological elements output from the Beijing Rapid Update Cycle (BJ RUC) numerical forecasting model with 3 km resolution from 1 November 2012 to 30 March 2013. We used the stepwise regression model methods to build three types of statistical models for hourly road surface temperature in 24 h in the winter half year for the different initial forecasting times (08:00,14:00, 05:00 BT) and the different months. Then the best type is used to forecast the road surface temperature from 1 November 2013 to 30 March 2014. The results are as follows. The road surface temperature is significantly correlated to T2 and the short wave radiation, but secondarily correlated to the long wave radiation and humidity output from RUC. Compared to the type of statistical model with the only one factor for the previous day, the type of regression model with meteorological elements of remarkable correlation inserted performs better in terms of the road surface temperature forecast accuracy by more than 25%, and the prediction error decreases by 1℃. For further enhancing the forecast accuracy rate, we selected the different initial times for verification so as to control error within ±3℃. The result of evaluation shows that the forecast value of the road surface temperature in the daytime is better than that over night, and sunny days are better than any other kinds of weather.
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