| 基于INCA和METRo的江苏省路面高温精细化预报 |
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| 引用本文： | 冯蕾,王晓峰,何晓凤,郜婧婧.基于INCA和METRo的江苏省路面高温精细化预报[J].应用气象学报,2017,28(1):109-118. |
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| 作者姓名： | 冯蕾 王晓峰 何晓凤 郜婧婧 |
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| 作者单位： | 中国气象局公共气象服务中心, 北京 100081 |
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| 基金项目： | 资助项目： 中国气象局2016年山洪项目“公路交通气象预报试点建设”，公益性行业(气象)科研专项（GYHY201306043，GYHY201406029，GYHY201306058），国家自然科学基金项目（41205045） |
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| 摘 要： | 使用INCA（Integrated Nowcasting through Comprehensive Analysis）多源资料融合分析和短临外推预报系统的预报结果作为气象强迫场，驱动一路面温度理论预报模型（Model of the Environment and Temperature of Roads，METRo），开展江苏省高速公路夏季路面高温预报试验，并使用公路沿线逐小时的路面温度观测资料对预报结果进行检验。结果表明:该预报方法能够较好地预报出高速公路沿线日最高路面温度的逐日变化趋势，以及日最高路面温度的大范围空间分布特征。平均日最高路面温度预报绝对偏差为4.1℃，平均相对偏差为10.8%。其中，日最高路面温度预报绝对偏差在5℃以内的站次占总数的64.5%，相对偏差在15%以内的站次占总数的74.6%，比常规业务预报方法分别提高了23.1%和25.3%。但该预报方法对较小的温度波动以及局地性较强的极端温度分布特征的预报技巧还需进一步提高。
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| 关 键 词： | 高速公路 路面温度 精细化预报试验 |
| 收稿时间： | 2016/6/6 0:00:00 |
| 修稿时间： | 2016/12/1 0:00:00 |
Fine Forecast of High Road Temperature Along Jiangsu Highways Based on INCA System and METRo Model |
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| Feng Lei,Wang Xiaofeng,He Xiaofeng and Gao Jingjing.Fine Forecast of High Road Temperature Along Jiangsu Highways Based on INCA System and METRo Model[J].Quarterly Journal of Applied Meteorology,2017,28(1):109-118. |
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| Authors: | Feng Lei Wang Xiaofeng He Xiaofeng and Gao Jingjing |
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| Institution: | Public Meteorological Service Center of CMA, Beijing 100081 |
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| Abstract: | The Integrated Nowcasting through Comprehensive Analysis (INCA) system is an observation-based analysis and forecasting system, in which measurements from automatic weather stations, radar data, satellite data, 3D fields from the operational numerical weather prediction (NWP) model and elevation data with high-resolution are incorporated. INCA system adds value to the classical NWP forecast by providing high-resolution analyses, nowcasts and improved forecasts both within and beyond the nowcasting range. A coupling of INCA system and the Environment and Temperature of Roads (METRo) Model is used to study the forecast of high road temperature during summer along highways in Jiangsu Province.Result shows that the highest road temperature forecasting is improved significantly through the coupling of the forecast with observations during the overlap period by adjusting the radiative fluxes to local conditions. The absolute error of the road temperature at 1400 BT is reduced by about 3℃ compared with results with non-coupling. The daily variation and the spatial distribution of the highest road temperature at 1400 BT can be captured by this forecasting system. The average absolute error for the daily highest road temperature is 4.1℃, and the average relative error is about 10.8%. Percentages of stations with absolute error below 5℃ is about 64.5%, and percentages of stations with relative error below 15% is about 74.6%, which is higher than that of the conventional method (a statistic road temperature forecast model driven by atmospheric forecasting from BJ-RUC) by about 23.1%, 25.3%. Analysis for the hot day shows that the level and of the highest road temperature and the location of the highest road temperature center can be reproduced well by this system. The level of the highest road temperature on more than 90% of stations is in accordance with the observation. The absolute error of the road temperature forecasting is below 5℃ for most stations. While the road temperature forecasting for the hottest points are lower than the observation. The distribution of the forecasting error of the road temperature at 1400 BT shows scattered features and there is no obvious positive (negative) error center. Besides, the forecast skill of small fluctuations of road temperature needs to be improved.The source of the road temperature forecasting errors are from both the road temperature forecast model and the numerical weather prediction products. The METRo model is also driven by the station weather observation to test which part is the main error. It estimates that about 70% of error is from the model itself, and notes that the cloud amount and surface pressure observations are from nearby weather stations. |
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| Keywords: | highway road temperature fine forecast |
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