Feng Lei, Wang Xiaofeng, He Xiaofeng, et al. Fine forecast of high road temperature along Jiangsu highways based on INCA system and METRo model. J Appl Meteor Sci, 2017, 28(1): 109-118. DOI:  10.11898/1001-7313.20170110.
Citation: Feng Lei, Wang Xiaofeng, He Xiaofeng, et al. Fine forecast of high road temperature along Jiangsu highways based on INCA system and METRo model. J Appl Meteor Sci, 2017, 28(1): 109-118. DOI:  10.11898/1001-7313.20170110.

Fine Forecast of High Road Temperature Along Jiangsu Highways Based on INCA System and METRo Model

DOI: 10.11898/1001-7313.20170110
  • Received Date: 2016-06-06
  • Rev Recd Date: 2016-01-13
  • Publish Date: 2017-01-31
  • 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.
  • Fig. 1  The forecast scheme of road temperature

    Fig. 2  The distribution of road weather observation stations

    Fig. 3  The absolute error of air temperature (a), relative humidity (b) and wind speed (c) in summer of 2014 for Jiangsu Province forecasted by BJ-RUC model and INCA system

    Fig. 4  The comparison of road temperature forecast and observation for Chengnan Station before and after coupling at 27 Jul (a) and 28 Jul (b) in 2014

    Fig. 5  The absolute error of road temperature forecast for Chengnan Station before and after coupling at 27 Jul (a) and 28 Jul (b) in 2014

    Fig. 6  The comparison of the daily maximum road temperature change forecast with observation from 21 Jul to 31 Jul in 2014 (a) Huangtang (south) Station, (b) Tongsansulushengjie Station

    Fig. 7  The spatial distribution of road temperature for 177 stations along highways in Jiangsu Province at 1400 BT 21 Jul and 1400 BT 22 Jul in 2014 (a) observation at 1400 BT 21 Jul, (b) forecast at 1400 BT 21 Jul, (c) observation at 1400 BT 22 Jul, (d) forecast at 1400 BT 22 Jul

    Fig. 8  The distribution of road temperature forecasting error for 177 stations along highways in Jiangsu Province at 1400 BT 21 Jul (a) and 1400 BT 22 Jul (b) in 2014

    Table  1  Statistics of error of road temperature forecast at 1400 BT for 177 stations along highways in Jiangsu Province from 21 Jul 2014 to 21 Aug 2014

    预报方案 绝对偏差 相对偏差
    1℃以内 3℃以内 5℃以内 5%以内 10%以内 15%以内
    WRF预报场驱动统计模型 7.5% 24.4% 41.4% 18.8% 35.6% 49.3%
    INCA预报场驱动METRo模型 14.7% 41.3% 64.5% 37.2% 61.2% 74.6%
    观测驱动METRo模型 17.2% 49.3% 78.1% 42.2% 76.7% 88.3%
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    • Received : 2016-06-06
    • Accepted : 2016-01-13
    • Published : 2017-01-31


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