| ECMWF高分辨率模式对陕西2017年7月高温预报的检验及订正 |
| |
| 引用本文： | 王丹,王建鹏,娄盼星,戴昌明.ECMWF高分辨率模式对陕西2017年7月高温预报的检验及订正[J].干旱区地理,2019,42(1):38-46. |
| |
| 作者姓名： | 王丹 王建鹏 娄盼星 戴昌明 |
| |
| 作者单位： | 陕西省气象服务中心,陕西西安,710014;陕西省气象台,陕西西安,710014 |
| |
| 基金项目： | 陕西省气象局2016年青年科研基金项目（2016Y-5）；中国气象局2018年预报员专项项目（CMAYBY2018-074）；中国气象局2018年气象预报业务关键技术发展专项（YBGJXM（2018）03-13）；陕西省气象局精细化气象格点预报攻关团队共同资助 |
| |
| 摘 要： | 2017年7月陕西累计出现26 d日最高气温≥35℃的高温天气，其中14 d日最高气温突破40℃，7月7~14日和17~27日出现2次区域性持续高温天气。利用陕西99个国家站的最高气温逐时观测和ECMWF高分辨率模式的定时最高气温预报资料，检验ECMWF高分辨率模式对2017年7月陕西极端高温天气的预报能力，以及一元线性回归方法对气温预报的订正能力。结果表明，144 h之前模式较好地预报出了陕西2次区域性持续高温天气，但是高温日数的预报值在陕西大部分地区较观测值偏少，漏报了陕南大部分地区的高温日，与14:00~17:00时段最高气温的预报值在陕西大部分地区较观测值偏低，其中陕南地区的预报平均绝对误差明显大于其它地区有关。一元线性回归方法对168 h之前的最高气温预报为正订正效果，订正后陕西大部分地区最高气温的预报准确率上升，平均绝对误差减小，日最高气温≥35℃或≥40℃的高温预报较订正前更接近实况。
|
| 关 键 词： | 高温天气 ECMWF高分辨率模式 检验与订正 陕西 |
Evaluation and correction of high temperature weather forecast in Shaanxi Province in July 2017 using ECMWF high-resolution model |
| |
| WANG Dan,WANG Jian-peng,LOU Pan-xing,DAI Chan-gming.Evaluation and correction of high temperature weather forecast in Shaanxi Province in July 2017 using ECMWF high-resolution model[J].Arid Land Geography,2019,42(1):38-46. |
| |
| Authors: | WANG Dan WANG Jian-peng LOU Pan-xing DAI Chan-gming |
| |
| Affiliation: | 1.Shaanxi Meteorological Service Center, Xi’an 710014 Shaanxi,China; 2.Shaanxi Meteorological Observatory, Xi’an 710014，Shaanxi,China |
| |
| Abstract: | There were 26 days of high temperature when the daily maximum air temperature (MAT) was above 35 ℃ in Shaanxi Province in July 2017, and out of the 26 days there were 14 days with the MAT being even above 40 ℃. The continuous hot weather in this region in the month occurred twice during the periods from July 7th to 14th and from July 17th to 27th respectively. Based on hourly observation of MAT at 99 stations in Shaanxi Province reported by CMA (Chinese Meteorological Administration) and fixed time forecast of ECMWF (European Center for Medium Weather Forecasting) high resolution model, the performance of ECMWF model for the hot weather forecast and temperature correction by linear regression method were evaluated. The evaluation methods included average error, average absolute error and accuracy with absolute error less than 1 ℃ and 2 ℃. The results showed an obvious regional difference in forecast ability of the model, whose accuracy of the MAT is higher in Guan Zhong and lower in Southern Shaanxi. The regional continuous hot weather could have been better forecasted by the model during the forecast time of 144 hours, but the number of hot weather days predicted was less than the observation days in most areas of Shaanxi province, and it failed to forecast the hot weather days in most areas of southern Shaanxi. This was because that the values of MAT from prediction were less than the observation in most areas of Shaanxi, and the MAE (mean absolute error) in southern Shaanxi was more than that in other areas of Shaanxi from 14:00 to 17:00 BST. With the extension of forecast hours, the capability of the linear regression method to correct air temperature was decreased in wave mode. The linear regression method had positive effect to correct air temperature during the forecast time of 168 hours, which is better between 14:00 PM and 01:00 AM the next day than that in any other times, and there was a significant negative correlation relationship between the correction ability of the method and forecast ability of the model. The linear regression method could increase the accuracy and decrease the MAE of MAT. The ME (mean error) of MAT was closer to zero, and the hot weather forecast with a daily MAT being above 35 ℃ or 40 ℃ was closer to the observation after correction. The research provides a rational reference for hot weather forecasts from ECMWF model in summer, and it is noted further studies during other time periods are needed to verify the universality of the conclusion as this study was focused on the period of July 2017. |
| |
| Keywords: | |
| 本文献已被 万方数据 等数据库收录！ |
| 点击此处可从《干旱区地理》浏览原始摘要信息 |
|
点击此处可从《干旱区地理》下载全文 |
|
