| 北京地区温度要素模式预报和客观方法检验评估 |
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| 引用本文： | 赵桂洁,何娜,郝翠,李靖,李桑.北京地区温度要素模式预报和客观方法检验评估[J].气象科技,2021,49(6):869-877. |
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| 作者姓名： | 赵桂洁 何娜 郝翠 李靖 李桑 |
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| 作者单位： | 北京市气象台，北京 100089 |
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| 基金项目： | 冬奥赛场定点气象要素客观预报及风险预警技术研究及应用（2018YFF0300104）、国家重点研发计划（2018YFF0300104）、气象预报业务关键技术发展专项（YBGJXM（2018）03）、基于机器学习的冬奥精细天气预报技术研发及示范应用（Z201100005820002）、北京市气象局科技项目（BMBKJ201703001）资助 |
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| 摘 要： | 利用2018年10月1日至2019年9月30日北京地区55个地面气象站的实况观测数据对欧洲中期天气预报中心的全球预报(ECMWF-thin)、国家气象中心区域预报(Grapes)、北京睿图(RMAPS)、国家级指导预报(SCMOC)、北京智能网格温度客观预报(BJTM)和集合相似预报(AnEn)的逐日最高、最低气温预报结果进行检验评估。结果表明:(1)ECMWF-thin模式预报效果优于Grapes和RMAPS,客观方法BJTM和AnEn对ECMWF-thin的改进效果明显。(2)AnEn在10月至次年4月预报效果好,BJTM在5—9月预报效果好;不同预报时效中,AnEn在短期、中期前段预报效果较好,BJTM在中期5~9 d预报效果相对较好。(3)以南郊观象台为代表站进行检验,结果显示模式预报均存在明显的系统偏差,客观方法对系统偏差有很好的订正效果。(4)在降水、大风或无天气系统时,BJTM、AnEn的日最高温度预报准确率较高;雾霾天气背景下,ECMWF-thin的最高温度预报准确率较高。雾霾、大风和无天气系统时,ECMWF-thin最低温度预报偏差最小,客观方法对模式预报无改进;降水天气背景下,RMAPS和BJTM对最低温度的预报偏差最小。
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| 关 键 词： | 温度预报 检验评估 客观方法 季节性 天气背景 |
| 收稿时间： | 2020/9/25 0:00:00 |
| 修稿时间： | 2021/8/25 0:00:00 |
Evaluation of Models and Objective Methods for Temperature in Beijing Area |
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| ZHAO Guijie,HE N,HAO Cui,LI Jing,LI Sang.Evaluation of Models and Objective Methods for Temperature in Beijing Area[J].Meteorological Science and Technology,2021,49(6):869-877. |
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| Authors: | ZHAO Guijie HE N HAO Cui LI Jing LI Sang |
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| Institution: | Beijing Weather Forecast Center, Beijing 100089 |
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| Abstract: | Compared with the observations from 55 Beijing auto weather stations from 1 October 2018 to 30 September 2019, the European Center Medium Range Weather Forecasts Fine Grid Model (ECMWF thin), Regional Assimilation and Prediction System (Grapes), Rapid refresh Multi scale Analysis and Prediction System - Short Term (RMAPS ST), Central Station Guided Forecast (SCMOC), Beijing Intelligent Grid Temperature Objective Prediction Method (BJTM) and Analog Ensemble method (AnEn) which mainly focus on the daily maximum and minimum temperature in the Beijing area are evaluated. (1) In total, the results show that ECMWF thin model performance was better than Grapes and RMAPS; Two objective methods, BJTM and AnEn, had apparent improvement effects on ECMWF thin. (2) AnEn performed well from October 2018 to April 2019, and BJTM performed well from May to September 2019. Regarding different forecast timeliness, AnEn performed well in the short term and the first part of medium term, BJTM performed well in 5 to 9 days in medium term. (3) Focusing on the Guanxiangtai station, the systematic deviation was evident in all three models. Objective methods reduced the systematic deviation of models. (4) Under the background of precipitation, wind and no obvious weather, the two objective methods BJTM and AnEn had significantly improved the forecast quality of the ECMWF thin model for daily maximum temperature. However, when haze weather happened, the forecast accuracy of ECMWF thin was significantly higher than other models and methods. For the minimum daily temperature, except for precipitation weather background, the ECMWF thin model had the smallest deviation, and objective methods slightly improved the model results. Moreover, the RMAPS results showed better performance when precipitation occurred, and objective methods reduced the systematic deviation of the large scale model. |
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| Keywords: | temperature forecast model evaluation objective method seasonality weather background |
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