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引用本文:段春锋,徐敏,程智,罗连升.2017.DERF2.0模式对月尺度西太平洋副热带高压预测能力评估.气象,43(10):1267-1277.
DERF2.0模式对月尺度西太平洋副热带高压预测能力评估
Evaluation on Monthly Prediction of Western Pacific Subtropical High by DERF2.0 Model
投稿时间:2016-11-26  最后修改时间:2017-08-24
DOI:10.7519/j.issn.1000-0526.2017.10.011
中文关键词: 模式评估,西太平洋副热带高压,DERF2.0,预测性能
英文关键词: model evaluation, western Pacific subtropical high (WPSH), DERF2.0, prediction performance
基金项目:国家自然科学基金项目(41605068)、公益性行业(气象)科研专项(GYHY201406021)、安徽省气象局预报员专项(kY201703)和气象预报业务关键技术发展专项[YBGJXM(2017)05]共同资助
作者单位
段春锋 安徽省气候中心合肥 230031;安徽省气象科学研究所安徽省大气科学与卫星遥感重点实验室合肥 230031 
徐敏 安徽省气候中心合肥 230031 
程智 安徽省气候中心合肥 230031 
罗连升 安徽省气候中心合肥 230031 
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中文摘要:
      基于国家气候中心第二代月动力延伸预测模式业务系统(DERF2.0)1983—2015年回报数据,利用时间相关系数、标准化均方根误差、距平符号一致率和泰勒图分析等方法综合评估了DERF2.0模式对月尺度西太平洋副热带高压的预测性能。结果表明:模式能够预测出气候平均态上588 dagpm等值线的空间分布及强度和纬向风切变位置,但有偏大偏强的系统偏差。模式对高度场年际变率的预测性能好于纬向风场。模式较为准确地预测了副高气候态的年循环信息,但存在系统偏差,副高面积相对观测偏大,强度偏强,脊线偏北,西伸脊点偏西。模式对副高年际变率的预测性能较好,其中强度最佳,面积其次,脊线相对较差。副高预测性能依赖于超前起报时间,随着起报时间的临近,预测性能整体呈上升趋势,业务中及时更新预测。模式对副高面积超前0~20 d、强度超前0~2 d、西伸脊点超前0~5 d、脊线超前0~7 d起报对未来一个月的预测性能优于持续性预测,业务中可以重点参考。
英文摘要:
      Based on hindcast data of the National Climate Centre second generation monthly Dynamic Extended Range Forecast operational system (DERF2.0) from 1983 to 2015, prediction performance of the system for western Pacific subtropical high (WPSH) is evaluated by using time correlation coefficient, standardized root mean square error, anomaly sign consistency rate and Taylor diagrams. The results show that the model can predict spatial distribution of 588 dagpm contour lines and zonal wind shear position, but system bias is large and strong. The prediction performance for interannual variability of the 500 hPa height field is better than for zonal wind field. Climatological annual cycle information of WPSH are well represented, but there are obvious systematic deviations. The prediction is larger for WPSH area, stronger for intensity, by north for ridge line, and by west for west ridge point than the observation. The prediction performance for interannual variability of WPSH is good. The best is for intensity, secondly for area, and the poorest for ridge line. The prediction performance depends on the lead time. With time approaching, the prediction performance shows an increasing trend. Updating forecast is needed in time for rolling correction in operation. The performance of model WPSH prediction for area ahead of 0-20 days, for intensity ahead of 0-2 days, for western ridge point ahead of 0-5 days, for ridge line ahead of 0-7 days is better than persistence forecast. This can be used as a reference in forecasting operation.
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