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基于卡尔曼滤波递减平均算法的集合预报综合偏差订正
引用本文:马旭林,时洋,和杰,计燕霞,WANG Yong.基于卡尔曼滤波递减平均算法的集合预报综合偏差订正[J].Acta Meteorologica Sinica,2015,73(5):952-964.
作者姓名:马旭林  时洋  和杰  计燕霞  WANG Yong
作者单位:南京信息工程大学气象灾害教育部重点实验室(KLME), 南京, 210044,南京信息工程大学气象灾害教育部重点实验室(KLME), 南京, 210044,南京信息工程大学气象灾害教育部重点实验室(KLME), 南京, 210044,南京信息工程大学气象灾害教育部重点实验室(KLME), 南京, 210044,气象与地球动力学中央研究院预测模型所, 维也纳, 奥地利
基金项目:国家自然科学基金项目(41275111)、重大研究计划培育项目(91437113)、公益性行业(气象)科研专项(GYHY201506005)、江苏高校优势学科建设工程资助项目(PAPD)。
摘    要:针对集合预报存在的偏差和集合离散度通常偏小的问题,在卡尔曼滤波递减平均的一阶矩和二阶矩偏差订正方案的基础上发展了综合偏差订正方案,并利用B08RDP WWRP(The WWRP Beijing 2008 Olympics Research and Development Project)项目中日本气象厅(JMA)区域集合预报的850 hPa温度资料,将敏感性试验得到的一阶矩和二阶矩订正的最优权重系数应用于综合偏差订正方案,并对其订正效果进行多方面检验分析。试验结果表明,一阶矩订正可以有效减小集合平均偏差,集合平均预报质量得到了明显改善;二阶矩订正对集合离散度具有较强的调整能力,订正后的集合预报可靠性、区分不同天气事件的能力总体上得到了提高;综合偏差订正方案有效融合了一阶矩和二阶矩订正的优势,其各自的最优权重系数适用于综合偏差订正方案,对集合平均偏差和离散度具有良好的订正效果,能够改善集合预报的整体质量。但一阶矩与二阶矩订正对综合偏差订正的贡献程度随评分指标而异,一阶矩订正对等级概率(RPS)评分和异常值百分比评分的贡献分别为83.75%和18.83%,可信度的改善约83.98%源于二阶矩订正,而相对作用特征(ROC)评分中二者的贡献基本相当。

关 键 词:数值天气预报  集合预报  综合偏差订正  卡尔曼滤波
收稿时间:2014/11/13 0:00:00
修稿时间:2015/4/24 0:00:00

The combined descending averaging bias correction based on the Kalman filter for ensemble forecast
MA Xulin,SHI Yang,HE Jie,JI Yanxia and WANG Yong.The combined descending averaging bias correction based on the Kalman filter for ensemble forecast[J].Acta Meteorologica Sinica,2015,73(5):952-964.
Authors:MA Xulin  SHI Yang  HE Jie  JI Yanxia and WANG Yong
Institution:Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), NUIST, Nanjing 210044, China,Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), NUIST, Nanjing 210044, China,Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), NUIST, Nanjing 210044, China,Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), NUIST, Nanjing 210044, China and Department of Forecasting Models, Central Institute for Meteorology and Geodynamics, Vienna, Austria
Abstract:To aim at the problem with both bias and small spread in ensemble forecast, a combined descending averaging bias correction method is designed based on the original Kalman filter. By using 850 hPa temperature from the regional ensemble dataset of JMA in the WWRP Beijing Olympics Research and Development Project, the optimal weights of the first and second moment are obtained by the weight sensitivity experiments and applied in the combined bias correction. Then, impacts of the combined bias correction are evaluated. The results show that the first moment bias correction largely reduces the bias in ensemble mean, so the forecast quality of ensemble mean is greatly improved. The second moment bias correction has good ability to adjust the spread to close the RMSE of the ensemble mean, improving the reliability and resolution of ensemble forecasts. To this end, a new descending averaging bias correction method is developed to combine the first moment with the second moment bias correction whose respective optimal weights can be applied to the combined bias correction, so as to improve the overall quality of ensemble forecast. However, the contributions of the first and second moment bias correction to the combined bias correction varies in terms of scores. For RPS and Outliers scores, the contributions of the first moment bias correction are 83.75% and 18.83%, respectively. The 83.98% improvement is from the second moment bias correction in terms of reliability, and the contributions of both moments are largely equal for ROC.
Keywords:Numerical weather forecast  Ensemble forecast  Combined bias correction  Kalman filter
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