| 基于多模式集成冬半年气温预报偏差修正 |
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| 引用本文： | 齐铎,刘松涛,赵广娜,高梦竹.基于多模式集成冬半年气温预报偏差修正[J].气象与环境学报,2022,38(3):119-126. |
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| 作者姓名： | 齐铎 刘松涛 赵广娜 高梦竹 |
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| 作者单位： | 黑龙江省气象台, 黑龙江 哈尔滨 150030 |
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| 基金项目： | 黑龙江省气象局院士工作站(重点)项目(YSZD201901);黑龙江省气象局院士工作站(重点)项目(YSZD202001);黑龙江省气象局竞争性科技攻关项目(HQGG202101);黑龙江省气象局智能网格预报及数值模式释用创新团队 |
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| 摘 要： | 卡尔曼滤波递减平均方法对模式直接输出的气温预报进行订正,能有效提高预报准确率,但有时会造成显著负订正的现象,使订正预报效果反而不及模式直接输出。利用消除偏差集合平均方法(BREM)选择最优滑动训练期对2019年10月至2020年4月ECMWF预报(EC)、经过卡尔曼滤波递减平均法订正的预报(EC_COR)及中央台网格指导预报(SCMOC)等3种气温预报在黑龙江省的结果进行集成,并将BREM方法对EC_COR的修正效果进行评估,结果表明:不同预报结果都表现为冬季和夜间预报的准确率更低,气温偏低的11月至翌年1月更倾向于表现出预报较实况系统性偏高的特点。BREM方法能有效地修正EC_COR对EC负订正的现象,且可显著高于任何一种参与集成的单一预报效果。可在对单一模式进行卡尔曼滤波递减平均订正的基础上,进一步提升预报质量。另外,利用集成方法对高质量预报产品的融合(不局限于模式直接输出预报或是订正预报)可获取较单一预报更优的预报结果。
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| 关 键 词： | 多模式集成 卡尔曼滤波递减平均方法 地面气温 偏差订正 |
| 收稿时间： | 2021-07-22 |
Predictive temperature deviation correction in winter half year based on multimodal integration |
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| Duo QI,Song-tao LIU,Guang-na ZHAO,Meng-zhu GAO.Predictive temperature deviation correction in winter half year based on multimodal integration[J].Journal of Meteorology and Environment,2022,38(3):119-126. |
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| Authors: | Duo QI Song-tao LIU Guang-na ZHAO Meng-zhu GAO |
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| Institution: | Heilongjiang Provincial Meteorological Observatory, Harbin 150030, China |
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| Abstract: | The corrections using Kalman filter decreases average method can effectively improve the prediction accuracies of modal predictive temperature, however, they can also cause significant negative corrections which make the results inferior to the original model outputs.Based on the bias removed ensemble mean (BREM), October of 2019 to April of 2020 in the optimal sliding training period is chosen to make integration predictions using the results of ECMWF (EC), corrections by Kalman filter decreases average method (EC_COR), and the data of national meteorological center forecast (SCMOC), respectively.The corrected results of BREM on EC_COR predictions are evaluated as well.It is shown that the accuracies in various predictions all appear worse in winter and at night, with a systematic higher temperature deviation from November to the next January.BREM can effectively prevent the negative corrections of EC_COR on EC with better effects than those of any other single method before integration, significantly improving the predictions.In addition, the integration of high-quality predictive productions, which is not limited to the model output predictions or forecast corrections, can obtain better results than a single forecast. |
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| Keywords: | Multimodal integration Kalman filtering decrement average method Surface temperature Deviation correction |
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