| 基于优化样本组合的集合-变分混合同化方案研究 |
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| 引用本文： | 陈耀登,郭闪,王元兵,臧增亮,潘晓滨.基于优化样本组合的集合-变分混合同化方案研究[J].热带气象学报,2020,36(4):464-476. |
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| 作者姓名： | 陈耀登 郭闪 王元兵 臧增亮 潘晓滨 |
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| 作者单位： | 1.南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心，江苏 南京 210044 |
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| 基金项目： | 国家重点研发计划项目2017YFC1502102国家自然科学基金项目41675102 |
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| 摘 要： | 为有效引入“流依赖”的背景场误差协方差，同时降低集合预报带来的计算量，尝试通过优选与同化时刻天气形势更相似的历史预报样本，并结合预报过程中的时间滞后样本，将两种样本引入集合-变分混合同化系统中，构建基于优选历史预报样本和时间滞后样本的集合-变分混合同化方案。单点观测理想试验表明，优选历史预报样本结合时间滞后样本，既能够缓解样本不足所导致的采样误差，又能够为同化系统提供“流依赖”的背景场误差协方差。连续一周的循环同化及预报试验结果显示，相较于ERA5资料和探空资料，三维变分方案整体表现稍差，样本组合混合同化方案分析场和预报场的均方根误差最小，且比仅用时间滞后样本的混合同化方案有所改进；降水评分整体也表现最优，尤其对中雨和暴雨的模拟改进较明显，较好地模拟出了强降水中心的强度和位置，且改善了降水过报的问题。
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| 关 键 词： | 数值天气预报 资料同化 混合同化 集合样本 流依赖 |
| 收稿时间： | 2019-12-04 |
STUDY ON ENSEMBLE-VARIATION HYBRID DATA ASSIMILATION BASED ON OPTIMIZED COMPOSITE SAMPLES |
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| CHEN Yao-deng,GUO Shan,WANG Yuan-bin,ZANG Zeng-liang,PAN Xiao-bing.STUDY ON ENSEMBLE-VARIATION HYBRID DATA ASSIMILATION BASED ON OPTIMIZED COMPOSITE SAMPLES[J].Journal of Tropical Meteorology,2020,36(4):464-476. |
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| Authors: | CHEN Yao-deng GUO Shan WANG Yuan-bin ZANG Zeng-liang PAN Xiao-bing |
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| Institution: | 1.Key Laboratory of Meteorological Disaster of Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China2.College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 211101, China |
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| Abstract: | In order to introduce flow-dependent background error covariance effectively and reduce the computing cost of ensemble forecast, the present research attempts to select historical forecasting samples similar to the weather condition at the time of assimilation and combine them with time-lagged samples in hybrid assimilation system. The single observation tests indicate that using the combination of selected historical samples and time-lagged samples, the sample error caused by limited samples is mitigated and flow-dependent background error covariance could be introduced in hybrid assimilation system. Compared with ERA5 and sound dataset, cycling assimilation and forecasts for a week show that 3DVAR has slightly poor performance. Hybrid experiment based on composite samples has the lowest root mean square error (RMSE), and performs better than hybrid experiments which only use the time-lagged samples. Its overall precipitation score is the best, and performs especially well in the assessment of moderate and heavy precipitation. The intensity and location of heavy precipitation center could be predicted well and the problem of over-estimating is alleviated in the composite sample hybrid assimilation scheme. |
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| Keywords: | numerical weather prediction data assimilation hybrid assimilation ensemble sample flow dependent |
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