| 综合多级相似预报技术在暴雨短期预报中的检验 |
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| 引用本文： | 李博,赵思雄,陆汉城,杨国祥.综合多级相似预报技术在暴雨短期预报中的检验[J].应用气象学报,2008,19(3):307-314. |
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| 作者姓名： | 李博 赵思雄 陆汉城 杨国祥 |
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| 作者单位： | 1.中国科学院研究生院, 北京 100049 |
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| 基金项目： | 国家重点基础研究发展计划(973计划) |
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| 摘 要： | 选取我国浙东南沿海特定预报区为研究范围, 对该地区不同型 (冷锋型、台风型、准静止锋型、气旋型、倒槽型等) 天气过程引发的暴雨进行研究, 开发出一种新的暴雨预报方法———综合多级相似技术。该技术采用减空法和极值剔除法对因子进行筛选, 采用多种气象要素和多种物理因子场综合, 进行各级相似试验和预报, 较前人普遍采用的单因子求相似离度和分级试验更进一步。提出相似判据在描述样本之间相似程度时随相似因子、相似区域的不同而有较大差异的思想, 并采取如下办法解决这个技术难题:在因子筛选过程中采用因子组合方法, 全面考虑各种优势因子及其组合, 对描述样本之间的相似程度发挥了较好的作用。此外, 通过双时次滚动预报减少漏报, 提高对灾害性天气的预警能力。每一种暴雨类型试验的CSI历史拟合值都在0.40以上, 最高值超过0.60, 试报CSI平均值为0.37, 试验结果说明该技术具有较强的平均预报能力。
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| 关 键 词： | 综合多级相似预报 相似离度 暴雨 |
| 收稿时间： | 2007-06-13 |
Test of the Synthetical Multilevel Analog Forecast Technology in Short term Rainstorm Prediction |
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| Li Bo,Zhao Sixiong,Lu Hancheng,Yang Guoxiang.Test of the Synthetical Multilevel Analog Forecast Technology in Short term Rainstorm Prediction[J].Quarterly Journal of Applied Meteorology,2008,19(3):307-314. |
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| Authors: | Li Bo Zhao Sixiong Lu Hancheng Yang Guoxiang |
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| Affiliation: | 1.Graduate University of Chinese Academy of Sciences, Beijing 1000492.Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 1000293.Chinese Huayin Ordnance Test Center, Huayin 7142004.Department of Atmospheric Sciences, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101 |
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| Abstract: | A new synthetical multilevel analog forecast technology (SMAT) is developed to make the analog forecast trial of different pattern rainstorm process in a selected region, including cold front pattern, typhoon pattern, quasi-stationary front pattern, cyclonic pattern, inverted trough pattern etc. The new forecast system's research process, the study results and its application are introduced. "Synthetical" represents the combination of various meteorological elements, combination of large scale weather condition and meso-scale weather condition, combination of static simulation and dynamical process simulation. Multilevel indicates three level forecast flows by which different aspects are described and are embodied in a harmonious body. The basic element field is used to reflect macro-atmospheric circumstance (large scale) similitude, local physical elements are used to reflect local climate trait (meso-scale) similitude, numerical model integral products are used to reflect dynamical process similitude. "The reducing FAR (vacant-forecast rate) " technology and extremum check method are included in SMAT, which are useful in selecting analog terms and optimizing forecast conditions' combinations. Multi-meteorology terms and physical conditions' combination are also included in SMAT, which are useful in each analog level trail. This is a step forward than the former single element analog. The science problem analog criterion alters a lot with different analog elements and ranges and it is pointed out, the following method is used to resolve this problem. Analog degree in a more general view can be described by evaluating various good elements and their combination samples. The key analog range is selected from some possible ranges. In the 3rd level analog process, assimilation numeric products are imported. Moreover, based on double-times rolling forecast, losing-forecast events can be decreased. This is good to improve forecast capability in disastrous weather. The following conclusions can be drawn. The historical testing CSI (forecast successful index) of each pattern is more than 0.4 (some are even more than 0.6), model testing average CSI is 0.37, this is better than other work in the same field. Better indexes can be gotten in the 3rd level forecast than double levels forecast. The selection and operation way of critical analog deviation suggest the idea of "false alarm better than miss hit". This leads to the high false alarm index. The 3rd level forecast based on the model products can be used in reducing the false alarm index. After revising the model continuously, model average forecast ability can be improved. Comparing with other current forecast methods (CSI is about 0.35), a revised model testing average CSI (0.392) is obtained. SMAT is also good at COR (forecast precise rate) and POD (miss hit forecast rate) index. Results show that successful forecast in various rainstorm process is achieved. SMAT model has a stronger forecast capability. |
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