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降水量Γ分布模式的普适性研究
引用本文:杨瑞雯, 赵琳娜, 巩远发, 李潇濛, 曹越. 2017: 中国东南地区降水的两种集合预报综合偏差订正对比分析. 暴雨灾害, 36(6): 507-517. DOI: 10.3969/j.issn.1004-9045.2017.06.003
作者姓名:杨瑞雯  赵琳娜  巩远发  李潇濛  曹越
作者单位:1.成都信息工程大学大气科学学院,高原大气与环境四川省重点实验室,成都 610225;2.中国气象科学研究院灾害天气国家重点实验室,北京 100081
基金项目:国家自然科学基金项目(41475044,91437104);国家重点基础研究发展计划(2015CB452806);科技支撑项目(2015BAK10B03);国家科技重大专项(2013ZX07304-001-1)
摘    要:

利用2015年6月1日—7月31日三个全球数值预报业务中心(CMA、ECMWF和NCEP)的24 h降水集合预报资料和我国东南地区降水观测资料,采用贝叶斯模型平均方法(A方案)和基于A方案的统计降尺度模型二次订正方法(B方案)对上述三个中心和多模式超级集合降水预报进行订正,并对比两种方案的订正效果;然后,选取2015年8月1—31日降水预报进行独立样本检验,分析订正前后的降水预报效果。结果表明:以第50百分位的降水预报为例,经A方案订正后各中心和多模式的集合平均消除了大量的小雨空报,其对小雨、中雨的订正效果很明显,对大雨以上的降水量级订正效果不明显。随着降水阈值增加,A方案的订正效果随之减弱。此方案对雨带走向的订正不明显,会使降水大值区量级降低甚至消失。采用B方案订正后,不仅可降低原始集合预报的空报率,还可对降水量级和落区进行订正,使降水预报的范围和量级与实况更接近,但对大量级降水,如50.0 mm以上的降水量级订正效果仍然不显著。



关 键 词:集合预报  降水预报  降尺度模型  贝叶斯模型平均  偏差订正
收稿时间:2017-08-21

Maximum Likelihood Estimation for the Gamma Distribution Using Data Containing Zeros
YANG Ruiwen, ZHAO Linna, GONG Yuanfa, LI Xiaomeng, CAO Yue. 2017: Comparative analysis of integrated bias correction to ensemble forecast of precipitation in southeast China. Torrential Rain and Disasters, 36(6): 507-517. DOI: 10.3969/j.issn.1004-9045.2017.06.003
Authors:YANG Ruiwen  ZHAO Linna  GONG Yuanfa  LI Xiaomeng  CAO Yue
Affiliation:1.Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225;2.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081
Abstract:Based on the 24 h precipitation ensemble forecasts from three global numerical forecast centers (CMA, ECMWF and NCEP) and surface precipitation observations in southeast China from 1 June to 31 July 2015 and using the Bayesian Model Averaging (scheme A) and combined Bayesian Model Averaging and statistically downscale (scheme B), we have corrected the three single-center and multicenter grand ensemble precipitation predictions, compared the adjustment effects of the two schemes, and then selected the precipitation forecasts from August 1 to 31 in 2015 to perform an independent sample test, and analyzed the skills of precipitation prediction before and after the correction. Taking 50th percentile precipitation forecast of three single center and grand ensemble as example, the results indicate that scheme A eliminates a large number of the false alarms of light rain and corrects the bias for light and moderate rains remarkably. But the correction of the precipitation intensity for those exceeding heavy rain is not evident. With the precipitation thresholds increasing, the correction results of scheme A becomes weaker. The correction to the orientation of rain belt is not clear, and reduces the magnitude of precipitation area of heavy rain or even makes the area disappear. After adopting scheme B adjustment, it not only reduces the false alarm of raw ensemble forecasts, but also corrects the precipitation intensity and the rainfall area, so that the range and magnitude of the precipitation forecasts are closer to observations. But its effect is still not significant to correct high-graded precipitation such as those exceeding 50.0 mm.
Keywords:ensemble forecast  precipitation prediction  down-scale model  Bayesian Model Averaging  bias correction
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