| 基于重组降水集合预报的洪水概率预报 |
| |
| 引用本文： | 赵琳娜,刘莹,包红军,王彬雁,白雪梅,李潇濛,杨瑞雯,李依瞳.基于重组降水集合预报的洪水概率预报[J].应用气象学报,2017,28(5):544-554. |
| |
| 作者姓名： | 赵琳娜 刘莹 包红军 王彬雁 白雪梅 李潇濛 杨瑞雯 李依瞳 |
| |
| 作者单位： | 1.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），国家重点基础研究发展计划（2015CB452806），国家科技支撑项目（2015BAK10B03），国家科技重大专项（2013ZX07304-001-1） |
| |
| 摘 要： | 采用条件亚正态模型方法，生成了具有包含不同可能性的降水集合预报。为了保持各子流域降水集合预报变量之间的空间相关性，采用集合预报重组方法对降水集合预报进行重新排列。使用重组后的降水集合预报驱动水文模型，实现了淮河上游大坡岭-息县、淮河上游息县-王家坝和汝河-洪河上游3个子流域的12次洪水过程的洪水概率预报，并对1988年9月7日和1991年7月31日两次洪水概率预报进行个例分析。结果表明:相对于单一确定性预报，通过条件亚正态分布模型生成降水集合预报后，再经过Schaake洗牌法空间相关性重新组合的降水集合预报，捕捉洪峰出现时间和流量的能力更强。对洪水概率预报来说，降水概率预报更能达到对未来的水文事件进行最大可能估计的目的，并尽可能综合了降水预报不确定性因素，同时也说明维持变量原有的空间相关特征对于降水概率预报具有重要意义。
|
| 关 键 词： | Schaake洗牌法 条件亚正态分布 洪水概率预报 集合预报 概率定量降水预报 |
| 收稿时间： | 2017/5/17 0:00:00 |
| 修稿时间： | 2017/6/29 0:00:00 |
The Probabilistic Flood Prediction Based on Implementation of the Schaake Shuffle Method over the Huaihe Basin |
| |
| Zhao Linn,Liu Ying,Bao Hongjun,Wang Binyan,Bai Xuemei,Li Xiaomeng,Yang Ruiwen and Li Yitong.The Probabilistic Flood Prediction Based on Implementation of the Schaake Shuffle Method over the Huaihe Basin[J].Quarterly Journal of Applied Meteorology,2017,28(5):544-554. |
| |
| Authors: | Zhao Linn Liu Ying Bao Hongjun Wang Binyan Bai Xuemei Li Xiaomeng Yang Ruiwen and Li Yitong |
| |
| Affiliation: | State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;College of Atmospheric Sciences, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225,Sichuan Provincial Meteorological Observatory, Chengdu 610072,National Meteorological Center, Beijing 100081,Sichuan Provincial Meteorological Observatory, Chengdu 610072,Heilongjiang Provincial Meteorological Observatory, Harbin 150001,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;College of Atmospheric Sciences, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225,State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;College of Atmospheric Sciences, Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, Chengdu University of Information Technology, Chengdu 610225 and State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081;Jinlin Provincial Meteorological Observatory, Changchun 130062 |
| |
| Abstract: | Daily precipitation records of 19 rain gauges over the Huaihe Wangjiaba-Dapoling catchment and single-value forecasts of 24-hour cumulative precipitation of the Global Forecast System (GFS) with lead time up to 14 days from 1 January 1981 to 31 December 2003 are employed to construct a probability forecast model which can generate ensemble forecast based on conditional meta-Gaussian distribution. Several single-value forecasts could be computed by this model using forecasts of the GFS for daily mean areal precipitation (MAP) and cumulative MAP for each lead time (1-14 days) over 3 sub-catchments in the Huaihe Basin. Then a method is implemented to reorder the ensemble output to recover the space-time variability in precipitation, namely Schaake shuffle method. Ensembles are then reordered to match the original order of the selection of historical data. Using this approach, the observed inter sub-catchments correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology is applied in recovering the space-time variability in modeled streamflow for twelve flood processes over the Huaihe Basin. Results demonstrate that the observation of discharge is included in the interval between the 5th percentage and the 95th percentage forecasts of discharge that is generated by MAP ensemble forecasts which is calculated from the conditional meta-Gaussian distribution model and Schaake shuffle. Several members can capture the flood peak flow and the corresponding peak time. Using approach of Schaake Shuffle, sub-catchment correlations of each ensemble member forecasting could be recovered, which are closer to the observation.A test of flood forecasting result from precipitation probability forecasts of conditional meta-Gaussian distribution model and Schaake shuffle for the stream between Dapoling to Wangjiaba Hydrologic Station is carried out. It shows that MAP ensemble forecasts can provide the maximum estimation of possibility of the future hydrologic events for flood forecasting comparing to the single-value MAP forecast of GFS model. And a comprehensive interval which includes the factor that can lead to hydrologic uncertainty is also given. |
| |
| Keywords: | Schaake shuffle conditional meta-Gaussian distribution probabilistic flood forecasting ensemble prediction probabilistic quantitative precipitation forecast |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《应用气象学报》浏览原始摘要信息 |
|
点击此处可从《应用气象学报》下载全文 |
|
