LU Huiguo,ZHANG Jie,JIANG Juanping,et al.The Research of Observation Data Correction based on Convex Optimization Theory[J].Journal of Chengdu University of Information Technology,2021,36(04):404-412.[doi:10.16836/j.cnki.jcuit.2021.04.010]
四川盆地极端降水演变特征及拟合
- Title:
- The Research of Observation Data Correction based on Convex Optimization Theory
- 文章编号:
- 2096-1618(2021)04-0404-09
- Keywords:
- meteorology; applied meteorology; Sichuan basin; extreme precipitation; spatio-temporal evolution; generalized extreme value model; GEV model
- 分类号:
- P426.6
- 文献标志码:
- A
- 摘要:
- 针对四川盆地容易出现极端降水事件的问题,利用四川和重庆共103个气象台站1985-2014年的逐日降水资料对四川盆地极端降水的时空演变特征进行分析,同时采用最大似然估计法估计广义极值模型参数,建立广义极值模型拟合四川盆地极值降水,结果表明对于平稳的极值序列,平稳的广义极值模型有较好的拟合效果; 对于非平稳的极值序列,与平稳GEV模型相比,用模型参数与时间建立函数关系构建的非平稳GEV模型可以提高对极值降水的拟合效果。
- Abstract:
- Aiming at the problem of extreme precipitation events in the Sichuan Basin, the daily precipitation data of 103 meteorological stations in Sichuan and Chongqing from 1985 to 2014 were used to analyze the temporal and spatial evolution characteristics of extreme precipitation in the Sichuan Basin. At the same time, the maximum likelihood estimation method was used to estimate the generalized extremum model parameters, establish a generalized extremum model to fit the extreme precipitation in the Sichuan Basin. The results show that for stationary extreme value series, the stationary generalized extreme value model has a good fitting effect; for non-stationary extreme value series,compared with the stationary GEV model, the non-stationary GEV model constructed by establishing a functional relationship between model parameters and time can improve the fitting effect of extreme precipitation.
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备注/Memo
收稿日期:2020-07-05
基金项目:四川省科技厅重点研发资助项目(2019YFG0126)