文章摘要
张凯静1,2,汪萍3,戴新刚2,程智4.中国降水回归模型设计策略与回报检验[J].海洋气象学报,2017,37(3):27-35
中国降水回归模型设计策略与回报检验
The design strategy and hindcasting experiment of China precipitation using regression model
投稿时间:2017-05-30  修订日期:2017-07-13
DOI:10.19513/j.cnki.issn2096-3599.2017.03.004
中文关键词: 降水预测  回归方程  东亚季风  ENSO指数  NAO指数
英文关键词: precipitation prediction  regression〖BP(〗HYPERLINK"javascript:void(0)  "〖BP)〗 equation  East Asian monsoon  ENSO index  NAO index 〖
基金项目:国家自然科学基金项目 (41475075,41075058);国家重点研发计划项目(2016YFA0601901);青岛市气象局科研项目(2015qdqxh02)
作者单位
张凯静1,2,汪萍3,戴新刚2,程智4 1. 青岛市气象台 2. 中国科学院大气物理研究所东亚区域气候环境重点实验室 3. 中国气象科学研究院大气成分研究所 4. 93381部队分区气象台 
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中文摘要:
      利用中国160站降水记录及欧洲中心ERA-Interim再分析数据等构建了16种线性回归降水预测模型,包括“站点”降水直接/间接预测模型(间接模型是指先预测东亚季风指数,再以此估计站点降水), 以及“区域-站点”降水直接/间接预测模型,即先预测一个区域的降水再分配到站点。此外,还构建了所有模型集合的降水预测模型。预报因子包括两种ENSO指数、北大西洋涛动(NAO)指数和青藏高原积雪深度等4个因子。模型亦分为3因子和4因子(包括积雪因子)组及对降水取/不取对数组等前处理。2005—2016年的回报试验表明,“站点”模型优于“区域-站点”模型,对降水取对数模型优于不取对数模型。另外,“站点”模型组中的间接降水预测模型优于直接模型,但“区域-站点”组却相反。ERA-Interim积雪深度资料不确定性带来的偏差超过该因子对降水预测的贡献。平均PS评分最高的是3因子的直接站点降水取对数模型(MDS-3Ln),平均达到71分,高于集合模型(MEM)得分。这些结果表明,线性回归降水模型的设计理念与实际预测效果可能并不一致,其原因是因子的选取或数学处理过程会引入新的不确定性或偏差,必须综合评估各种设计方案的“成本-效益”关系。
英文摘要:
      16 kinds of linear regression prediction models are developed based on precipitation data of 160 stations in China and ECMWF reanalysis data (ERA-Interim). The models include station direct/indirect precipitation prediction model group (in indirect models, the East Asian monsoon index is firstly predicted, and based on which, station precipitations are predicted.), region-station direct/indirect precipitation prediction model group (regional precipitations are firstly predicted, and then distributed to the stations). In addition, an ensemble precipitation prediction model is composed with those 16 models as members. There are totally 4 factors in the regression equations, including two types of ENSO index, North Atlantic Oscillation(NAO)index and the mean snow depth on Tibetan plateau.According to the number of factors, the models can also be grouped into 3 factors (including the ENSO indices and NAO index) models or 4 factors (contains all the factors) models. In addition, the differences of the models also lies in their predictor, precipitation or its logarithm. Hindcast for 2005—2016 shows that the average PS score for the station prediction model group is higher than that of region-station prediction model group. The performance of the models taking logarithmic precipitation as predictor is better than those using precipitation itself as predictor. In the station prediction model group, the indirect precipitation prediction models are usually superior to the direct ones, while it is opposite for the region-station model group. For precipitation prediction, the deviation caused by the uncertainty in snow depth of ERA-Interim reanalysis data is greater than the contribution of the factor of snow depth. Among all the models, the direct station prediction model with 3 factors for logarithmic precipitation (MDS-3Ln) get the highest PS score on average, reaching 71 point, which is higher than that of the ensemble model (MEM). It is indicated that the real performance of a linear regression precipitation model is not always consistent with its design strategy, since new factors or processing methods may induce new uncertainties or deviations. Therefore, it is necessary to evaluate the Cost-Effectiveness in model development.
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