摘要
中国夏季降水大幅度月际尺度变化往往造成极端旱涝事件交替或转折,但其月际异常会被季节平均掩盖,影响季节尺度气候预测准确度,因此亟需考虑月际气候预测,提升月际—季节尺度气候预测准确度。本文首先采用年际增量和场信息耦合型预测方法研制中国夏季6~8月月际尺度降水动力和统计结合气候预测模型,之后根据月际尺度降水预测,开展季节平均降水预测。首先,基于前期观测信息和美国第二代气候预测系统(CFSv2)预测结果,选取前期12月观测的南太平洋中高纬关键区海温、1月北极关键区海冰密集度以及CFSv2预测系统2月起报的夏季同期关键区海温作为月际尺度降水预测因子,分别研制以上具有物理意义的单预测因子预测模型,并采用奇异值分解(SVD)误差订正方法对其改进;之后,利用多因子择优集合方案,研制预测效能较高且稳定的中国160站夏季月际尺度降水动力和统计结合预测模型,进而基于月际尺度预测开展夏季季节平均气候预测。1983~2022年夏季(6~8月)中国160站逐月降水预测模型的交叉检验结果表明:逐月回报与观测降水距平百分率的时间相关系数通过90%置信水平的站点占比分别为90%,88%,82%,多年平均的空间相关系数分别为0.39、0.40和0.39,均通过99%置信水平。针对2020~2022年连续三年同样拉尼娜背景下但不同中国夏季降水形势,开展月际—季节独立回报检验,其结果显示,2020~2022年6、7、8月预测降水距平百分率的趋势异常综合检验(PS)平均分分别为75、75和70分;夏季季节平均降水的PS评分分别为72、76和73分,均高于多年业务预测平均分。由此,考虑月际异常开展季节尺度气候预测是提升月际—季节尺度气候预测准确度的一个有效途径。
Large intermonth variations in summer precipitation tend to cause alternations or transitions of extreme drought and flood in China;however,seasonal averages may cover alternations on a monthly scale and influence prediction skills on a seasonal scale.Therefore,improving monthly climate forecasts is imperative,contributing to prediction enhancement on the seasonal scale.This study focuses on real-time predictions of monthly precipitation at 160 stations in China during the summer season(June,July,and August)using the year-to-year increment method and field information coupled pattern method,and seasonal precipitation is calculated using monthly predictions.Information from preceding observations and simultaneous predictions from the second version of the Climate Forecast System(CFSv2)are considered.Consequently,the observed sea surface temperature(SST)over the mid-high latitude of the South Pacific in December,the observed sea ice concentration in the critical region of the Arctic in January,and the simultaneous SST from CFSv2 released in February are selected as predictors to develop the downscaling model.First,prediction models based on individual predictors are established to evaluate the prediction skills of different predictors,and subsequently,the singular value decomposition error correction method is employed to reduce errors in downscaling models.Additionally,the optimized ensemble scheme is utilized to synthesize hybrid downscaling models for summer precipitation over China on monthly scale with higher stability,and further seasonal prediction is conducted with results on monthly scale.The re-forecast results during the period 1983−2022 showed that the hybrid downscaling models derived from the optimized ensemble scheme exhibit comprehensive prediction skills compared with single-predictor models.The percentages of stations,at which the time anomaly correlation coefficients of re-forecast results are larger than the 90%confidence level,count for 90%,88%,and 82%respectively for June,July,and August.The
作者
范可
田宝强
戴海霞
FAN Ke;TIAN Baoqiang;DAI Haixia(School of Atmospheric Science,Sun Yat-sen University,and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519082;Nansen-Zhu International Research Centre,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029;Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science&Technology,Nanjing 210044;College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410073)
出处
《大气科学》
CSCD
北大核心
2024年第1期359-375,共17页
Chinese Journal of Atmospheric Sciences
基金
国家自然科学基金项目42230603
国家重点研发计划“政府间国际科技创新合作”重点专项2022YEF0106800
南方海洋科学与工程广东省实验室(珠海)创新团队建设项目311020001。
关键词
中国夏季月际—季节尺度
动力和统计结合降水预测
SVD误差订正
择优集合方案
实时预测
Monthly predictions of precipitation in China during summer
SVD error correction method
Optimized ensemble scheme
Dynamic and statistic combined method
Real-time prediction
