CAJ | 학술논문

利用逐日气温和降水量数据、NCEP／NCAR 再分析资料以及预报场资料，通过分析提取我国南方区域持续性低温雨雪过程及其预报因子，使用粒子群-神经网络方法建立非线性的统计集合预报模型（PSONN-EPM），对我国南方区域持续性低温雨雪过程进行预报试验。结果表明：以过程的冷湿程度及影响范围为标准，将低温雨雪过程分为一般过程和严重过程，并建立不同的预报模型效果较好。通过10 d 独立样本预报试验看，基于粒子群-神经网络方法建立的集合预报模型比基于逐步回归方法建立的预报模型的预报平均相对误差小，对严重过程预报能力高于对一般过程预报，且这种非线性统计集合建模方法在建模过程中不需要调整神经网络参数，在实际预报业务中值得尝试。
이용축일기온화강수량수거、NCEP／NCAR 재분석자료이급예보장자료，통과분석제취아국남방구역지속성저온우설과정급기예보인자，사용입자군-신경망락방법건립비선성적통계집합예보모형（PSONN-EPM），대아국남방구역지속성저온우설과정진행예보시험。결과표명：이과정적랭습정도급영향범위위표준，장저온우설과정분위일반과정화엄중과정，병건립불동적예보모형효과교호。통과10 d 독립양본예보시험간，기우입자군-신경망락방법건립적집합예보모형비기우축보회귀방법건립적예보모형적예보평균상대오차소，대엄중과정예보능력고우대일반과정예보，차저충비선성통계집합건모방법재건모과정중불수요조정신경망락삼수，재실제예보업무중치득상시。
Based on daily minimum temperature,maximum temperature and precipitation data of 756 stations in China,National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR)reanalysis data during 1951-2013 and NCEP 24 h forecast data,a nonlinear statistical ensemble prediction model based on the particle swarm optimization-neural network (PSONN-EPM)is developed for predicting and verifying the regional persistent freezing rain and snow storm process in southern China by analyzing and extracting significant predictors.Results show that model performance can be effectively im-proved when dividing low-temperature processes into the general process and severe process which are con-structed based on cold extents,humidity and influence ranges of the freezing rain and snow storm proces-ses.In 10-day independent forecast test,the average relative errors for the general process and the severe process are 2.04 and 0.6 using stepwise regression equation forecast method,while those are 1.33 and 0.30 by using PSONN-EPM technique.It means forecast errors are reduced by 0.71 and 0.3 as compared with the stepwise regression method.In addition,the predication result for the severe freezing rain and snow storm process is better than that for the general process.The PSONN-EPM integrates predictions of multiple ensemble members,thus the prediction accuracy and stability are higher than those of the tradi-tional linear regression method.Furthermore,such method does not contain any tunable parameters,and is applicable for practical operational weather prediction.