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应用北半球大气环流系统预测中国降水量和气温
引用本文:金秀良,宋燕,吴洪,张志良,尤莉.应用北半球大气环流系统预测中国降水量和气温[J].气象科技,2014,42(6):1028-1038.
作者姓名:金秀良  宋燕  吴洪  张志良  尤莉
作者单位:内蒙古自治区兴安盟气象台,乌兰浩特 137400;中国气象局干部培训学院,北京 100081;中国气象局干部培训学院,北京 100081;吉林省气象台,长春 130062;内蒙古自治区气候中心,呼和浩特 010051
基金项目:国家重大科学研究计划(2012CB957803、2012CB957804)、中国气象局成都高原气象研究所高原气象开放基金课题(LPM2012004)和内蒙古自治区气候与气候变化创新团队项目共同资助
摘    要:在普查影响中国降水量和气温的大气环流系统及其指数的基础上,选取高相关、独立性强的大气环流系统指数作为预测因子,采用过滤式的筛选因子方法,动态建立线性回归方法,构建区域月气候预测模型(Regional Monthly Climate Forecast Model,RMCFM),自主开发操作系统,设置人机交互界面,实现人机交流操作方式,并通过解释应用方法做出中国160个台站的月降水量和月气温预测。实践证明,RMCFM具备天气气候专家系统概念,具有运算速度快,结构清晰易操作等特点,预测准确率较高,应用RMCFM可提高区域气候预测能力。

关 键 词:大气环流系统  释用方法  线性回归方程  区域月气候预测模型
收稿时间:2013/12/13 0:00:00
修稿时间:2014/7/18 0:00:00

Using Atmospheric Systems in Northern Hemisphere to Predict Monthly Precipitation and Temperature in China
Jin Xiuliang,Song Yan,Wu Hong,Zhang Zhiliang and You Li.Using Atmospheric Systems in Northern Hemisphere to Predict Monthly Precipitation and Temperature in China[J].Meteorological Science and Technology,2014,42(6):1028-1038.
Authors:Jin Xiuliang  Song Yan  Wu Hong  Zhang Zhiliang and You Li
Abstract:Various atmospheric systems and the indexes affecting precipitation and temperature in China are examined. The parameters of high independence and high correlation coefficients were chosen as prediction factors to predict the monthly precipitation and temperature of 160 observation stations through downscaling method. By using the filtration method to determine primary prediction factors and establishing the linear regression equations dynamically, the Regional Monthly Climate Forecast Model (RMCFM) is built up, in which the operational interface for communication between people and computer is set up. RMCFM can make monthly climate prediction of monthly precipitation and temperature over 160 stations in China. Practice proved that RMCFM is of high calculating speed and clear structure, and easy to operate. The real time daily grid reanalysis data in NCEP/NCAR were used as pretreatment data for RMCFM. It is proved that RMCFM improved the capability of regional climate prediction.
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