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基于DERF2.0的长江中下游春播期气候预测
引用本文:章大全,王永光.基于DERF2.0的长江中下游春播期气候预测[J].应用气象学报,2016,27(2):182-190.
作者姓名:章大全  王永光
作者单位:中国气象局国家气候中心,北京 100081
基金项目:资助项目: 全球变化研究国家重大科学研究计划项目(2012CB955203),公益性行业(气象)科研专项(GYHY201306032,GYHY201406022),中国气象局气象关键技术集成与应用项目(152020012004),中国气象局短期气候预测创新团队项目
摘    要:基于1983—2012年国家气候中心第2代月动力延伸模式 (DERF2.0) 回报资料和春播历史资料,结合NCEP/NCAR再分析资料,选取影响长江中下游地区春播期气候条件的关键环流因子。利用最优子集回归方法建立针对长江中下游地区春播期气候条件的动力模式解释应用预测模型,并对不同起报时次的模式解释应用预测结果进行检验评估。检验结果表明:该解释应用方案对于长江中下游春播期气候条件有较好的预测能力,且随着起报时间的临近,预测技巧整体呈上升趋势。1983—2012年的回报检验还显示,解释应用方案能够较好地模拟出连续不利日数和不利日数的年际变率,同时对年代际变率也有所体现。

关 键 词:月动力延伸模式    春播    模式解释应用    预测
收稿时间:7/2/2015 12:00:00 AM

Climatic Prediction of Spring Sowing Period in the Middle and Lower Reaches of the Yangtze Based on DERF2.0
Zhang Daquan and Wang Yongguang.Climatic Prediction of Spring Sowing Period in the Middle and Lower Reaches of the Yangtze Based on DERF2.0[J].Quarterly Journal of Applied Meteorology,2016,27(2):182-190.
Authors:Zhang Daquan and Wang Yongguang
Institution:National Climate Center, CMA, Beijing 100081
Abstract:Based on the hindcast data of the second generation monthly dynamic extended range forecast model (DERF2.0) of National Climate Center and historical spring sowing data, combined with NCEP/NCAR reanalysis data, correlation analysis is conducted between historical spring sowing data and reanalysis data and model hindcast data, respectively. Regions with anomalies correlation coefficient (ACC) passing significant test are defined as key circulation zones, and the overlapping areas where both anomalies correlation coefficients passing the significant test are selected as key influencing factors of climatic conditions of spring sowing period in the middle and lower reaches of the Yangtze. Using historical time series of selected factors of certain circulation variables, e.g., geopotential height of 200, 500 hPa and 700 hPa, zonal and meridional wind of 850 hPa as predictors, favorable, unfavorable and continuous unfavorable days of spring sowing period in the middle and lower reaches of the Yangtze as predictors, utilizing optimal subset regression method (OSR), a model interpretation scheme of climatic conditions of sowing period prediction is established. The performance of the model interpretation scheme with different lead time are evaluated and analyzed. Meanwhile, the predictive skill of typical years with unfavorable climatic conditions is tested. Hindcast test of predictive scheme exhibits considerable overall predictive skill on both favorable and unfavorable days of spring sowing period. Predictive results of different lead time indicate that the prediction performance of unfavorable and continuous unfavorable days grows better as the lead time shortens. Moreover, hindcast result with lead time equals 0 shows that the model interpretation scheme not only simulates the annual variations well, but also illustrates certain predictive ability of decadal change of climatic conditions of spring sowing period. In the operational prediction of climatic conditions of spring sowing period, rolling forecast result of model interpretation scheme should be utilized to achieve better predictive performance. Since the model interpretation scheme does not give climatic conditions of spring sowing period directly, each variables of model output should be considered. In order to test the predictive skill of typical years with unfavorable climatic conditions, five years with typical unfavorable climatic conditions of spring sowing period (with continuous unfavorable days exceeding 10 days) are selected and verification results indicate that since the 1980s, the integrated result of scheme is approximately the same with observation, which exhibits considerable predictive skill.
Keywords:monthly dynamic extended range forecast model  spring sowing  model interpretation  prediction
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