基于动力统计方法对中国夏季温度模式误差订正的研究

利用气候趋势系数分析1983-2012年NCEP/NCAR温度逐日再分析资料和国家气候中心全球海气耦合模式(CGCM)逐月温度资料,得出近30年中国夏季观测温度和模式预报温度趋势变化特征。通过经验模态分解方法拟合并去除观测温度整体趋势得到无趋势温度资料,结合系统误差订正和最优多因子组合动力统计相结合两种不同的模式误差订正预测方法,分析了全球变暖对中国夏季温度预测的影响。结果表明:近30年来中国夏季大部分地区观测温度呈上升趋势,且通过了0.01显著性水平检验,而模式预报温度几乎无趋势变化,对全球变暖存在明显的模拟不足。为避免这种模式对观测气温整体趋势的模拟不足对预测效果的影响并得到最优的预测结果,利用经验模态分解方法拟合并去除观测温度的整体趋势,对观测温度的拟合趋势预测时采取先去除预测后再回加的方式在预测过程中避开模式结果模拟不足的问题。近10年独立样本回报预测结果表明,上述方法可以有效地提高预测效果,并解决直接预测出现的预测结果偏低的问题,说明了在对模式预报结果后处理过程中去除增温趋势的重要性。

IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.

COMPLEX NETWORK OF EXTREME PRECIPITATION IN EAST ASIA

In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area-weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon(EASM),and another one with low area-weighted connectivity receiving heavy precipitation during both the active and the retreating phase of the EASM. Besides, a new way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day(2≤n≤10) lead, respectively. Compared to the normal EASM year, the prediction accuracy is low in weak years and high in strong years, which is relevant to the differences of correlations and extreme precipitation rates in different EASM situations. Recognizing and indentifying these effects is good for understanding and predicting extreme precipitation in East Asia.

Spatiotemporal distribution characteristics and attribution of extreme regional low temperature event

Based on an objective identification technique for regional low temperature event（OITRLTE）, the daily minimum temperature in China has been detected from 1960 to 2013. During this period, there were 60 regional extreme low temperature events（ERLTEs）, which are included in the 690 regional low temperature events（RLTEs）. The 60 ERLTEs are analyzed in this paper. The results show that in the last 50 years, the intensity of the ERLTEs has become weak; the number of lasted days has decreased; and, the affected area has become small. However, that situation has changed in this century.In terms of spatial distribution, the high intensity regions are mainly in Northern China while the high frequency regions concentrate in Central and Eastern China. According to the affected area of each event, the 60 ERLTEs are classified into six types. The atmospheric circulation background fields which correspond to these types are also analyzed. The results show that, influenced by stronger blocking highs of Ural and Lake Baikal, as well as stronger southward polar vortex and East Asia major trough at 500-h Pa geopotential height, cold air from high latitudes is guided to move southward and abnormal northerly winds at 850 h Pa makes the cold air blow into China along diverse paths, thereby forming different types of regional extreme low temperatures in winter.

基于奇异值分解对中国夏季降水模式误差订正的研究

利用奇异值分解(SVD)方法对国家气候中心气候业务模式CGCM的预报结果进行订正,进而得到改进后的预报结果.通过对1983—2011年中国夏季降水的模式预报和实际降水值各年份之间的耦合场进行SVD分解,选取模式预报和实况降水相关性最好的前3—7个模态,对这5种不同模态个数的订正结果加以对比,选出对中国区域订正效果最好的模态个数,得到模式预报的订正结果.对2004—2009年6年进行交叉回报试验,以距平相关系数和均方根误差作为评判标准来检验回报结果,得出2004—2009大部分年份取前5个模态作为当年订正的模态个数时,订正效果最好.利用已有的2004—2009年的模式和实际降水资料进行检验,证实在大部分年份5个模态订正效果最好,并将结果与系统误差订正法的结果相比较,表明SVD法对2004—2009年6年的订正结果中,有4年比系统误差法的订正效果好.以2010年作为预测年,以1983—2009年27年交叉回报检验的结果确定模态个数,对预测结果加以分析,显示该方法具有潜在的业务应用价值.

长江中下游汛期降水模式预测误差相似性及其可预报度

对数值模式预测误差进行相似预报进而订正模式预报结果,是提高模式预报水平的有效手段之一.本文从数值模式预测误差场存在相似性的角度出发,研究了长江中下游汛期降水逐年模式误差场间的相似性及其可预报性,探讨了数值模式预测误差在历史资料中的相似信息量,发现利用相似误差订正可以明显提高模式预报水平.进一步对历史模式预测误差场进行经验正交函数(EOF)分解,着重研究了误差场前三个特征向量的空间分布及其时间系数演化规律,发现对各分量分别进行相似预报可以简化预报对象,并且针对性更强,可以提高其潜在预报水平.在研究数值模式预测误差场相似性的基础上,定义了数值模式预测误差的相似可预报度,用以衡量逐年模式预测误差的可预测性,发现模式预测误差场前三个分量的相似可预报度明显高于原始模式误差场,揭示出有针对性地分别预报模式预测误差各分量的潜在应用价值.

社区高血压慢性病管理模式对于预防并发症的分析

对社区慢性病管理在高血压管理中应用效果进行分析。方法:选取本院收治的高血压患者66例作为此次的研究对象,将其均分成对照组与观察组,33例对照组患者采取常规管理模式,另外33例患者采取社区慢性病管理模式,将两组患者管理后的生活质量情况进行对比与分析。结果:观察组患者管理后的生活质量情况显著比对照组患者管理后的生活质量情况优,差异明显具统计学意义(p<0.05)。结论:高血压患者管理工作中实施社区慢性病管理措施可有效提高患者治疗效果,改善患者生活质量,值得在临床上应用与推广。