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1.
中国业务动力季节预报的进展   总被引:34,自引:9,他引:25       下载免费PDF全文
利用动力模式开展季节到年际的短期气候预测 ,是目前国际上气候预测的发展方向。自 1996年以来 ,经过 8a多的研制和发展 ,国家气候中心已建立起第 1代动力气候模式预测业务系统 ,其中包括 1个全球大气 海洋耦合模式 (CGCM )、1个高分辨率东亚区域气候模式 (RegCM_NCC)和 5个简化的ENSO预测模式 (SAOMS) ,可用于季节—年际时间尺度的全球气候预测 ;全球海气耦合模式与区域气候模式嵌套 ,可以提供高分辨率的东亚区域气候模式制做季节预测。CGCM对 1982~ 2 0 0 0年夏季的历史回报试验表明 ,该模式对热带太平洋海表面温度和东亚区域的季节预测具有较好的预测能力。RegCM NCC的 5a模拟基本上能再现东亚地区主要雨带的季节进展。利用嵌套的区域气候模式RegCM NCC对 1991~ 2 0 0 0年的夏季回报表明 ,在预报主要季节雨带方面有一定技巧。 2 0 0 1~ 2 0 0 3年 ,CGCM和RegCM NCC的实时季节预报与观测相比基本合理。特别是 ,模式成功地预报了 2 0 0 3年梅雨季节长江和黄河之间比常年偏多的降水。SAOMS模式系统的回报试验表明 ,该系统对热带太平洋海表面温度距平有一定的预报能力 ,模式超前 6~ 12个月的回报与观测的相关系数明显高于持续预报。 1997~ 2 0 0 3年 ,SAOMS多模式集合实时预报与观测的相关系数达到  相似文献
2.
文中利用一个全球大气 海洋耦合模式 ,对中国汛期气候异常进行了 1991~ 2 0 0 1年共 11a的跨季度回报试验和检验研究。采用一套多指标的评估方法 ,对该模式的预报性能进行系统的定量评估。结果表明 ,该模式对中国汛期降水和温度及夏季北半球大尺度环流场等都有一定的跨季度预报能力。模式对中国不同区域夏季降水的预测能力有所不同。总的来说 ,模式对中国东部和西部的降水趋势回报较好 ,模式预报好于气候预报和持续性预报。从相关系数指标来看 ,模式跨季度预测夏季温度的技巧在中国西部比中国东部高。  相似文献
3.
利用全球环流模式进行我国汛期短期气候预测的试验   总被引:10,自引:2,他引:8       下载免费PDF全文
利用OSU/NCC全球大气环流模式耦合全球混合层海洋与海冰模式,采用集合预报的方法,对中国汛期降水进行了1982~1995 年共14 年的跨季度综合性回报检验研究.结果表明,该模式对我国汛期降水具有一定的跨季度预报能力,对部分地区(江淮至华东沿海、东北部分地区等)有较强的预报能力.  相似文献
4.
A Correction Method Suitable for Dynamical Seasonal Prediction   总被引:8,自引:0,他引:8       下载免费PDF全文
Based on the hindcast results of summer rainfall anomalies over China for the period 1981–2000 by the Dynamical Climate Prediction System (IAP-DCP) developed by the Institute of Atmospheric Physics, a correction method that can account for the dependence of model’s systematic biases on SST anomalies is proposed. It is shown that this correction method can improve the hindcast skill of the IAP-DCP for summer rainfall anomalies over China, especially in western China and southeast China, which may imply its potential application to real-time seasonal prediction.  相似文献
5.
影响我国的热带气旋年频数预测   总被引:5,自引:0,他引:5       下载免费PDF全文
利用中国气象局<热带气旋年鉴>的热带气旋大风和降水资料集,确定了明显影响我国及华东和华南地区的热带气旋个例,并研制热带气旋年频数的预测方案,使得频数预测对防灾减灾更为实用.预测因子采用相关普查的方法,从1961-2000年前期的海表温度、海平面气压及200,500 hPa和850 hPa位势高度和风场中选出,所用的资料为NOAA ER SST和NCEP/NCAR再分析资料.在相关分析的基础上,构建因子时兼顾了因子的系统性的空间结构和时间的变化,并用主成分分析方法去除因子的多重共线性;在最优子集回归建模的基础上进一步对模型进行检验和优化.模型检验和2001-2008年回报试验说明各模型均对各自热带气旋频数(TCF)具有较好的预测能力.  相似文献
6.
China is a monsoon country.The most rainfalls in China concentrate on the summer seasons.More frequent floods or droughts occur in some parts of China.Therefore,the prediction ofsummer rainfall in China is a significant issue.As we know,the obvious impacts of the sea surfacetemperature anomalies(SSTA)on the summer rainfall over China have been noticed.Thepredictions of the SSTA have been involved in the research.The key project on short-term climate modeling prediction system has been finished in 2000.The system included an atmospheric general circulation model named AGCM95,a coupledatmospheric-oceanic general circulation model named AOGCM95,a regional climate model overChina named RegCM95,a high-resolution Indian-Pacific OGCM named IPOGCM95,and asimplified atmosphere-ocean dynamic model system named SAOMS95.They became theoperational prediction models of National Climate Center(NCC).Extra-seasonal predictions in 2001 have been conducted by several climate models,which werethe AGCM95,AOGCM95,RegCM95,IPOGCM95,AIPOGCM95,OSU/NCC,SAOMS95,IAPAPOGCM and CAMS/ZS.All of those models predicted the summer precipitation over China and/or the annual SSTA over the tropical Pacific Ocean in the Modeling Prediction Workshop held inMarch 2001.The assessments have shown that the most models predicted the distributions of main rain beltover Huanan and parts of Jiangnan and droughts over Huabei-Hetao and Huaihe River Valleyreasonably.The most models predicted successfully that a weaker cold phase of the SSTA over thecentral and eastern tropical Pacific Ocean would continue in 2001.The evaluations of extra-seasonal predictions have also indicated that the models had a certaincapability of predicting the SSTA over the tropical Pacific Ocean and the summer rainfall overChina.The assessment also showed that multi-model ensemble(super ensembles)predictionsprovided the better forecasts for both SSTA and summer rainfall in 2001,compared with the singlemodel.It is a preliminary assessment for the extra-seasonal predictions by the climate models.Thefurther investigations will be carried out.The model system should be developed and improved.  相似文献
7.
最优子集回归方法在季节气候预测中的应用   总被引:3,自引:1,他引:2       下载免费PDF全文
柯宗建  张培群  董文杰 《大气科学》2009,33(5):994-1002
利用DEMETER计划多个模式的模拟资料研究1959~2001年多模式集合预报的季节降水在中国区域的表现,并结合最优子集回归(OSR)方法对中国区域的季节降水进行降尺度预报,比较其与多模式集合预报的技巧.研究表明:多个单模式在中国区域对季节降水的模拟性能普遍较差,多元线性回归(MLR)集合的预报技巧不如集合平均(EM).利用OSR方法进行降尺度预报可以极大改善中国区域季节降水的预报技巧.夏季,降水距平相关系数(ACC)在长江以南、西藏以及内蒙古中部等地区提高很显著,ACC在中国区域的平均达到0.29,明显高于多模式集合平均与多无线性回归集合.冬季,OSR方法可以改善多模式集合在中国北方地区较低的预报技巧.概率Brier技巧评分(BSS)也表明了OSR方法对季节降水预报的改善.需要说明的是,虽然OSR方法在中国区域能明显提高季节降水的预报技巧,但是其选取的预报因子与中国区域季节降水的物理机制问题仍有待于进一步的研究.  相似文献
8.
李芳 《气象学报》2012,70(2):183-191
借助ENSEMBLES计划提供的5个海-气耦合模式(CGCM)的多初值后报降水资料,采用常用的4种多模式集合方案,即等权集合(EE)、对单个集合成员先订正再等权集合(Cali-EE)、基于多元线性回归的集合方案(MLR)、基于贝叶斯统计学的集合方案(Bayes),制作1960—2005年中国东部夏季降水概率密度函数(PDF)季度预测。在此基础上,比较最优(技巧最高)集合方案与气候学预测(衡量概率密度函数预测是否有技巧的基准)的技巧,初步评估目前基于多模式集合方案的、中国东部夏季降水的概率密度函数季度预测能力。结果表明,Bayes方案在华南最优,Cali-EE在长江流域、江淮流域以及中国北方的中部最优,MLR在中国北方的东部最优;基于这些最优集合方案的概率密度函数预测产品均具有高校准度,且其锐度高于或接近气候学预测;并且,对于所有区域,最优集合方案的预测技巧总是高于气候学预测,这暗示即使不提取模式其他变量中所包含的预测信息,对于中国东部夏季降水季度预测,常用的多模式集合方案也已具备制作有技巧的概率密度函数预测产品的能力。  相似文献
9.
Recent advances in dynamical climate prediction at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS) during the last five years have been briefly described in this paper. Firstly,the second generation of the IAP dynamical climate prediction system (IAP DCP-II) has been described,and two sets of hindcast experiments of the summer rainfall anomalies over China for the periods of 1980-1994 with different versions of the IAP AGCM have been conducted. The comparison results show that the predictive skill of summer rainfall anomalies over China is improved with the improved IAP AGCM in which the surface albedo parameterization is modified. Furthermore, IAP DCP-II has been applied to the real-time prediction of summer rainfall anomalies over China since 1998, and the verification results show that IAP DCP-II can quite well capture the large scale patterns of the summer flood/drought situations over China during the last five years (1998-2002). Meanwhile, an investigation has demonstrated the importance of the atmospheric initial conditions on the seasonal climate prediction, along with studies on the influences from surface boundary conditions (e.g., land surface characteristics, sea surface temperature).Certain conclusions have been reached, such as, the initial atmospheric anomalies in spring may play an important role in the summer climate anomalies, and soil moisture anomalies in spring can also have a significant impact on the summer climate anomalies over East Asia. Finally, several practical techniques(e.g., ensemble technique, correction method, etc.), which lead to the increase of the prediction skill for summer rainfall anomalies over China, have also been illustrated. The paper concludes with a list of criticalre quirements needed for the further improvement of dynamical seasonal climate prediction.  相似文献
10.
We present a model for predicting summertime surface air temperature in Northeast China (NESSAT) using a year-to-year incremental approach. The predicted value for each year's increase or decrease of NESSAT is added to the observed value within a particular year to yield the net forecast NESSAT. The seasonal forecast model for the year-to-year increments of NESSAT is constructed based on data from 1975-2007. Five predictors are used: an index for sea ice cover over the East Siberian Sea, an index for central Pacific tropical sea surface temperature, two high latitude circulation indices, as well as a North American pressure index. All predictors are available by no later than March, which allows for compilation of a seasonal forecast with a two-month lead time. The prediction model accurately captures the interannual variations of NESSAT during 1977-2007 with a correlation coefficient between the predicted and observed NESSAT of 0.87 (accounting for 76% of total variance) and a mean absolute error (MAE) of 0.3℃. A cross-validation test during 1977 2008 demonstrates that the model has good predictive skill, with MAE of 0.4℃ and a correlation coefficient between the predicted and observed NESSAT of 0.76.  相似文献
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