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非线性BP网络映射与赤道东太平洋海温预测 被引量:11
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作者 张韧 《海洋通报》 CAS CSCD 北大核心 2000年第4期1-7,共7页
基于NCEP/NCAR再分析资料,利用人工神经网络BP模型及其优化算法,建立起了赤道太平洋纬向风和滞后的东太平洋海温之间的映射关系和预报模型,成功地模拟和仿真出了1982/1983、1986/1987年发生的强EI ... 基于NCEP/NCAR再分析资料,利用人工神经网络BP模型及其优化算法,建立起了赤道太平洋纬向风和滞后的东太平洋海温之间的映射关系和预报模型,成功地模拟和仿真出了1982/1983、1986/1987年发生的强EI Nino现象和1988年的强La Nina现象。结果表明,这种方法可有效用于辩识和反演复杂的大气、海洋动力系统及其预报模型。 展开更多
关键词 BP网络 非线性 海温预测 东太平洋 网络映射关系
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太平洋海温预测的神经网络模型 被引量:3
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作者 赵保华 朱雪亮 《计算机应用》 CSCD 1997年第4期18-19,共2页
本文对太平洋海表面温度场资料进行分析,研究探讨了一种基于B-P网络的海温预测模型。根据阿留申群岛等四个关键区的海温,可以比较准确地预测赤道东太平洋的海温。
关键词 海洋气候 神经网络模型 海温预测 太平洋
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Future projection of East China Sea temperature by dynamic downscaling of the IPCC_AR4 CCSM3 model result 被引量:2
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作者 于晓林 王凡 唐晓晖 《Chinese Journal of Oceanology and Limnology》 SCIE CAS CSCD 2012年第5期826-842,共17页
Future temperature distributions of the marginal Chinese seas are studied by dynamic downscaling of global CCSM3 IPCC_AR4 scenario runs. Different forcing fields from 2080-2099 Special Report on Emissions Scenarios (... Future temperature distributions of the marginal Chinese seas are studied by dynamic downscaling of global CCSM3 IPCC_AR4 scenario runs. Different forcing fields from 2080-2099 Special Report on Emissions Scenarios (SRES) B1, A1, and A2 to 1980-1999 20C3M are averaged and superimposed on CORE2 and SODA2.2.4 data to force high-resolution regional future simulations using the Regional Ocean Modeling System (ROMS). Volume transport increments in downscaling simulation support the CCSM3 result that with a weakening subtropical gyre circulation, the Kuroshio Current in the East China Sea (ECS) is possibly strengthened under the global wanning scheme. This mostly relates to local wind change, whereby the summer monsoon is strengthened and winter monsoon weakened. Future temperature fluxes and their seasonal variations are larger than in the CCSM3 result. Downscaling 100 years' temperature increments are comparable to the CCSM3, with a minimum in B1 scenario of 1.2-2.0~C and a maximum in A2 scenario of 2.5-4.5~C. More detailed temperature distributions are shown in the downscaling simulation. Larger increments are in the Bohai Sea and middle Yellow Sea, and smaller increments near the southeast coast of China, west coast of Korea, and southern ECS. There is a reduction of advective heat north of Taiwan Island and west of Tsushima in summer, and along the southern part of the Yellow Sea warm current in winter. There is enhancement of advective heat in the northern Yellow Sea in winter, related to the delicate temperature increment distribution. At 50 meter depth, the Yellow Sea cold water mass is destroyed. Our simulations suggest that in the formation season of the cold water mass, regional temperature is higher in the future and the water remains at the bottom until next summer. In summer, the mixed layer is deeper, making it much easier for the strengthened surface heat flux to penetrate to the bottom of this water. 展开更多
关键词 future temperature marginal China seas DOWNSCALING IPCC AR4 SRES CCSM3 ROMS
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Predicting Western Pacific Subtropical High Using a Combined Tropical Indian Ocean Sea Surface Temperature Forecast 被引量:2
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作者 WANG Li-Wei ZHENG Fei ZHU Jiang 《Atmospheric and Oceanic Science Letters》 CSCD 2013年第6期405-409,共5页
Weather and climate in East China are closely related to the variability of the western Pacific subtropical high(WPSH), which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer ... Weather and climate in East China are closely related to the variability of the western Pacific subtropical high(WPSH), which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature(SST) over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial. 展开更多
关键词 western Pacific subtropical high SST tropical Indian Ocean statistical prediction
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厄尔尼诺海区海温季节预测比较 被引量:3
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作者 余锦华 《南京气象学院学报》 CSCD 1999年第3期374-380,共7页
利用奇异值分解方法建立多项线性统计气候预测模型,对厄尔尼诺海区各季节海温进行了短期气候预测试验,并采用历史资料独立样本检验的方法,评定不同预报时效的预报技巧。结果表明,不同海区各季节的预报效果相差很大。赤道西太平洋(... 利用奇异值分解方法建立多项线性统计气候预测模型,对厄尔尼诺海区各季节海温进行了短期气候预测试验,并采用历史资料独立样本检验的方法,评定不同预报时效的预报技巧。结果表明,不同海区各季节的预报效果相差很大。赤道西太平洋( Nino 4区)有最高的可预报性,在夏季末做冬季海温预报的技巧最高,多数条件下,这个区域的预报性能比较稳定。以海温和南方涛动指数作因子场的预报效果最好,大部分预报以单季的海温和南方涛动指数为预报因子效果较好,但有些预报以连续2季或3季的海温和南方涛动指数作预报因子的预报效果更好。 展开更多
关键词 气候预测 奇异值分解 季节海温预测 厄尔尼诺
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基于CCA-BP-BPNN释用模型的太平洋SST预报 被引量:4
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作者 周林 杨成荫 +1 位作者 王汉杰 赵苏璇 《解放军理工大学学报(自然科学版)》 EI 北大核心 2009年第4期391-396,共6页
为了有效提高预报精度,将一种基于神经网络并能综合有效利用全场信息的非线性释用技术应用到海洋SST预测上。通过CCA-BP法建立的典型因子,可以代表气象因子场与SST之间的大部分协方差关系,使气象因子与站点要素相关性大为提高,进而通过... 为了有效提高预报精度,将一种基于神经网络并能综合有效利用全场信息的非线性释用技术应用到海洋SST预测上。通过CCA-BP法建立的典型因子,可以代表气象因子场与SST之间的大部分协方差关系,使气象因子与站点要素相关性大为提高,进而通过神经网络技术(BPNN)建立非线性预报模型。利用该模型尝试对热带太平洋表层海温形势(ENSO)进行预报,并建立了该区域逐点海温的预报方案。试报结果表明,该方法对预测春季海温形势有较好的效果,有效预报时效可达1 a以上;对6 a的3月份热带太平洋表层海温预报,平均绝对误差为0.22°C。该方法为海洋SST统计预报提供了一个值得参考的途径。 展开更多
关键词 解释应用 海温预测ENSO预测 典型相关 神经网络误差后传算法
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