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论《怀风藻》汉诗对曹植《洛神赋》的接受
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作者 王伟伟 《长春工程学院学报(社会科学版)》 2020年第3期80-84,共5页
曹植《洛神赋》随《文选》在近江奈良朝广泛传播,成为日本知识阶层熟稔的文学文本,进而对古代日本文学、文化发出了强力辐射。本论文通过中日诗歌原典的考辨,梳理、论证《怀风藻》对《洛神赋》中语象、意象的接受。《洛神赋》中的"... 曹植《洛神赋》随《文选》在近江奈良朝广泛传播,成为日本知识阶层熟稔的文学文本,进而对古代日本文学、文化发出了强力辐射。本论文通过中日诗歌原典的考辨,梳理、论证《怀风藻》对《洛神赋》中语象、意象的接受。《洛神赋》中的"洛神"原型、"洛浦"意象对古代日本"人神恋"叙事范式的确立和发展,"神女"系神话、"仙境"的建构具有重要意义。《洛神赋》承载的美感心理、情感模式以"符号"的形式进入日本文化,并不断被改写、重构,成为其不断传承的大陆"文化因子"。 展开更多
关键词 洛神赋 怀风藻 接受 洛浦 回雪
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A case study of cold-seasonthundersnow in Beijing 被引量:2
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作者 JIAO Reguang CHEN Bin +1 位作者 HABIB Ammara SHI Guangyu 《Atmospheric and Oceanic Science Letters》 CSCD 2019年第6期392-398,共7页
The characteristics of local thundersnow at ne spatiotemporal scale on 9 and 10 November 2009 in Beijing were analyzed,using wind pro ler,microwave radiometer,automatic weather station,Doppler weather radar,and satell... The characteristics of local thundersnow at ne spatiotemporal scale on 9 and 10 November 2009 in Beijing were analyzed,using wind pro ler,microwave radiometer,automatic weather station,Doppler weather radar,and satellite data.Furthermore,the causes of winter convection are discussed.The results showed that it was reflux weather.The cause of the thunder and lightning was the elevated convection above the lower cold and dry air,and the trigger for convection was the short wave trough and convergencein the middle level.This thundersnow event developed from monsoon-like long-lasting water vapor transport at 850 hPa from the South China Sea along with strong instability of,and convergence in,the conveyor layer. 展开更多
关键词 THUNDERSNOW REFLUX doppler radar wind profiler RADIOMETER
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Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China 被引量:5
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作者 LIU Yang LI Lan-hai +2 位作者 CHEN Xi YANG Jin-Ming HAO Jian-Sheng 《Journal of Mountain Science》 SCIE CSCD 2018年第1期33-45,共13页
Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect ... Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution. 展开更多
关键词 Snow depth Spatial distribution Regression kriging Geographically weighted regression kriging
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An Artificial Neural Network-Based Snow Cover Predictive Modeling in the Higher Himalayas 被引量:1
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作者 Bhogendra MISHRA Nitin K.TRIPATHI Muk S.BABEL 《Journal of Mountain Science》 SCIE CSCD 2014年第4期825-837,共13页
With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantita... With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network (ANN), was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from - HadCM3, a global circulation model to project future climate scenario, under the AIB emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2Oll to 2040. The 4700 m to 52oo m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantialchanges due to the impact of climate change. 展开更多
关键词 Snow cover Kaligandai river HIMALAYAS Artificial neural network Global warming CLIMATECHANGE
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