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基于BPSO-NBayes的雷暴释用预报技术研究 总被引：1，自引：1，他引：0
提出了一种新的雷暴预报法,即二进制粒子群-朴素贝叶斯分类器(Binary Particle Swarm Optimization-Naive Bayesian Classifiers,BPSO-NBayes)方法,以福州、连城、宁波3站为例,对使用T511数值预报产品站点的雷暴释用预报技术进行研究。利用2010—2014年T511数值预报产品和单站观测资料,使用BPSO-NBayes方法,建立了0~72 h雷暴预报模型,并与Fisher判别准则和Bayes判别准则进行比较。预报结果表明,BPSO-NBayes模型临界成功指数都在0.29以上,平均值达到0.33以上,是3种方法中最好的,空报率都在0.59以下,漏报率在0.60以下,而且变化幅度很小。BPSO-NBayes模型明显优于Fisher判别准则和Bayes判别准则,具有良好的稳定性和预报能力。 相似文献
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A Modeling Study of the Effects of Anomalous Snow Cover over the Tibetan Plateau upon the South Asian Summer Monsoon 总被引：1，自引：0，他引：1
The effect of anomalous snow cover over the Tibetan Plateau upon the South Asian summer monsoon is investigated by numerical simulations using the NCAR regional climate model (RegCM2) into which gravity wave drag has been introduced. The simulations adopt relatively realistic snow mass forcings based on Scanning Multi-channel Microwave Radiometer (SNINIR) pentad snow depth data. The physical mechanism and spatial structure of the sensitivity of the South Asian early summer monsoon to snow cover anomaly over the Tibetan Plateau are revealed. The main results are summarized as follows. The heavier than normal snow cover over the Plateau can obviously reduce the shortwave radiation absorbed by surface through the albedo effect, which is compensated by weaker upward sensible heat flux associated with colder surface temperature, whereas the effects of snow melting and evaporation are relatively smaller.The anomalies of surface heat fluxes can last until June and become unobvious in July. The decrease of the Plateau surface temperature caused by heavier snow cover reaches its maximum value from late April to early May. The atmospheric cooling in the mid-upper troposphere over the Plateau and its surrounding areas is most obvious in May and can keep a fairly strong intensity in June. In contrast, there is warming to the south of the Plateau in the mid-lower troposphere from April to June with a maximum value in May.The heavier snow cover over the Plateau can reduce the intensity of the South Asian summer monsoon and rainfall to some extent, but this influence is only obvious in early summer and almost disappears in later stages. 相似文献
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敬信盆地第四系下更新统沉积环境以冲积扇相为主,发育有下部孔隙承压含水层(Mx);中上更新统以湖泊相为主,发育有上部孔隙承压含水层(Ms);孔隙潜水含水层的的发育层位为全新统。盆地内各层位地下水的补给来源主要为大气降水,上、下孔隙承压水主要靠孔隙潜水越流补给,其动态变化滞后于孔隙潜水近一个月,地下水向图们江径流、排泄。潜水含水层富水性较弱,水化学类型复杂;承压含水层富水性强,上部承压含水层水质优良,下部承压含水层水质稍差,水中Fe、Mn含量较高,并沿图们江向下游有增高的趋势。 相似文献
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Based on the annual frequency data of tropical cyclones from 1960 to 2005 and by the polynomial fit and statistical analysis, this work has discovered that TC activity in the 46a exhibits significant decadal- scale variability. It has two high frequency periods (HFP) and two low frequency periods (LFP). Significant differences in the number of TCs between HFP and LFP are found in active TC seasons from July to October. Differences of large-scale circulation during HFP and LFP have been investigated with NCEP/NOAA data for the season. In HFP, the condition includes not only higher sea surface temperature,lower sea level pressure, larger divergence of upper air, larger relative vorticity at low levels and smaller vertical shear, but also 500-hPa wind vector being more available for TC activity and moving to western North Pacific, the position of the subtropical anticyclone over the western Pacific shifting more northward,and South Asian Anticyclone at 100-hPa being much smaller than that in LFP. The precipitation of western North Pacific has no clear influence on TC activity. 相似文献
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台风“卢碧”变性增强过程的诊断研究 总被引：6，自引：6，他引：0
利用日本气象厅JRA再分析资料和台风最佳路径资料以及TRMM降水资料,对0920号台风"卢碧"变性过程的环流形势、高低层热力异常和锋生锋消等特征进行了分析研究。结果表明,"卢碧"的变性是中高纬度冷空气入侵的结果,变性过程伴随着西风槽的东移,高空急流的加强以及西太平洋副热带高压的减弱东撤。低纬度暖湿气流及较大的正涡度异常气旋式卷入台风环流,并在其东北侧积聚产生大量降水,释放的凝结潜热有利于北侧脊的加强,使得高空急流进一步加强,使得台风中心气压进一步降低。锋生、强降水伴随的潜热释放以及气旋中心气压之间存在着正反馈效应是"卢碧"变性后得以增强再发展的主要原因。热成风涡度梯度的分布对"卢碧"的变性增强过程具有较好的指示意义。 相似文献
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基于近似支持向量机的能见度释用预报研究 总被引：4，自引：0，他引：4
利用2008—2010年逐年12月、次年1月的T511L61数值预报产品和单站观测资料,采用近似支持向量机方法,分别建立了南京、杭州和衢州站分类和回归结合的能见度释用预报模型（简称分类和回归结合模型）。利用2011年12月、次年1月资料作为独立样本,对模型进行试报检验,并与不分类条件下的纯回归模型进行对比。结果表明:分类和回归结合模型的预报效果好于纯回归模型,在24、36、48、60和72 h试报中,分类和回归结合模型的南京、杭州和衢州三站平均的准确率依次为75.5%、83.7%、72.1%、75.4%和78.0%,在除48 h的其余4个预报时次中,分类和回归结合模型的三站平均的准确率均高于纯回归模型。分类和回归结合模型在单站能见度预报中有较好的应用前景。 相似文献