河北省重污染天气分型及其预报概念模型研究     

王琼  沈瑞珊  庞杨 《气象与环境科学》2020,43(4):107-114

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均一化前后河南省年平均最高气温和高温日数的对比分析     

左璇  姬兴杰 《气象与环境科学》2020,43(4):38-43

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湖北省1961-2012年大气稳定度、混合层厚度和大气环境容量系数的变化特征     

高媛  任永建  吴蓉 《气象与环境科学》2020,43(4):44-50

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中国龙卷气候特征和环境条件研究进展综述     

郑永光 《气象科学进展》2020,10(6)

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安徽省酷热天气分析及预报指标          下载免费PDF全文

张娇  王东勇  童金  余金龙  徐怡  郑淋淋  陶玮 《大气科学学报》2020,43(2):405-415

基于安徽省1961-2017年逐日地面最高气温资料,采用Mann-Kendall法对安徽省高温天气事件进行突变分析,发现安徽省2000年后高温事件明显增加。为分析安徽省酷热天气特征和产生机理,文中挑选了35~37℃高温天气个例对比分析。结果发现:1)500 hPa西太平洋副高位置和850 hPa气温对酷热天气预报的指示性最好。2)受西太平洋副热带高压不同位置控制,安徽省增温机制不同:当为高压中心控制时,太阳辐射在增温过程中起决定性作用,安徽省易出现酷热天气;当高压中心位于海上,脊线位于安徽省附近时,安徽省高温强度较弱。通过酷热天气个例研究和合成平均分析,文中总结了安徽省酷热天气预报指标。  相似文献   

1980～2017年四川盆地寒潮及其气温变化特征          下载免费PDF全文

肖红茹  龙柯吉  伍清  蒋兴文 《高原山地气象研究》2020,40(4):47-52

利用“中国地面气候资料日值数据集(V3.0)”1980～2017年的地面日平均气温、最高气温和最低气温资料,按照四川盆地寒潮业务标准统计分析了38年四川盆地出现的77次区域寒潮特征及其日平均气温、最高气温和最低气温的变化特征,结果发现:近38年四川盆地区域寒潮频次呈不显著的增加趋势,增速为0.18次/10a,而强度呈显著增强趋势,增速为1.14℃/10a;平均最高气温和最低气温均表现为弱的升高趋势,且冬季比春、秋季升温趋势显著。寒潮天气过程中24h内主要表现为最高气温的下降,最低气温普遍下降不明显且近50%的站点趋于上升;最高气温累计降幅,春、秋季明显大于冬季,而最低气温累计降幅季节差异不大。给出72h气温累计变化趋势,类同24h。   相似文献   

论VAM的适宜时间   总被引：1，自引：1，他引：0  

蒋利龙  易又庆 《测绘工程》2005,14(1):51-54

垂直角观测(vAM)的质量与作业时间有关.通过对大气折光角及其起伏的日变化规律的分析,提出了一种根据动态照准误差确定vAM的有利与不利时间的方法.与传统的观点相比较,采用该法确定的适宜时间将具有更高的参考价值.  相似文献   

合肥市郊低层大气的激光雷达探测研究   总被引：7，自引：1，他引：6  

袁松  辛雨  周军 《大气科学》2005,29(3):387-395

利用L300米散射激光雷达对合肥市郊大气边界层进行长期系统观测.分析讨论了大气边界层气溶胶消光系数与温度、湿度的关系, 大气边界层气溶胶消光系数垂直分布和时间变化的主要特征, 给出了激光雷达探测的大气边界层高度的统计特征及其与无线电气象探空仪探测大气边界层高度的比较结果.  相似文献   

Retrieving atmospheric temperature profiles from AMSU-A data with neural networks   总被引：5，自引：0，他引：5  

YAO Zhigang  CHEN Hongbin  LIN Longfu 《大气科学进展》2005,22(4):606-616

Back propagation neural networks are used to retrieve atmospheric temperature profiles from NOAA-16 Advanced Microwave Sounding Unit-A (AMSU-A) measurements over East Asia. The collocated radiosonde observation and AMSU-A data over land in 2002-2003 are used to train the network, and the data over land in 2004 are used to test the network. A comparison with the multi-linear regression method shows that the neural network retrieval method can significantly improve the results in all weather conditions. When an offset of 0.5 K or a noise level of ±0.2 K is added to all channels simultaneously, the increase in the overall root mean square (RMS) error is less than 0.1 K. Furthermore, an experiment is conducted to investigate the effects of the window channels on the retrieval. The results indicate that the brightness temperatures of window channels can provide significantly useful information on the temperature retrieval near the surface. Additionally, the RMS errors of the profiles retrieved with the trained neural network are compared with the errors from the International Advanced TOVS (ATOVS) Processing Package (IAPP). It is shown that the network-based algorithm can provide much better results in the experiment region and comparable results in other regions. It is also noted that the network can yield remarkably better results than IAPP at the low levels and at about the 250-hPa level in summer skies over ocean. Finally, the network-based retrieval algorithm developed herein is applied in retrieving the temperature anomalies of Typhoon Rananim from AMSU-A data.  相似文献   

Climate-Induced Variability of Sea Level in Stockholm： Influence of Air Temperature and Atmospheric Circulation     

Deliang CHEN  Anders OMSTEDT 《大气科学进展》2005,22(5):655-664

This study is focused on climate-induced variation of sea level in Stockholm during 1873-1995. After the effect of the land uplift, is removed, the residual is characterized and related to large-scale temperature and atmospheric circulation. The residual shows an overall upward trend, although this result depends on the uplift rate used. However, the seasonal distribution of the trend is uneven. There are even two months （June and August） that show a negative trend. The significant trend in August may be linked to fresh water input that is controlled by precipitation. The influence of the atmospheric conditions on the sea level is mainly manifested through zonal winds, vorticity and temperature. While the wind is important in the period January-May, the vorticity plays a main role during June and December. A successful linear multiple-regression model linking the climatic variables （zonal winds, vorticity and mean air temperature during the previous two months） and the sea level is established for each month. An independent verification of the model shows that it has considerable skill in simulating the variability.  相似文献