Influence of Tropical Cyclones on Hong Kong Air Quality

Tropical cyclones(TCs) constitute one of the major atmospheric activities affecting the air quality of the Pearl River Delta region. In this study, the impact of TCs on air quality in Hong Kong during the TC active season(July–October) from 2000 to 2015 is investigated. It is found that 57.5% of days with concentration of particulate matter with an aerodynamic diameter 10 μm(PM_(10)) above the 90th percentile are related to TC activity. TCs in three regions, located to the east, southeast, and southwest of Hong Kong, have obvious impacts on pollutant concentration. When TCs are located east of Hong Kong near Taiwan, 65.5%/38.7% of the days have high or extremely high PM_(10)/ozone(O_3) levels, which are associated with northerly wind, sinking motion, and relatively low precipitation. When TCs are located southeast of Hong Kong, 48.1%/58.2% of the days have high pollution levels, associated mainly with continental air mass transport. When TCs are south or west of Hong Kong, only 20.8%/16.9% of the days have high PM_(10)/O_3 levels, and the air quality in Hong Kong is generally good or normal due to TC-associated precipitation, oceanic air mass transport, and an enhanced rising motion. The higher chance of high O_3 days when TCs are present between Hong Kong and Taiwan, possibly due to lower-than-normal precipitation along the east coast of China under TC circulation. The results in this study highlight the important influence of TC position and associated atmospheric circulations on the air quality in Hong Kong.     

利用远震接收函数反演重庆地震台下方的速度结构

选取重庆地震台2010年至2012年记录的60个远震宽频带数字地震记录,采用频率域反褶积法获得台站的接收函数,采用H-Kappa叠加方法反演台站下方的地壳厚度和泊松比,作为台站下方波速反演的约束条件,以减少反演的非唯一性.计算结果显示,重庆地震台下方地壳厚度为42 km,与中国大陆中西部地区Moho面深度在38-45 km保持一致.该研究对增强该区的深部地质构造特征、分析孕震机制等具有积极意义.     

“海勘一号”钻机的研制

根据我国海岸带的特点,开发研制了“海勘一号”HK—1型全液压轻便钻机,介绍了该钻机的设计思路、主要技术参数、结构特点、试验及应用等情况。     

Revealing hidden persistence in maximum rainfall records

ABSTRACT

Clustering of extremes is critical for hydrological design and risk management and challenges the popular assumption of independence of extremes. We investigate the links between clustering of extremes and long-term persistence, else Hurst-Kolmogorov (HK) dynamics, in the parent process exploring the possibility of inferring the latter from the former. We find that (a) identifiability of persistence from maxima depends foremost on the choice of the threshold for extremes, the skewness and kurtosis of the parent process, and less on sample size; and (b) existing indices for inferring dependence from series of extremes are biased downward when applied to non-Gaussian processes. We devise a probabilistic index based on the probability of occurrence of peak-over-threshold events across multiple scales, which can reveal clustering, linking it to the persistence of the parent process. Its application shows that rainfall extremes may exhibit noteworthy departures from independence and consistency with an HK model.