Statistical analysis of intensity variations in tropical cyclones in the East China Sea passing over the Kuroshio

Journal of Oceanology and Limnology - Intensity variations of the SE-NW-oriented tropical cyclones (TC) in the East China Sea (ECS) passing over the Kuroshio are studied using multi-year...     

最小二乘定权方法对流动重力计算结果的影响

在处理流动重力观测数据时,采用最小二乘方法解算由权系数和观测值组成的超定方程组,得到最优权值,并利用最优权计算重力值。结果表明,等权方法计算得到的结果点值精度不高,但包含了较全的重力异常地震分析预报信息;不等权方法计算得到的结果点值精度较高,但削弱了有用的重力异常地震分析预报信息;最小二乘方法计算得到的结果不但有较高的点值精度,对M_S4.0及以上地震的分析预报效果也最优。     

Source Evolution and Its Relationship to Climate Change Since the Middle–Late Pleistocene in Laizhou Bay,China

Through the geochemical analysis of two hundred-meters cores KD4 and ZK3 from Laizhou Bay,in this study,we determined the distribution law and controlling factors of the geochemical elements.We analyzed 24 elements with respect to their R factors and major principal components,which were combined with the source discrimination functions DFCr/Th and DFCa/Al to obtain the sediment source index and its variation with depth for this area.A comparison of the changes in climate indicators suggests a clear correlation between the source and climate changes.The results show that the Yellow River and surrounding short-term rivers are the main sediment sources in this area.The PC3 of the KD4 core and PC2 of the ZK3 core(e.g.,CaO,MnO,SiO2)exhibit significant variations and reflect the relative contributions of Yellow River sources.The deposition process can be divided into six stages:in Phase I(MIS 5c–MIS 5a),the Yellow River formed,and the composition of the Yellow River had a greater influence on the sedimentary composition of the study area.In Phase II(MIS 5a–MIS 3),the sediment sources of the Yellow River and the short-term streams in this area were wavering,with the sediments derived from short-term rivers playing a more important role.In Phase III(MIS 3),with a sharp drop in temperature,the study area was in the process of retreat,and the sediment source changed from the Yellow River to short-term rivers,after which the Yellow River source material remained the main sediment source for the region.A similar process occurred three more times in Phase IV(MIS 3–MIS 2),Phase V(MIS 2–MIS 1),and Phase VI(MIS 1).With changes in climate,especially during alternating sea-land phases,the sediment source varied in marine-terrestrial-marine phases,and the changes are observed as Yellow River source-surrounding provenance-Yellow River source.However,this process of change is not synchronized with the sea-land strata alternation.     

An Extreme Monsoonal Heavy Rainfall Event over Inland South China in June 2023: A Synoptic Causes Analysis

An extreme monsoonal heavy rainfall event lasted for nine days and recurred in the interior of northern south China from June 13 to 21, 2022. Using regional meteorological stations and ERA5 reanalysis data, the causes of this extreme monsoonal rainfall event in south China were analyzed and diagnosed. The results are shown as follows. A dominant South Asian high tended to be stable near the Qinghai-Tibet Plateau, providing favorable upper-level dispersion conditions for the occurrence of heavy rainfall in south China. A western Pacific subtropical high dominated the eastern part of the South China Sea, favoring stronger and more northward transport of water vapor to the northern part of south China at lower latitudes than normal. The continuous heavy precipitation event can be divided into two stages. The first stage (June 13-15) was the frontal heavy rainfall caused by cold air (brought by an East Asian trough) from the mid-latitudes that converged with a monsoonal airflow. The heavy rains occurred mostly in the area near a shear in front of the center of a synoptic-system-related low-level jet (SLLJ), and the jet stream and precipitation were strongest in the daytime. The second stage (June 16-21) was the warm-sector heavy rainfall caused by a South China Sea monsoonal low-level jet penetrating inland. The heavy rainfall occurred on the windward slope of the Nanling Mountains and in the northern part of a boundary layer jet (BLJ). The BLJ experienced five nighttime enhancements, corresponding well with the enhancement of the rainfall center, showing significant nighttime heavy rainfall characteristics. Finally, a conceptual diagram of inland-type warm-sector heavy rainfall in south China is summarized.     

城市扩张时空度量与差异性分析——以东、中、西部典型城市为例

城市化是区域人口增长与社会经济发展的综合进程,其最为明显的特征是城市空间扩张。研究采用面向对象最近邻分类以及概率神经网络分类的方法,从Landsat TM影像中提取我国东(广州)、中(合肥)、西部(西宁)城市主城区建设用地信息,应用皮尔逊卡方检验、香农熵模型以及城市扩张速度等计算3个城市相同时间段内的城市扩张自由度、蔓延度、感观优劣度等,对研究区1995—2010年期间的扩张特征进行时空综合度量。结果表明:1)不同城市扩张特征不尽相同,东(广州)、中部(合肥)城市相较西部(西宁)扩张自由度及蔓延度偏高,区域差异明显;2)东(广州)、中部(合肥)城市扩张感观优劣度相对较低,应努力构建并完善城市扩张监管体系,统筹协调城市建设用地的开发利用,积极推进高质量城镇化,实现城市的可持续发展;3)东(广州)、中部(合肥)相较西部(西宁)近15年扩张明显,扩张速度较快。     

基于蚁群算法的道路选取模型研究

在制图综合中,道路选取是非常重要的内容之一,研究道路选取的智能化方法是非常必要的。文中在研究道路语义、几何特征、拓扑关系和结构特征的基础上,充分考虑居民点对道路选取过程的影响,建立道路选取的蚁群算法模型。通过实验证明该模型算法的可行性和有效性。     

夏季南亚高压与邻近上对流层下平流层区水汽变化的联系

利用1979-2015年ERA-interim月平均再分析资料,分析了夏季南亚高压（SAH）与邻近上对流层下平流层（UTLS）区水汽空间分布特征,讨论了二者的相关关系和因果联系。结果表明:（1）在对流层上层,水汽大值区位于南亚高压的东南侧,并随高度升高向西北倾斜到100 hPa,水汽大值中心基本位于南亚高压中心附近。（2）南亚高压偏强（弱）时,南亚高压东部UTLS区水汽显著偏多（少）,而南亚高压西北部水汽异常不显著。（3）南亚高压偏强（弱）时南亚高压中部UTLS区水汽偏多（少）可能与南亚高压对水汽的抽吸和对水汽输送屏障有关。（4）而南亚高压东南侧UTLS区水汽偏多（少）时南亚高压偏强（弱）可能与深对流输送的水汽潜热释放有关。      

Developing the science product algorithm testbed for Chinese next-generation geostationary meteorological satellites: Fengyun-4 series

Fengyun-4A (FY-4A), the first of the Chinese next-generation geostationary meteorological satellites, launched in 2016, offers several advances over the FY-2: more spectral bands, faster imaging, and infrared hyperspectral measurements. To support the major objective of developing the prototypes of FY-4 science algorithms, two science product algorithm testbeds for imagers and sounders have been developed by the scientists in the FY-4 Algorithm Working Group (AWG). Both testbeds, written in FORTRAN and C programming languages for Linux or UNIX systems, have been tested successfully by using Intel/g compilers. Some important FY-4 science products, including cloud mask, cloud properties, and temperature profiles, have been retrieved successfully through using a proxy imager, Himawari-8/Advanced Himawari Imager (AHI), and sounder data, obtained from the Atmospheric InfraRed Sounder, thus demonstrating their robustness. In addition, in early 2016, the FY-4 AWG was developed based on the imager testbed—a near real-time processing system for Himawari-8/AHI data for use by Chinese weather forecasters. Consequently, robust and flexible science product algorithm testbeds have provided essential and productive tools for popularizing FY-4 data and developing substantial improvements in FY-4 products.     

Contributions of thermoelastic deformation to seasonal variations in GPS station position

GPS Solutions - We investigate surface displacements due to land temperature variation with the 2014 global thermoelastic model, which is a solution on a uniformly elastic sphere under the...     

Land surface temperature and its impact factors in Western Sichuan Plateau,China

The understanding influence of multiple factors variations on land surface temperature (LST) remains elusive. LST was retrieved by the atmospheric correction algorithms. Based on the correlation coefficients, stepwise regression analysis was developed to examine how multiple factors variability led to LST variations. The differences in LST between impact factors vary depending on time in a day. The elevation and land use types significantly affect the LST in sunny slope or shadow areas has a significantly quadratic curve correlation or a negative linear correlation with it, the influence of slope and aspect is not very significant. LST for forestland, grassland and bare land in the sunny slope and shadow area was the cubic polynomial related to its elevation. Normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) effectively express LST in mountainous. LST and NDMI or NDVI have a significantly negative correlation, NDMI is more effective and more applicable for the expression of LST.