基于潜标测量的海洋环境噪声谱特性分析

利用海洋环境噪声测量潜标系统对南海典型海域开展了为期3个月的海洋环境噪声测量,16通道海洋环境噪声测量系统每小时测量两分钟噪声信号。数据处理结果表明,800~5 000Hz范围内,噪声谱与风速相关性最好,且风速越大相关性越好,噪声谱与风速的相关性好于与浪高的相关性。风关噪声谱级在海水中部基本不随接收深度发生变化,但由于测量水听器阵长度未能覆盖整个水深,因此未给出海面和海底处谱级变化规律。在400Hz以上的高频段整个风速范围内噪声谱级都随风速发生变化,且噪声谱级与对数风速具有很好的线性关系。     

始新统油气系统:泰国湾东北部油气勘探新层系

泰国湾区域经历了前裂谷期、裂谷期、裂后期的构造演化阶段,形成了多个裂谷盆地。泰国湾区域东北部在渐新世经历了一次明显构造反转,较泰国湾区域大部分地区强烈。通过对比区内钻井,结合地震解释,对该区的沉积特征和构造演化进行了分析,认为这次反转构造导致了反转构造带上构造、沉积特征与邻区有较大的不同。由于这次反转构造,泰国湾东北部在新层系发育新类型的油气系统,即深部的始新统油气系统:烃源岩为中始新统湖相泥岩,储层为上始新统-渐新统三角洲相砂岩,盖层为下中新统三角洲前缘相泥岩和上中新统以上的海相泥岩。该油气成藏系统已被钻井钻遇油气显示,是本区有效油气成藏系统。     

基于Γ函数的暖季小时降水概率分布

我国暖季小时降水的气候概率分布特征分析是开展短时强降水概率预报的重要基础工作。本文使用1991—2009年5月1日至9月30日的小时降水资料,采用最大似然估计方法,对用于描述518个观测站点降水分布的Γ函数的形状参数α和尺度参数β进行了估算,对极端α和β分布情况下大于0.1 mm的暖季小时降水的概率密度分布状况及其累积概率密度分布函数进行了分析,并给出了多个站点基于Γ函数的超过给定阈值的降水累积概率的分布。结果表明:α和β之间的相关性高达0.975,其分布与我国的地势分布有很大的关系。Γ分布可以很好地描述小时降水的分布状况,模拟得到的结果具有更好的连续性,揭示了实况降水中不能观测到的极端降水发生的可能性;华南沿海和海南西北部为最容易出现短时强降水的区域,在有降水的情况下,其小时雨量超过10、20和30 mm的累积概率分别达到了8.0%、2.0%和0.7%,另一个常出现极端降水的区域为鲁苏皖交界处,这是强对流预报中值得注意的区域;95%累积概率密度对应的小时降水阈值分布显示,自西北向东南,极端小时降水的阈值不断增大;α与站点海拔高度之间具有很好的指数相关性,其相关系数达到了0.709,表明地形对我国暖季小时降水量的分布具有重要的影响。     

近红外CO2高光谱探测仪通道选择

近红外波段 (1.6 μm) 遥感可探测大气CO₂含量信息,应用于碳循环研究中。宽波段、高分辨率不但对仪器研制是一个挑战,而且巨大的数据量对观测的正演、反演也是一个挑战性课题。该文应用自由度及信息量分析法,对近红外高光谱波段中探测通道进行CO₂信息量分析,选择前20~100个高信息量的CO₂探测通道,并进行了反演模拟测试。结果表明:前20个高信息量通道占所有通道总信息量的76.4%,仅用所选的前20个通道进行反演,与所有通道参加反演的结果相比,误差增加0.3×10-6;通道数增至60时,信息量增加,通道数再增加,信息量则增加不显著;CO₂反演误差存在相似的关系。在高CO₂信息量分布上,弱吸收性质的1.6 μm波段和强吸收性质的2.06 μm波段表现出不同特点。     

大量气象数据小文件自适应优化传输

为满足气象实时资料传输中大量气象数据小文件的高时效传输需求,对其中的数据传输服务进行优化,提出一种基于实时网络状况的自适应数据传输优化方法。该方法采用优化网络传输协议并使用文件压缩技术,通过获取网络传输线路上的实时参数,实时调整压缩参数和网络传输参数以优化传输性能。在自适应压缩时,通过试验分析和归纳,确立了气象数据小文件标准为文件小于50 KB;根据网络实时状况,设计了基于实时网络状况自适应调整压缩等级的算法。在自适应传输参数调优中,研究了TCP缓冲区大小和TCP并发连接数在GridFTP协议中的重要性,针对实时网络状况,分别设计了自适应调整TCP缓冲区大小和TCP并发连接数的算法,算法提升传输性能65%。对以上提出各自适应参数调整算法进行试验验证表明,融合压缩和网络传输的自适应调优方法能显著提升气象小数据文件的传输性能近500倍。     

Signal acquisition of Luojia-1A low earth orbit navigation augmentation system with software defined receiver

Low Earth Orbit (LEO) satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System (GNSS) outage, or as ...     

Fusion of high-resolution DEMs derived from COSMO-SkyMed and TerraSAR-X InSAR datasets

Voids caused by shadow, layover, and decorrelation usually occur in digital elevation models (DEMs) of mountainous areas that are derived from interferometric synthetic aperture radar (InSAR) datasets. The presence of voids degrades the quality and usability of the DEMs. Thus, void removal is considered as an integral part of the DEM production using InSAR data. The fusion of multiple DEMs has been widely recognized as a promising way for the void removal. Because the vertical accuracy of multiple DEMs can be different, the selection of optimum weights becomes a key problem in the fusion and is studied in this article. As a showcase, two high-resolution InSAR DEMs near Mt. Qilian in northwest China are created and then merged. The two pairs of InSAR data were acquired by TerraSAR-X from an ascending orbit and COSMO-SkyMed from a descending orbit. A maximum likelihood fusion scheme with the weights optimally determined by the height of ambiguity and the variance of phase noise is adopted to syncretize the two DEMs in our study. The fused DEM has a fine spatial resolution of 10 m and depicts the landform of the study area well. The percentage of void cells in the fused DEM is only 0.13 %, while 6.9 and 5.7 % of the cells in the COSMO-SkyMed DEM and the TerraSAR-X DEM are originally voids. Using the ICESat/GLAS elevation data and the Chinese national DEM of scale 1:50,000 as references, we evaluate vertical accuracy levels of the fused DEM as well as the original InSAR DEMs. The results show that substantial improvements could be achieved by DEM fusion after atmospheric phase screen removal. The quality of fused DEM can even meet the high-resolution terrain information (HRTI) standard.     

Variability of Atlantic Meridional Overturning Circulation in FGOALS-g2简

The variability of Atlantic Meridional Overturning Circulation （AMOC） in the pre-industrial control experiment of the Flexible Global Ocean-Atmosphere-Land System model, Grid-point Version 2 （FGOALS-g2） was investigated using the model outputs with the most stable state in a 512-yr time window from the total 1500-yr period of the experiment. The period of AMOC in FGOALS-g2 is double peaked at 20 and 32 years according to the power spectrum, and 22 years according to an auto-correlation analysis, which shows very obvious decadal variability. Like many other coupled climate models, the decadal variability of AMOC in FGOALS-g2 is closely related to the convection that occurs in the Labrador Sea region. Deep convection in the Labrador Sea in FGOALS-g2 leads the AMOC maximum by 3-4 years. The contributions of thermal and haline effects to the variability of the convection in three different regions [the Labrador, Irminger and Greenland-Iceland- Norwegian （GIN） Seas] were analyzed for FGOALS-g2. The variability of convection in the Labrador and Irminger Seas is thermally dominant, while that in the colder GIN Seas can be mainly attributed to salinity changes due to the lower thermal expansion. By comparing the simulation results from FGOALS-g2 and 11 other models, it was found that AMOC variability can be attributed to salinity changes for longer periods （longer than 35 years） and to temperature changes for shorter periods.     

Chinese Contribution to CMIP5：An Overview of Five Chinese Models’Performances

An overview of Chinese contribution to Coupled Model Intercomparison Project-Phase 5 （CMIP5） is presented. The performances of five Chinese Climate/Earth System Models that participated in the CMIP5 pro ject are assessed in the context of climate mean states, seasonal cycle, intraseasonal oscillation, interan-nual variability, interdecadal variability, global monsoon, Asian-Australian monsoon, 20th-century historical climate simulation, climate change pro jection, and climate sensitivity. Both the strengths and weaknesses of the models are evaluated. The models generally show reasonable performances in simulating sea surface tem-perature （SST） mean state, seasonal cycle, spatial patterns of Madden-Julian oscillation （MJO） amplitude and tropical cyclone Genesis Potential Index （GPI）, global monsoon precipitation pattern, El Ni-no-Southern Oscillation （ENSO）, and Pacific Decadal Oscillation （PDO） related SST anomalies. However, the perfor-mances of the models in simulating the time periods, amplitude, and phase locking of ENSO, PDO time periods, GPI magnitude, MJO propagation, magnitude of SST seasonal cycle, northwestern Pacific mon-soon and North American monsoon domains, as well as the skill of large-scale Asian monsoon precipitation need to be improved. The model performances in simulating the time evolution and spatial pattern of the 20th-century global warming and the future change under representative concentration pathways pro jection are compared to the multimodel ensemble of CMIP5 models. The model discrepancies in terms of climate sensitivity are also discussed.