Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

Delayed geochemical hazard: Concept, digital model and case study

Delayed Geochemical Hazard (DGH briefly) presents the whole process of a kind of serious ecological and environmental hazard caused by sudden reactivation and sharp release of long-term accumulated pollutant from stable species to active ones in soil or sediment system due to the change of physical-chemical conditions (such as temperature, pH, Eh, moisture, the concentrations of organic matters, etc.) or the decrease of environment capacity. The characteristics of DGH are discussed. The process of a typical DGH can be expressed as a nonlinear polynomial. The points where the derivative functions of the first and second orders of the polynomial reach zero, minimum and maximum are keys for risk assessment and harzard pridication.The process and mechanism of the hazard is due to the transform of pollutant among different species principally. The concepts of "total releasable content of pollutant", TRCP, and "total concentration of active specie", TCAS, are necessarily defined to describe the mechanism of DGH. The possibility of the temporal and spatial propagation is discussed. Case study shows that there exists a transform mechanism of "gradual release" and "chain reaction" among the species of the exchangeable and the bounds to carbonate, iron and manganese oxides and organic matter, thus causing the delayed geochemical hazard.     

沙坡头没蒿和柠条叶片δ13C 和 N 含量的季节变化特征

通过对腾格里沙漠东南缘沙坡头人工固沙区油蒿和柠条叶片稳定碳同位素分辨率（△）和N 含量的测定，研究了不同种植方式下油蒿和柠条叶片△ 和 N 含量的季节变化及其关系。结果表明：在不同种植方式下，两种植物的△ 与 N 含量在不同月份之间均存在显著差异。油蒿叶片 △ 极显著高于柠条的：在不同种植方式下，油蒿单种下 △ 显著高于混种，柠条则反之。 油蒿叶片 N 含量极显著低于柠条的：不同种植方式下柠条叶片 N 含量无显著差异，而单种油蒿叶片 N 含量则显著低于混种。在 △ 与 N 含量的关系中，柠条叶片 △ 与 N 在两种生境、单种和混种下均极显著正相关，雨明柠条叶片 N 含量可以作为其 △ 与 N 含量的季节变化及其关系在不同微生境有所差异。     

基于小波包变换特征向量提取的隐蔽水声通信性能评价方法研究

针对水声通信系统的隐蔽性缺乏客观、有效的评价手段,提出一种基于小波包变换提取信号特征向量并比较特征向量相似度的信号隐蔽性评价算法。为了区别截获信号和环境噪声,考虑从能量分布特征的角度,提取截获信号的特征参数。所提算法利用小波包变换具有更高的时频分辨率、自适应选择频带等特点,获取信号的小波包能量占比、小波包能量熵、小波包尺度熵,采用上述 3 个特征参数组成特征向量和利用余弦相似度算法判别信号隐蔽性。仿真实验和海试结果表明,所提方法得到的信号隐蔽性能基本符合实际信号隐蔽性能随信噪比变化规律,为隐蔽水声通信系统评估设计和水声对抗性能评级提供有效参考。     

基于多模态感知的水下目标检测应用构想

水下目标检测在海洋生物研究、考古探索、军事防御等多领域广泛应用,随着人工智能快速发展,水下目标检测也朝着无人化、智能化发展。深度学习采用神经网络挖掘信息特征,在速度和精度上均表现出优异的性能,成为了计算机视觉技术的主流算法,然而水下环境复杂,将其应用于水下图像目标检测仍存在较大的挑战。水下目标各模态信息互补,特征丰富,有利于目标检测识别,因此结合应用场景调研现有技术,然后设计基于深度学习的多模态水下目标检测系统,同时对比分析了现有关键技术的优缺点,最后对多模态目标检测系统未来发展进行总结与展望,具有重要意义。     

基于YOLO算法的海洋中尺度涡三维结构构建

海洋中尺度涡是一种常见的中尺度海洋现象,研究海洋中尺度涡的分布及运动特性对航运、气候、军事等具有重要作用,海洋中尺度涡的识别是海洋学和计算机科学领域的一个热门研究课题。运用深度学习的方法和框架,对中尺度涡的二维识别和三维结构构建展开研究分析。首先,获取全球海洋再分析数据并进行流线可视化,构建涡旋流线数据集;其次,利用YOLO v5s卷积神经网络对涡旋流线数据集进行训练,并对南海区域中尺度涡进行有效检测。实验结果表明,YOLO v5s训练后得到最优模型经过测试,平均检测精度均值达到了86.10%;最后,根据涡旋检测结果,对检测出的同时刻不同深度的涡旋判断是否属于同一涡旋,确定后进行该涡旋的三维结构构建。     

采用泵喷补偿技术的近底拖体水压场数值模拟研究

采用标准 k-ε 模型,结合 Fluent 软件,对采用泵喷补偿技术的拖体水下被拖行运动所产生的压力场变化进行了数值模拟实验。结果表明：采用泵喷补偿技术对拖体水压场的“马鞍形”分布特性影响较小, 但随着喷水速度的增加,拖体首端压力负值略有增加,而中段的负峰幅值显著增加,当喷水速度达到一定值后,拖体的压力特性曲线已经较为接近期望值,证明泵喷补偿技术具有一定的可行性。     

充分利用海洋环境提升声呐装备性能的研究

声呐装备的性能发挥与海洋环境密切相关。研究海洋环境对声呐装备的影响,积极推动环境适配型声呐装备的发展,对于综合利用水下探测和通信手段,提升我国水下信息化能力具有重要意义。着重分析典型海洋环境和过程对声呐性能的影响,包括不规则海底边界、海水的介质特性以及多尺度海洋动力过程, 通过算例对比说明环境效应对声呐的影响。进而,针对声呐装备的使用和发展,围绕环境适应与适配提出相关建议与措施。     

基于ROSE2.0的普洱地区CINRAD/CC雷达冰雹探测算法评估及参数本地化

为提高冰雹探测算法（Hail Detection Algorithm,HDA）产品的可用性,针对2015—2020年普洱地区监测到的22次冰雹个例,利用新一代雷达业务应用软件ROSE2.0对相关雷达基数据进行回放及产品分析,以命中率、虚警率、临界成功指数为指标对HDA算法在普洱地区的识别效果进行评估并给出本地化参数配置方案。结果表明:HDA算法在普洱地区命中率接近100%,但虚警现象非常普遍,使用强冰雹概率（Probability of Severe Hail,POSH）的预警效果优于任意大小冰雹概率（Probability of Hail,POH）,且冰雹尺寸越大POSH虚警的概率越低。进一步使用模拟测评法对POSH算法的适配参数进行分析,发现正确输入降雹日当天的0 ℃层和-20 ℃层高度能有效减少POSH的虚警率及提高临界成功指数;同时使算法预测的最大冰雹直径普遍偏大的情况得到控制,其中,中小冰雹直径偏离百分比减小76.07%,改善效果显著高于大冰雹。此外,增大反射率因子及POSH阈值能有效控制虚警,但也导致漏报次数快速增加,当阈值太大时命中率明显降低,为了保证较高的命中率和临界成功指数,选择Z=50 dBz或POSH=70%为阈值能明显改善HDA算法的识别效果。     

Effect of the Initial Vortex Structure on Intensity Change During Eyewall Replacement Cycle of Tropical Cyclones: A Numerical Study

This study investigates the effect of the initial tropical cyclone (TC) vortex structure on the intensity change during the eyewall replacement cycle (ERC) of TCs based on two idealized simulations using the Weather Research and Forecasting (WRF) model. Results show that an initially smaller TC with weaker outer winds experienced a much more drastic intensity change during the ERC than an initially larger TC with stronger outer winds. It is found that an initially larger TC vortex with stronger outer winds favored the development of more active spiral rainbands outside the outer eyewall, which slowed down the contraction and intensification of the outer eyewall and thus prolonged the duration of the concentric eyewall and slow intensity evolution. In contrast, the initially smaller TC with weaker outer winds corresponded to higher inertial stability in the inner core and weaker inertial stability but stronger filamentation outside the outer eyewall. These led to stronger boundary layer inflow, stronger updraft and convection in the outer eyewall, and suppressed convective activity outside the outer eyewall. These resulted in the rapid weakening during the formation of the outer eyewall, followed by a rapid re-intensification of the TC during the ERC. Our study demonstrates that accurate in- itialization of the TC structure in numerical models is crucial for predicting changes in TC intensity during the ERC. Additionally, monitoring the activity of spiral rainbands outside the outer eyewall can help to improve short-term intensity forecasts for TCs experiencing ERCs.