Analysis of Continuous Thunderstorm in One Week at Qingdao Airport Based on Sounding Data

Continuous thunderstorm occurring at Qingdao Airport in China from August 7 to 13,2022 was analyzed based on sounding data.The weather was divided into thunderstorm gale,thunderstorm and heavy precipitation,and some physical quantities and time variables which can effectively identify severe convective weather types were preliminarily obtained.The results show that CAPE was sensitive to different types of weather,but the uncertainty was relatively large.Convective temperature T_(CON),temperature difference between 500 and 850 hPa,and vertical wind shear can distinguish thunderstorm gale,thunderstorm and heavy precipitation weather obviously.Besides,K index,Showalter index,θ_(se) difference between 500 and 850 hPa were also important basis to distinguish thunderstorm and heavy precipitation weather.Thunderstorm gale can be distinguished by the 24-hour variations of K index,and the difference of dew point between 500 and 850 hPa.The 24-hour variations of(T-T_(d))_(500) and vertical wind shear can be used to distinguish between heavy precipitation and thunderstorm weather;the 24-hour variation of stratification stability Δθ_(se) can distinguish the three kinds of weather well.For the wind field,the existence of vertical wind shear was required for strong convective weather,and the 24-hour increment of thunderstorm gale and thunderstorm was larger than that of heavy precipitation.

Study on the detection of abnormal sounding data based on LS-SVM

A new method of detecting abnormal sounding data based on LS-SVM is presented.The theorem proves that the trend surface filter is the especial result of LS-SVM.In order to depict the relationship of trend surface filter and LS-SVM,a contrast is given.The example shows that abnormal sounding data could be detected effectively by LS-SVM when the training samples and kernel function are reasonable.

Deep Learning-based Environmental Sound Classification Using Feature Fusion and Data Enhancement

Environmental sound classification(ESC)involves the process of distinguishing an audio stream associated with numerous environmental sounds.Some common aspects such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording make the ESC task much more complicated and complex.This research is to propose a deep learning model to improve the recognition rate of environmental sounds and reduce the model training time under limited computation resources.In this research,the performance of transformer and convolutional neural networks(CNN)are investigated.Seven audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted fromtheUrbanSound8K,ESC-50,and ESC-10,databases.Moreover,this research also employed three data enhancement methods,namely,white noise,pitch tuning,and time stretch to reduce the risk of overfitting issue due to the limited audio clips.The evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on enhanced database.For UrbanSound8K,ESC-50,and ESC-10,the highest attained accuracies are 0.98,0.94,and 0.97 respectively.The experimental results reveal that the proposed technique can achieve the best performance for ESC problems.

Shallow Discussion about the Application of L-band Sounding Seconds Data in the Artificial Precipitation

[Objective] The research aimed to discuss shallowly the application of L-band sounding seconds data in the artificial precipitation. [Method] The characteristics, getting manner and displaying method of L-band sounding seconds data were introduced briefly. Moreover, its application prospect in the artificial precipitation operation was analyzed initially. We aimed to improve its application rate in the artificial precipitation operation. [Result] L-band sounding seconds data had the great improvement in the time-space resolution and the space positioning accuracy aspects when compared with the previous sounding data, and the precision reached the second level. It could provide the high-precision data basis for the assimilation of artificial precipitation numerical model initial field, and improve the numerical model. Moreover, the sounding product could provide the accurate scientific basis for the selection of artificial precipitation operation tool, the determination of operation height and range, and guide the artificial precipitation operation, and improve the operation efficiency. [Conclusion] The research provided the analysis and reference basis for the command of artificial precipitation operation.

A New Infrared Atmospheric Sounding Interferometer Channel Selection and Assessment of Its Impact on Met Office NWP Forecasts

A new set of Infrared Atmospheric Sounding Interferometer （IASI） channels was re-selected from 314 EUMETSAT channels. In selecting channels, we calculated the impact of the individually added channel on the improvement in the analysis outputs from a one-dimensional variational analysis （1D-Var） for the Unified Model （UM） data assimilation system at the Met Office, using the channel score index （CSI） as a figure of merit. Then, 200 channels were selected in order by counting each individual channel＇s CSI contribution. Compared with the operationally used 183 channels for the UM at the Met Office, the new set shares 149 channels, while the other 51 channels are new. Also examined is the selection from the entropy reduction method with the same 1D-Var approach, Results suggest that channel selection can be made in a more objective fashion using the proposed CSI method. This is because the most important channels can be selected across the whole IASI observation spectrum. In the experimental trial runs using the UM global assimilation system, the new channels had an overall neutral impact in terms of improvement in forecasts, as compared with results from the operational channels. However, upper-tropospheric moist biases shown in the control run with operational channels were significantly reduced in the experimental trial with the newly selected channels. The reduction of moist biases was mainly due to the additional water vapor channels, which are sensitive to the upper-tropospheric water vapor.

Reconstructing Sound Speed Profiles with Sea Surface Data

Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, thesea surface data could be obtained in near-real time. Typically, the subsurface fields are correlated with the sea surfaceparameters. Thus, the SSPs could be obtained by means of satellite remote sensing. In this paper, the history as wellas the current research over the reconstruction of subsurface fields by means of sea surface data is introduced. Thentwo methods to reconstruct the SSPs with sea surface data, including the linear regression method using the empiricalorthogonal function, and the self-organizing method based on the big data theory, are described in detail in the paper.

多导睡眠监测设备和肠鸣音设备之间数据同步采集方法研究

目的提出一种基于敲击脉冲信号的两个独立设备数据同步采集的方法,以实现数据的同步采集。方法利用多导睡眠监测设备的鼾声传感器与肠鸣音设备相互敲击产生瞬态脉冲信号作为同步标记信号,基于同步标记信号的三阶累积量实现同步标记信号的识别和对齐,并采用多组同步标记信号评价数据同步的精度。结果多导睡眠监测设备的鼾声传感器和肠鸣音设备相互敲击,产生了清晰明确的同步标记脉冲信号,结合时域特征和三阶累积量实现了同步标记信号的精确识别和对齐。经过多组同步标记信号识别验证表明,此种同步方法可保证同步精度范围在5 ms内。结论该数据同步采集方法简单易行,可保证多导睡眠监测设备和肠鸣音设备的高精度同步采集,同时也为其他完全独立的多个设备数据同步采集提供参考。

基于地形趋势面的边缘波束数据处理方法研究

受多波束特点及水体环境的影响,多波束边缘波束的数据质量低于中央波束的数据质量。通过分析多波束边缘波束测深误差的影响因素,提出了通过相邻条带中央波束的水深数据获取地形趋势面来提高边缘波束的水深数据质量。在此基础上,推导出了水底地形的曲线拟合公式和边缘波束测深点误差改正公式,并采用了多波束实测数据进行了验证。结果表明:使用该方法处理后的多波束边缘波束水深数据质量明显提高,水深数据更接近真实水深,有效削弱了多波束边缘波束的测深误差,对如何提高多波束的边缘波束数据质量有指导作用。研究成果可应用于多波束数据处理软件的深度研发和多波束实测数据后处理。

基于时频融合特征的肺动脉高压心音分类模型

先心病相关肺动脉高压是一种严重的心血管疾病,致死率高,对其进行早期筛查与识别对于治愈尤为重要。目前临床是通过右心导管术确诊,此为有创检查,不便于在大规模筛查中采用,研究一种无创便捷的识别方法迫在眉睫。文中建立了一种时频融合的心音分类模型。首先对心音信号进行预处理,然后使用融合滤波器组对信号进行转换并求取动态时频特征,最后将得到的融合特征参数输入表格式先验数据拟合网络(TabPFN)中进行分类识别。实验结果表明,该算法在正常、CHD-PAH和CHD中的平均准确率、精确率、灵敏度、特异度和F1分别为92.21%,92.15%,92.15%,96.11%,92.14%。对于先心病相关肺动脉高压的早期筛查与识别具有重要意义。

The Earth Summit Mission-2022:Successful ozone soundings contribute to source identification in the north Mt.Qomolangma region

As part of“The Earth Summit Mission-2022”during the second Tibetan Plateau Scientific Expedition and Research(STEP)in April and May 2022,we conducted the ozone sounding experiment(an ozonesonde mated to a radiosonde)at Mt.Qomolangma Base Camp(MQBC;86.85°E,28.14°N;5200 m),a location at an extremely high altitude.A total of ten sounding profiles were obtained between April 30 and May 06,2022,of which seven profiles were above35 km in altitude,with a maximum detection altitude up to 39.0 km.This study presents the temporal variation and vertical distributions of atmospheric temperature,humidity,and ozone during the MQBC campaign.The averaged ozone concentration was high(68.3 ppbv)at the surface and then increased smoothly until peaking(~110 ppbv)in the middle troposphere(approximately 10 km),and afterward,the ozone concentration increased rapidly from the upper troposphere to a maximum of~10 ppmv at~30 km.The enhanced ozone concentration in the middle troposphere was associated with the blocking high pressure,and transport from the southern flank of the Himalayas occurred during the campaign period.The average total ozone column was 291.9±21.4 DU for the seven profiles exceeding 35km in altitude.The ozonesonde measurements were also compared with the vertical ozone profiles retrieved from the space-borne ozone products from the Microwave Limb Sounder(MLS)onboard the Aura satellite and the Atmospheric Infrared Sounder(AIRS)onboard the Aqua satellite.

拉曼雷达重叠函数的模拟与校准

重叠函数的校准对地基激光雷达低空大气探测的准确性至关重要。目前被广泛用于校准拉曼激光雷达重叠函数的双通道实验标定法,需要满足弹性散射通道与氮气拉曼散射通道的重叠函数近似相等的前置条件。但实际仪器光路往往会偏离理想状态,使得该前置条件无法得到满足,导致校准失败。本文使用光线追踪法模拟重叠函数,计算了各种光路失调情况下弹性散射通道与氮气拉曼散射通道的比值,并引入了一种镜头遮蔽实验来评估双通道实验标定法的前置条件是否得到满足。数值模拟结果表明,当雷达接收面的不同象限被遮蔽时,如果弹性散射通道与氮气通道的信号强度比值基本保持不变,则满足前置条件;如果弹性散射通道与氮气通道的信号强度比值有显著差异,则可判定仪器光学系统失调,需要调整直至满足上述前置条件时才能使用双通道实验标定法标定。利用该方法对南京市气象局安装的拉曼激光雷达进行了光路测评和调整,并与CCD(charge-coupled device)侧向成像激光雷达观测信号做对比,结果显示调整后的定标效果更好。

顾及多因子的中国区域加权平均温度模型

针对中国地区地形起伏较大等情况影响T m值特别是短期T m值的高精度获取的问题,提出一种顾及多因子的大气加权平均温度模型。首先,通过探空数据利用积分法计算出加权平均温度,拟合分析选出特征因子加入建模;其次,将利用积分法计算的探空数据与NCEP数据分别作为参考值,引入Bevis模型、GPT3-Z模型和时空模型,将模型计算的预测值和参考值作对比,分析各模型精度;之后,将各模型用于水汽反演,分析反演精度。结果表明:多因子T m模型反演的T m值精度最高,且多因子T m模型中加入了日周期变化,相比全年在短期时间内提升更加明显;将各模型用于GNSS水汽计算,多因子T m模型精度最好,理论RMS误差和相对误差分别为0.23 mm和1.69%。

基于域泛化的工业设备无监督异常声音检测算法

在工业场景中,因为设备异常现象的罕见性和高度多样化,以及机器的操作条件或环境噪声在训练和测试阶段的不同,会改变训练和测试数据之间的声学特性。为解决上述问题,提出一种基于联合深度学习和变分贝叶斯高斯混合模型的无监督异常声音检测算法。通过两种神经网络联合训练进行信息提取,并利用变分贝叶斯高斯混合模型对其所获得的嵌入进行聚类分析;引入一种新的混合示例数据增强方法,用多种方式相结合的替代方法来生成示例,以对齐不同域之间的分布;应用了一种改进的子集群AdaCos损失函数,以排除潜在的异常值。实验结果表明,该方法在三种工业机器类型的数据集上目标域的平均曲线下面积达到了79.03%,平均F 1分数达到了67.23%;对比基线模型,谐波平均值提升约20%,在工业设备无监督异常声音检测中表现良好。

基于MobileNetV3卷积神经网络的供水管道漏损音频分类

为了对城市供水管网漏损音进行准确识别,提出一种基于MobileNetV3的供水管道漏损音频分类识别方法。首先将ROPP数据集中的音频文件进行离线数据增强,将漏损信号转变为对数梅尔谱图并采用谱减法实现数据降噪;然后使用注意力机制模块与MobileNetV3网络训练识别并提取图像特征;最后使用Softmax函数对漏损音频进行分类。实验结果表明,该方法可以使漏水类别的分类精确度达到99.40%,召回率达到99.20%。

顾及多因子的江苏省大气加权平均温度精化模型

加权平均温度(T_m)是GNSS可降水量反演过程中的关键变量。基于江苏省徐州、射阳和南京这3个探空站18a的实测探空数据(2000—2017年),对T_m与地表温度T_s、地表气压P、地表水汽压e以及地表水汽压对数形式ln e之间的相关性进行分析,采用最小二乘法构建江苏省的T_m经验模型:单因子模型T_(m1)和多因子模型T_(m2)、T_(m3)。采用2018—2019年探空数据作为对未来预测的数据,对3种本地化模型的精度进行评估。结果表明3种本地化模型的精度均高于Bevis模型,且多因子模型T_(m3)的精度高于多因子模型T_(m2)和单因子T_(m1)的精度,该模型的精度比Bevis模型提高了16.8%。

微波辐射计与探空观测数据对比分析

为了评估RPG-HATPRO微波辐射计的探测性能,文章对比分析了广东省清远市探空观测站的探空观测数据与微波辐射计数据,通过比较不同观测高度、不同天气条件下的数据质量分析仪器性能。结果表明:RPG-HATPRO微波辐射计边界层温度廓线的有效探测高度为2000 m,对流层湿度廓线的有效探测高度为10000 m;微波辐射计的对流层温度数据探测误差分布随高度增加呈曲折上升趋势;降水条件对微波辐射计温度、湿度数据准确性有一定影响,在无降水条件下,微波辐射计湿度数据的准确性更为稳定。研究结果为微波辐射计的更有效应用提供支持。

无人测深船在内河航道管理中的应用

论述了无人测深船引入内河应用的背景和必要性。为大力开展智慧航道建设,提高内河航道管养水平,缓解内河航道水深检测、疏浚、巡航、航道图数据更新等的工作管养压力,结合郁江南宁市区航道条件,开展无人测深船应用试验,分析在不同水情环境下的试验效果,探讨了无人测深船在内河应用的优势和发展方向。

基于探空数据探讨河套地区层云、层积云降水的物理量特征

基于2019年内蒙古河套地区雷达、卫星探测资料,将降水过程分为层云降水和层积云降水,并基于L波段探空秒数据反演云的垂直结构。得到如下结论:(1)层云降水和层积云降水大多为1~2层云。降水云为1层云时,层云降水和层积云降水的云底均在2.0 km左右;但是,层积云降水的云厚明显大于层云降水。降水云为2层云时,层云降水和层积云降水的云底也均在2.0 km左右;但是,层积云降水的下层云厚度大于层云降水,总厚度也明显大于层云降水,夹层更薄。(2)降水云为层云时,云系的整层可降水量为26.2 mm,总指数为41.91℃,K指数为28.32℃,抬升凝结温度为10.00℃,对流有效位能为59.39 J·kg^(-1),云系稳定。降水云为层积云时,云系的整层可降水量为32.1 mm,总指数为42.03℃,K指数为32.29℃,抬升凝结温度为12.61℃,对流有效位能为103.09 J·kg^(-1),云系比较稳定。比较而言,降水云系为层积云时更多,且整层可降水量更多,更适宜开展人工增雨作业。

FY-3A SATELLITE MICROWAVE DATA ASSIMILATION EXPERIMENTS IN TROPICAL CYCLONE FORECAST

China's new generation of polar-orbiting meteorological satellite FY-3A was successfully launched on May 26,2008,carrying microwave sounding devices which had similar performance to ATOVS of NOAA series.In order to study the application of microwave sounding data in numerical prediction of typhoons and to improve typhoon forecasting,we assimilated data directly for numerical forecasting of the track and intensity of the 2009 typhoon Morakot(0908)based on the WRF-3DVar system.Results showed that the initial fields of the numerical model due to direct assimilation of FY-3A microwave sounding data was improved much more than that due to assimilation of conventional observations alone,and the improvement was especially significant over the ocean,which is always without conventional observations.The model initial fields were more reasonable in reflecting the initial situation of typhoon circulation as well as temperature and humidity conditions,and typhoon central position at sea was also adjusted.Through direct 3DVar assimilation of FY-3A microwave data,the regional mesoscale model improves the forecasting of typhoon track.Therefore,the FY-3A microwave data could efficiently improve the numerical prediction of typhoons.

基于EMD-XGBoost模型的交流输电线路可听噪声有效数据预测方法研究

背景噪声的存在使得采集输电线路可听噪声时会得到大量的无效数据,影响了可听噪声作为输电线路设计标准时的准确性。为此在夜间收集了可听噪声A声级、可听噪声5000 Hz以上分量及气象因素,并研究了可听噪声A声级与可听噪声5000 Hz以上分量以及气象因素之间的相关性。通过建立EMD算法优化的XGBoost模型,对可听噪声5000 Hz以上分量,气象因素与A声级有效数据之间的相关性进行了验证。结果表明:可听噪声8000 Hz分量、可听噪声16000 Hz分量、可见范围、温度均对A声级可听噪声预测准确率有促进作用。