TRMM卫星微波成像仪分级产品及其反演降水算法

文章叙述了获取定量降水信息的意义，简要介绍了对热带测雨卫星TRMM(Tropical Rainfall Measurement Mission)的仪器、美国国家宇航局提供的微波成像仪TMI(TRMM Microwave Imager)分级产品。对比了物理方法和经验方法反演降水的特点，并对一些经验方法以及倾斜对流系统对反演降水的影响、动态聚类分析、神经网络反演方法的研究成果进行了介绍。     

铜陵地区老鸦岭层状钼矿床铅同位素组成研究

安徽铜陵老鸦岭矿床中二叠系大隆组(P2d)顶部的含矿(钼矿化)黑色页岩以及附近(立新煤矿)同一层位不含矿黑色页岩的实测铅同位素组成分别为：^206Pb/^204Pb20.20～22.37,^207Pb/^204Pb15.67～15.82,^208Pb/^204Pb38.47～38.60和^206Pb/204Pb18.83～20.80,^207Pb/^204Pb15.65～15.76,^208Pb/^204Pb38．84～39．22。对137Ma的放射成因Pb进行校正后的铅同位素组成表明，含矿黑色页岩和不含矿黑色页岩均与燕山晚期火成岩无关，老鸦岭含矿黑色页岩可能具沉积成因。对沉积(约250Ma)以来的放射成因Pb进行校正后的铅同位素组成表明：不含矿黑色页岩的Pb源自上地壳，而含矿黑色页岩的Pb(因而推测其他成矿金属)可能源于上地壳物质(与不含矿黑色页岩的Pb源相似)与下地壳物质的混合。     

重庆市地学旅游资源研究

重庆市的地学旅游资源极其丰富，可划分为岩石圈旅游资源等4个大类、地质旅游资源等11个基本类型、地层旅游景观等18种景观和黔江县上白垩统正阳组剖面等107个景区(景点)。它们具有类型齐全、分布广泛、岩溶精品和森林独特的特点。从加强岩溶旅游资源调查等6个方面入手，就可提高对它们的开发利用程度.     

鄂尔多斯盆地中生代晚期以来大地热流的变化及其对生态环境格局和演变的影响

鄂尔多斯盆地的西北部、东北部和南部三个区域现今大地热流平均值分别为56．3、67．3和65．3mW／m^2，对应的生态环境格局也有明显的差异。研究表明，大地热流每增加4～5mW／m^2可使年均地表温度升高约l℃，使最低月均地表温度升高2。C以上。鄂尔多斯盆地东北部的平均大地热流比西北部高出11mW／m^2，东北部年均地表温度可能比西北部高出2～3℃，其最低月均地表温度可能比西北部高出4～6℃。西北部的大地热流平均值已经低于维持地表生态系统延续所需大地热流的临界值(57mW／m^2)，其自然生态系统整体上已经处于脆弱境地；东北部和南部的大地热流均大于57mW／m^2，自然生态系统均尚较稳健。东北部的沙漠化可能是风沙侵入的结果，其生态应该是可以恢复的。整个西北部作为一个整体看，72万年以前大地热流就已衰减到临界值以下，区域生态系统渐趋脆弱，开始整体上向荒漠化演变。     

碳酸岩Sr、Nd、Pb 同位素地球化学研究评述

碳酸岩是出露相对较少的幔源岩石，其中Sr与Nd是研究地幔物质组成的主要对象之一。本文统计了世界上主要碳酸岩的锶、钕、铅同位素组成特征；研究显示，碳酸岩源区主要是洋岛玄武岩高U／Pb的HIMU端员和富集端员(EM1或EM2)的混合作用；此外大部分碳酸岩的锶、钕同位素落在大洋玄武岩范围内；这些均表明其成因与地慢柱有密切联系。碳酸岩及与之共生的硅酸岩的同源或独立源区模式部很难充分解释两者同位素组成特征，逭反映碳酸岩的演化模式涉及更复杂的过程。可能是俯冲作用使碳酸岩源区经历不同时间和程度的富集、亏损过程导致地幔源区成分不均一。     

Velocity field in the spiral-wave pattern observed in rotating shallow-water experiments

The paper presents the first quantitative results of a laboratory study of the velocity field in a two-arm spiral-wave pattern generated in a steady-state fashion by a hydrodynamical instability in a differentially rotating, thin layer of liquid. The liquid layer has a free surface, and the rotational profile includes an interval where the velocity drops abruptly, as in the gaseous disks of spiral galaxies. The properties of anticyclonic vortices observed between the arms of this pattern at the corotation radius are considered.     

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".     

超低频电磁声源非线性动力学建模与分析

电磁声源输出力大、体积小、易于实现超低频输出,在无人反水雷作战中具有显著优势。其动力学行为受到机械回复力和电磁力的耦合作用影响,当施加电流超过坍塌电流,电磁力将超过机械回复力, 就会发生吸和,造成声源的损坏。为准确描述电磁声源非线性动力学特性,预先评估坍塌电流,建立考虑动态漏磁系数的声源非线性动力学模型。通过三维有限元仿真计算动铁芯运动到不同位置处气隙的漏磁系数, 拟合得到动态漏磁系数。根据等效磁路法建立声源的改进电磁力模型,进而建立电磁声源非线性动力学模型。 通过 Runge-Kutta 算法计算得到阶跃激励下声源振动的位移和速度,绘制相平面图。研究稳定与失稳 2 种情况下的相轨迹的动态变化规律,为电磁声源的设计和控制提供理论支撑。     

Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16,2022

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar, making it capable of enhancing tornado monitoring and early warning capabilities. This study analyzed the char- acteristics and nowcasting signals of a tornado case that occurred on June 16, 2022 in the Guangzhou region. Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation. The X-band phased array radar, with its high temporal and spatial resolution, provided an opportunity to capture early warning signals from polarimetric characteristics. The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature (TVS) features in a 10-minute lead time, surpassing the capabilities of the CINRAD/SA radar. Additionally, due to its higher scanning frequency, the X- band phased-array radar was capable of consistently identifying TVS with shorter intervals, enabling a more precise tracking of the tornado’s path. The application of professional radars, in this case, provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals.     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。