Artificial Precipitation Enhancement Weather System and the Operation Timing in Shenyang Area

By using the mathematical statistics and classification,the artificial precipitation enhancement cases in Shenyang area were analyzed.The results showed that the precipitation enhancement weather systems mainly included the northeast cold vortex,high-altitude trough,North China low-pressure,high-pressure rear and cold front cloud system.The appropriate height of precipitation enhancement was about 3 000-6 000 m in the middle and upper part of the cloud layer.The timing of precipitation enhancement should be in the radar's monitoring.The systems moved slowly or maintained stably in the developing or mature stages.The aircraft rainfall enhancement should be used in the stable and deep cloud layers.The rocket and antiaircraft gun rainfall enhancement should be used in the unstable move.

CHEMICAL BEHAVIOR OF CERIUM ELEMENT IN ROCK WEATHERING SYSTEM

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Selection of Weather Parameters for Air-Conditioning System Design for Buildings with Long Thermal Lag

Two building factors-a longer thermal lag of more than one hour for building envelops and a lag of indoor radiation to convert into cooling load-have impact on the instantaneous heat input and instantaneous cooling load.So the two factors should be taken into account when selecting the weather parameters for air-conditioning system design.This paper developed a new statistic method for the rational selection of coincident solar irradiance,dry-bulb and wet-bulb temperatures.The method was applied to historic weather records of 25 years in Hong Kong to generate coincident design weather data.And the results show that traditional design solar irradiance,dry-bulb and wet-bulb temperatures may be significantly overestimated in many conditions,and the design weather data for the three different constructions is not kept constant.

Weather Prediction With Multiclass Support Vector Machines in the Fault Detection of Photovoltaic System

Since the efficiency of photovoltaic(PV) power is closely related to the weather,many PV enterprises install weather instruments to monitor the working state of the PV power system.With the development of the soft measurement technology,the instrumental method seems obsolete and involves high cost.This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data.By this method,the weather types are deduced by data analysis,instead of weather instrument A better fault detection is obtained by using the support vector machines(SVM) and comparing the predicted and the actual weather.The model of the weather prediction is established by a direct SVM for training multiclass predictors.Although SVM is suitable for classification,the classified results depend on the type of the kernel,the parameters of the kernel,and the soft margin coefficient,which are difficult to choose.In this paper,these parameters are optimized by particle swarm optimization(PSO) algorithm in anticipation of good prediction results can be achieved.Prediction results show that this method is feasible and effective.

West African Weather Systems in Development of Tropical Cyclones

Tropical Cyclones have their origins from areas of low atmospheric pressure over warm waters in the tropics or subtropics.We have carefully studied the interconnection between the West African Weather Systems(WAWS)and their subsequent development into Tropical Cyclones.Between 2004 and 2005,we studied the interconnection and the teleconnexion between the WAWS and the various occurrences

Large-Scale Weather Systems: A Future Research Priority

A brief assessment is provided of both the case against and the case for assigning priority to research on large-scale weather systems （LSWS）. The three-fold case against is based upon： the emergence of new overarching themes in environmental science; the fresh emphasis upon other sub-disciplines of the atmospheric science; and the mature state of research and prediction of LSWS. The case for is also supported by three arguments. First is the assertion that LSWS research should not merely be an integral but a major component of future research related to both the new overarching themes and the other sub-disciplines. Second recent major developments in LSWS research, as epitomized by the paradigm shifts in the prediction strategy for LSWS and the emergence of the potential vorticity perspective, testify to the theme＇s on-going vibrancy. Third the field＇s future development, as exemplified by the new international THORPEX （The Observing System Research and Predictability Experiment） programme, embodies a perceptive dovetailing of intellectually challenging fundamental research with directed application（s） of societal and economic benefit. It is thus inferred that LSWS research, far from being in demise, will feature at the forefront of the new relationship between science and society.

The Need of Incorporating Indigenous Knowledge Systems into Modern Weather Forecasting Methods

The study was aimed to examine the need of incorporating traditional weather forecasting renowned indigenous knowledge system (IKS) into modern weather forecasting methods to be used for planning farming activities. In addition, not only gap that is not infused by current weather forecasting system with their advanced studies to understand why it is incorporated into existing technical frameworks was regarded, but also the limitation of advanced weather forecasting approach and strength to be elicited by indigenous knowledge system are crucial. Perspicuously, forms and onsite interrogates have been conducted to assess people’s beliefs, understanding, and attitudes on the indigenous knowledge system significance on weather forecasting. Therefore, atmospheric and biological conditions, astronomic, as well as relief characteristics were used to predict the weather over short and long periods. Usually, in assessing weather conditions, the conduct of animals and insects were listed as essential. Obviously, in order to predict weather particularly from rain within about short period of time, astronomical characteristics were used. Commonly, there are few peers who know conventional weather prediction approaches. This lowers the reliability of conventional weather prediction. The findings revealed some variables that impact meteorological inaccuracy by scientific methods and help to recognize and evaluate the gap that current meteorological technologies do not achieve and new particulars anticipated to be filled with conventional methods to attain accurate weather prediction. Additionally, the study indicated that both modern and conventional processes have certain positive and limitations, which means that they can be coupled to generate more accurate weather prediction reports for end users.

Electrical-hydraulic conductivity model for a weatheredfractured aquifer system of Olbanita, Lower Baringo Basin, Kenya Rift

Groundwater yield in the Kenya Rift is highly unsustainable owing to geological variability.In this study,field hydraulic characterization was performed by using geoelectric approaches.The relations between electrical-hydraulic(eh)conductivities were modeled hypothetically and calibrated empirically.Correlations were based on the stochastic models and field-scale hydraulic parameters were contingent on pore-level parameters.By considering variation in pore-size distributions over eh conduction interval,the relations were scaled-up for use at aquifer-level.Material-level electrical conductivities were determined by using Vertical Electrical Survey and hydraulic conductivities by analyzing aquifer tests of eight boreholes in the Olbanita aquifer located in Kenya rift.VES datasets were inverted by using the computer code IP2Win.The main result is that ln T=0.537(ln Fa)+3.695;the positive gradient indicating eh conduction through pore-surface networks and a proxy of weathered and clayey materials.An inverse(1/F-K)correlation is observed.Hydraulic parameters determined using such approaches may possibly contribute significantly towards sustainable yield management and planning of groundwater resources.

灾害天气下计及一二次设备耦合故障的电网短时风险评估

灾害天气下电网发生短时多重故障有可能超出保护系统预设的反应能力,一二次设备耦合影响将助推多重故障风险发展和蔓延。为此,提出了计及一二次设备耦合故障的短时风险评估方法。首先,分析了一次设备与二次设备之间的交互作用及其对电网短时运行风险的影响。其次,构建了一次设备和二次设备的故障概率模型,重点分析了一二次设备耦合故障引发保护拒动的概率。然后,结合保护系统对超预期故障的反应能力,提出了灾害天气下电网短时多重故障风险评估方法。最后,采用改造的IEEE39节点系统对所提方法进行测试。算例结果表明:受外界气象环境影响,保护系统缺陷暴露导致非期望动作使得电网多重故障风险更为严重;综合考虑灾害天气对一二次设备影响下的短时运行风险,对于发现和消除保护系统薄弱环节及制定电网降风险运行策略具有一定参考价值。

2013-2020年海河流域农田水热碳通量及气象要素观测数据集

本研究以海河流域官厅水库旁农田生态系统为研究对象,采用蒸渗仪、涡动相关仪、大孔径闪烁仪和自动气象站/气象要素梯度等观测系统,开展农田生态系统多尺度通量和气象要素的长期定位观测。本数据集由多尺度通量(米,百米,公里级)和气象要素数据组成,观测项目包括生态系统净碳交换量、潜热通量/蒸散发、感热通量、空气温度、空气相对湿度、风速、风向、向下/上短波辐射、向下/上长波辐射、净辐射、大气压、降水、红外辐射温度、光合有效辐射、土壤温度、土壤水分、土壤热通量、平均土壤温度等。本数据集经过了严格的处理和质量控制,可用于研究水库消涨对周边生态系统碳水等物质和能量的循环带来的影响,也可为相关遥感模型或过程模型等研究提供有力的数据基础。

Hydrogeophysical Characterization of a Weathered-Fractured Aquifer System: A Case Study of Olbanita, Lower Baringo Basin, Kenya Rift

Groundwater yields in the Kenya Rift are highly unsustainable owing to geological variability. In this study, field hydraulic characterization was performed by using geo-electric approaches. The relations between electrical–hydraulic (eh) conductivities were modeled hypothetically and calibrated empirically. Correlations were based on the stochastic models and field-scale hydraulic parameters were contingent on pore-level parameters. By considering variation in pore-size distributions over eh conduction interval, the relations were scaled-up for use at aquifer-level. Material-level electrical conductivities were determined by using Vertical Electrical Survey and hydraulic conductivities by analyzing aquifer tests of eight boreholes in the Olbanita aquifer located in Kenya rift. VES datasets were inverted by using the computer code IP2Win. The main result is that InT = 0.537(1nFa) + 3.695, the positive gradient indicating eh conduction through pore-surface networks and a proxy of weathered and clayey materials. An inverse (1/F-K) correlation is observed. Hydraulic parameters determined using such approaches may possibly contribute significantly towards sustainable yield management and planning of groundwater resources.

Analysis of the False Alarm Fault in Antenna-servo System of Weather Radar

A false alarm fault frequently appeared in antenna-servo system of the CINRAD/SA weather radar of Shanwei in the second half of 2011, so possible reasons for the false alarm fault were listed firstly using method of exhaustion, and then the main reason was determined using exclusive method. That is, the fault was closely related to the signal transmission channel from the antenna mount to servo system in RDA cabinet. After ex- amining questionable nodes in the transmission channels of the alarm signal, we found that the false alarm fault might result from the interference of a burr in the temperature sensing circuit of the elevation motor. In actual operation, a filter capacitor was connected with the corresponding pin in the upper optical board to screen the interference of a burr, thereby successfully eliminating the false alarm fault in antenna-servo system of the CIN- RAD/SA radar of Shanwei.

数值预报AI气象大模型国际发展动态研究

数值预报是研究地球系统的重要工具,有助于加深科学家对大气、海洋、气候和环境等复杂系统之间相互作用和变化过程的理解,在防灾减灾、气候变化和环境治理等方面发挥着不可或缺的作用。随着模式复杂度和分辨率的提高,传统数值模式在气候变化研究和气候预测方面取得了迅速的进展,但也面临一些挑战,需要得到数据同化、集合耦合、高性能计算和不确定性分析等多方面的支持。而近年来,“AI+气象”的交叉研究在气象领域引起了广泛关注。基于多种深度学习架构的人工智能大模型,依托强大的计算资源和海量的数据进行训练,能够以新的科学范式进行高效数值预报。气象大模型不断涌现,一些科技公司如华为、英伟达、DeepMind、谷歌、微软等,以及国内外高校如清华大学、复旦大学、密歇根大学、莱斯大学等发布了多个涵盖临近预报、短时预报、中期预报和延伸期预报等不同领域的气象大模型。这标志着人工智能与气象领域的交叉融合已经达到新的高度。尽管气象大模型在现阶段取得了较大突破,但其发展仍然面临弱可解释性、泛化能力不足、极端事件预报强度偏低、智能预报结果过平滑、深度学习框架能力需要拓展等诸多挑战。

吉林省玉米倒伏灾害天气分型及典型过程分析

为探求造成吉林省玉米倒伏灾害的天气系统类型、特征及发生规律,利用历史灾情和玉米发育期数据、ERA5再分析资料,采用典型个例分析方法,对吉林省造成玉米倒伏灾害的天气系统进行了分析总结。结果表明:造成玉米倒伏灾害的天气系统主要有台风、东北冷涡、低空切变线及高空槽。玉米拔节—抽雄期倒伏主要受东北冷涡影响,抽雄-乳熟期主要受低空切变线、高空槽影响,乳熟-成熟期主要受北上台风影响。台风是造成吉林省玉米倒伏最为严重的天气系统,影响范围广,其移动路径对作物倒伏灾害的发生范围起决定性作用,倒伏灾害多发生在台风移动路径的西侧或北侧,地面低压系统的北侧。东北冷涡次之,倒伏主要发生在水汽、动力、热力和对流不稳定条件均最为显著的冷涡东南象限。低空切变线、高空槽影响范围小,低空切变线类倒伏发生位置取决于低空切变线或辐合线的位置。高空槽类倒伏一般发生在高空槽前,地面冷锋附近。

视频分布式系统在全国高清气象视频会商系统中的应用

视频分布式系统自21世纪出现以后,经过近二十年的发展,已经成为一个成熟、先进的音视频处理系统,其出色的性能可以弥补全国高清气象视频会商系统以视频矩阵为核心的传统音视频处理系统的不足,因此在新一代全国高清气象视频会商系统中得以应用。视频分布式系统的应用提升了会商系统的稳定性和便捷性,在国省两级视频会商业务中发挥了重要作用。阐述了视频分布式系统的技术特点,分析了视频分布式系统在新一代全国高清气象视频会商系统中的应用。

人工影响天气虚拟仿真实验及课程体系建设

在“人工影响天气”的相关课程教学中,构建虚拟仿真实验教学平台有助于学生深入理解基础理论知识,并对实际业务操作及催化方案设计、优化产生感性认识。虚拟仿真实验教学突破了室内教学时空及仪器设备昂贵的局限性,解决了自然界中不能对同一块云反复催化的实际困难,对云降水环境及人工催化过程高度重现,对理论教学体系及教学形式进行补充,完善了课程教学体系。此外,科技成果在教学和本科生科研创新活动的引入,进一步增强学生科技创新意识及学习兴趣,促进了科研创新型人才的培养。

风电系统高影响天气的时空格局分析

以风电系统为例,基于已有标准和研究,定义和识别了与风速和气温等基本气象要素相关的风电系统高影响天气,并利用近30年气象站观测资料,总结了风电系统高影响天气的时空格局。研究发现,这些高影响天气的区域特征明显:新疆、四川盆地(“三北”、川渝西部和沿海地区)等地多出现影响风电出力的小风天气(大风天气),影响电力设备安全的极大风多出现在我国西部高原山地和东部沿海地区;夏季高温频发(冬季低温频发和电线积冰风险较高)的气象站主要分布在新疆东部、川渝东部、华东中部(东北、蒙西和新疆北部)等地。这些高影响天气的年代际变化也存在差异:我国大部分地区的大风日数和小风日数,以及东北、蒙西和新疆等地的低温日数和电线积冰风险日数在近10年都维持较低水平,这对风电出力和电力系统安全都是有利的;我国大部分区域的极端高温日数近30年来不断增加,这将给我国夏季电力设备的稳定运行和能源保供带来更多风险。

考虑气候风险耦合影响的中长期电力资源规划

考虑气候风险对电力供应和需求的影响进行电力系统气候适应性规划研究十分重要且紧迫。文中从规划视角开展电力系统气候适应性研究,构建考虑气候风险耦合影响的中长期电力资源规划模型,通过电力资源优化配置提升电力系统气候适应水平。结果表明:考虑气候长期变化趋势的规划方案中可再生能源发电量占比在装机容量减少的情况下反而有所增加,可再生能源利用水平提升,系统年均总投资建设成本下降;而考虑极端天气气候风险的规划方案中可控电源年均装机容量和发电量增加,以支撑可再生能源消纳和系统安全稳定运行,可再生能源年均发电量略有减少,系统年均总投资建设成本增加。考虑气候风险耦合影响的电力规划方案能够统筹提升可再生能源利用和系统安全水平,推动可再生能源高质量发展。

天气数据驱动下基于深度主动学习的新型电力系统供需失衡风险快速评估方法

相较于极端气象,天气因素对电力系统供需平衡的冲击常被忽视,但多日无风无光等事件同样可带来保供问题。提出了一种天气数据赋能、深度主动学习赋智的新型电力系统供需失衡风险快速评估方法,较传统方法更高效、更准确。首先,考虑源网荷储多环节建立以天为尺度的电力系统生产模拟模型,以进行无风无光等异常气象下系统供需失衡的分析。同时,针对传统可靠性指标的不足,提出以天为尺度的新型分布式指标,并进一步利用风险曲线描述系统长期风险。然后,提出应用深度主动学习的电力系统风险快速评估框架,建立风险预测主网络和误差预测副网络耦合的双深度神经网络,并构建相应的损失函数和训练流程。最后,基于IEEE标准算例进行了效果验证与各方法对比,结果验证了其高效、准确、可拓展等特点。该研究为新型电力系统的风险快速评估提出了一种新颖有效的思路。

基于置信规则库气象站故障检测专家系统

针对气象站故障检测的需求,研制了基于置信规则库的气象站故障检测专家系统。专家系统利用前期建立起来的气象站的气象数据库,根据当前实时气象数据,通过改进的置信规则库模型结合专家经验知识自动完成数据分析和处理、是否有故障判断并对故障程度进行等级划分。工作人员在进行现场故障检测之前,只需通过气象站传感器故障专家系统对异常传感器进行辅助诊断,确定故障等级后依据故障等级携带相应检修设备或需更换的零部件进入现场实施检查和维修。实验测试表明系统对各个设备的总体平均检测准确率从69.8%提高到85.7%,极大提升了检测的效率和准确性。