Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

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.

Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.

Localization Applications of 3D-GIS Artificial Weather Modification Operational Command System in Fuxin, China

Based on the command and management requirements of weather modification operations in Fuxin City, China, a new generation of three-dimensional operation command system platform for new artificial weather modification was developed and developed using modern communication network technology. The system uses integrated three-dimensional geographic information system (3D-GIS), global positioning system (GPS) positioning and virtual reality technology to calculate, analyze and process the new basic geographic information and weather modification information vector data, generate real-time weather modification operations and guide products in Fuxin area, and realize the impact on labor. The three-dimensional, comprehensive and effective management of basic weather information, early warning and forecast information, disaster prevention and mitigation information and other decision support information provides technical support for scientifically and effectively managing and directing weather modification operations.

Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the environment.Solar Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation systems.Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time.In this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and precipitation.Therefore,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting data.In order to perform long-term collection of weather data,Hadoop MapReduce tool is employed.The proposed solar irradiance prediction model operates on different stages.Primarily,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data normalization.Besides,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive analysis.It is divided into input layer,hidden layer,loadbearing layer,and output layer.To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is applied.In order to validate the performance of the proposed model,a series of experiments was conducted.The experimental values infer that the proposed model outperformed other methods used for comparison.

Buildings and civil engineering works-Sealants-Determination of changes in cohesion and appearance of elastic weatherproofing sealants after exposure of statically cured specimens to artificial weathering and mechanical cycling

Foreword ISO(the International Organization for Standardization)is a worldwide federation of national standards bodies(ISO member bodies).The work of preparing International Standards is normally carried out through ISO technical committees.Each member body interested in a subject for which a technical committee has been established has the right to be represented on that committee.

Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction data

Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand.Although numerical weather prediction(NWP)models can forecast solar radiation variables,they often have significant errors,particularly in the direct normal irradiance(DNI),which is especially affected by the type and concentration of aerosols and clouds.This paper presents a method based on artificial neural networks(ANN)for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts(ECMWF)and the Copernicus Atmospheric Monitoring Service(CAMS),respectively.Two ANN models were designed:one uses as input the predicted weather and aerosol variables for a given instant,while the other uses a period of the improved DNI forecasts before the forecasted instant.The models were developed using observations for the location of´Evora,Portugal,resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2,MAE and RMSE of 0.0646,21.1 W/m^(2)and 27.9 W/m^(2),respectively.The model was also evaluated for different timesteps and locations in southern Portugal,providing good agreement with experimental data.

Study on Potential and Countermeasures of Artificial Precipitation Enhancement in Fuxin Region

To exploit cloud water resources to increase precipitation on the ground effectively,according to the principles of artificial precipitation enhancement,based on historical data of weather and precipitation from Fuxin National Basic Meteorological Stations from 1981 to 2010,the distribution characteristics of cloud water resources,potential and countermeasures of artificial precipitation enhancement in Fuxin region of Liaoning Province were studied. The results show that the annual average quantity of cloud water resources in the air over Fuxin region over the past 30 years was 285. 6 × 10~8m^3,but the annual average precipitation was only 471.8 mm( amounting to 47.2 ×10~8m^3 of water resources),accounting for only16. 5% of total quantity of cloud water resources in the air; 12 times of artificial precipitation enhancement operation experiments were carried out by using the weather models and technical indicators suitable for artificial precipitation enhancement in Fuxin region,and the artificial precipitation efficiency was 18. 3% on average,5. 2% higher than before,and the annual average precipitation could increase by 2. 5 × 10~8m^3 approximately. All of these have great significance for implementing artificial precipitation enhancement operation scientifically and effectively,maximizing the exploitation of cloud water resources in the air,increasing total quantity of water resources,and mitigating drought in Fuxin region.

安徽省燃气炮人工增雨作业效果综合评估

选取2021—2023年安徽省81次燃气炮作业的双偏振天气雷达、雨量计等多源观测数据,综合评估燃气炮作业增雨效果并分析可能机理。结果显示:在降水开始前作业个例增雨效果较好,并伴有水平反射率因子Z_(H)、差分反射率Z_(DR)的增加和共极化相关系数ρ_(hv)的减少;降水开始后作业增雨效果欠佳。使用携带暖云催化剂的燃气炮作业后云体变化主要在零度层以下,且维持时间较短;使用携带冷云催化剂的燃气炮作业后暖云区和冷云区均有明显变化,且作业影响持续时间更长。燃气炮作业过程中雷达速度谱宽增大,可能是作业引起气流涡旋的增加所导致。统计结果显示:增雨的显著性与作业时长呈负相关,作业时长与Z_(DR)增量呈负相关,过量播撒会导致减雨;增雨的显著性与作业前影响区雨量呈负相关;增雨量与Z_(H)、中低层风速、风切变呈正相关,与高层风速呈负相关。

人工智能时代国省一体化人工影响天气综合业务平台建设

人工智能已成为新一轮国际竞争的焦点和科技发展的新引擎。近些年来,加强人工智能技术在行业气象服务、人工影响天气和气象影视等领域的应用,已成为气象领域的重要课题。人工智能在提高人工影响天气科技创新能力,解决人工影响天气效果检验、装备技术等方面发挥着重要作用。文章在阐述国省一体化人工影响天气综合业务平台建设必要性和现状的基础上,对平台建设前景进行了深入分析,并从作业条件智能识别、作业效果智能评估、作业安全智能管理、指导产品智能生成等4个方面,提出了国省一体化人工影响天气综合业务平台的建设思路,以推动人工智能与气象业务深度融合,持续优化人工智能气象应用发展环境。

融合物理理解与模糊逻辑的分类强对流客观短期预报系统:(1)系统构成

提供准确的雷暴、短时强降水、雷暴大风和冰雹客观短期预报产品,对提高预报预警的预见期,及早采取有针对性的预防措施有重要意义。基于对四类强对流天气现象物理成因理解,给出了由国家气象中心牵头研发,融合模糊逻辑人工智能方法的分类强对流客观短期概率预报系统的流程框架和实现方法,详细介绍了该系统的结构特征,以及系统中用于雷暴、短时强降水、雷暴大风和冰雹四类强对流天气预报模型构建的关键预报因子、隶属度函数获取方法和权重因子配置等信息,并在此基础上探讨了物理理解与模糊逻辑人工智能相融合方法具有广泛适用性的本质,可以表征产生特定强对流天气现象的环境配置的多样性和复杂性。

人工影响天气国际标准化发展趋势研究

为填补我国人工影响天气标准国际化工作的空白,提升我国在国际标准体系中的地位和影响力,以及增强我国人工影响天气标准在国际社会的认可度,本文对世界气象组织和国际标准化组织的发展现状进行了深入研究,并对气象领域的标准化现状进行了详细分析。基于国外以及我国已制定的人工影响天气标准情况,提出推动我国人工影响天气领域标准国际化工作的具体路径和方法,旨在为人工影响天气领域的国际标准“本地化”和本地标准“国际化”提供有力的技术支撑。

人工影响天气标准体系构建研究

本文从人工影响天气及其标准体系的概念入手,立足于气象战略定位、业务发展需求及标准化管理对国家标准体系框架构建的需求,结合人工影响天气标准体系的构建原则,构建并优化了人工影响天气标准体系。同时,对标准体系中的术语、作业条件、作业实施、装备保障、效果评估和作业安全六大子体系进行了详细阐述,进一步完善了气象标准体系的总体框架。最后,就如何推进人工影响天气标准体系的建设与优化提出了具体建议。

喷粉铝合金建筑型材耐老化性能紫外灯快速检测方法的研究

为了获得适合工厂对粉末喷涂铝型材耐老化性能进行质量控制的快速检测方法,研究了紫外灯加速老化试验结果与1 000 h氙灯老化试验结果之间的关系,试验样品为含有不同耐候等级的粉末涂料制成的粉末喷涂铝板,分别测试了组合紫外灯加速老化试验与1 000 h氙灯老化试验的光泽及颜色变化,两种试验结果之间的相关系数为0.85~0.89,216 h的组合紫外灯人工加速老化试验可以近似准确模拟1 000 h氙灯老化试验结果,该试验方法可以作为工厂内部质量控制方法使用。

人工增雪技术对降低雾霾天气污染物浓度的可行性研究

雾霾天气是影响城镇居民生活健康的主要原因之一,冬季降雪量不足会导致城市内空气污染物浓度加重。为此使用人工增雪技术提高冬季降雪量,并探究人工增雪技术对降低雾霾天气污染物浓度的可行性。分析人工增雪技术的效果和潜力,计算城市内雾霾天气污染物浓度和降雪量间的变化,以此分析该技术的可行性。分析结果表明,人工增雪能降低雾霾天气污染物浓度,有效改善城市内的酸雨情况,优化空气污染质量综合指数,提高空气质量优良率。证明了人工增雪技术对降低雾霾天气污染物浓度的可行性较强。

初论电力气象的基本概念与研究方向

电力气象伴随着电力系统和气象科学的发展而发展。在我国积极应对全球气候变化、推进能源转型的大背景下,加快培育电力气象新兴学科是新型电力系统建设的现实需要,也是科学技术创新发展的客观要求。该文将电力气象看作一门研究气象条件与电力系统间相互作用及其应用方法的交叉学科,探讨了电力气象的定义与应用,系统性梳理了19世纪以来电力气象的整体发展历程。随着新型电力系统发展,总结了当前电力气象的主要需求和技术挑战,提出电力气象基础理论、环境感知监测、定制化预报、灾害预警、供电保障及人工智能应用等重要研究方向,为深化电力气象应用、支撑新型电力系统高质量发展提供参考。

可解释人工智能在气象预报中的应用和展望

本研究探讨了可解释的人工智能在现代气象预报服务业务中的应用和展望。目前,AI技术在强对流监测、临近预报等方面提高了准确性,但仍存在训练数据集不完备、不平衡和模型解释性不足等问题。未来,可解释的人工智能将成为重要发展方向,提高预测模型可靠性和可信度,其与数值预报融合将成为另一趋势,提供更准确、可靠的天气预报。研究应关注解释性AI模型开发应用以及AI技术与传统数值预报融合方法,以推进可解释的人工智能在气象预报服务业务中的应用和发展。

生态修复型人工影响天气作业在天津市滨海新区的应用研究

在全球气候变化和生态环境问题日益严重的背景下,天津市滨海新区作为经济发展的重要区域,其生态环境质量对区域可持续发展影响巨大。生态修复型人工影响天气作业作为一项创新的环境保护措施,对改善区域生态环境、应对气候变化、促进农业发展等具有重要意义。深入探讨生态修复型人工影响天气作业在天津市滨海新区实施的重要性,分析其在实际应用中的效果与挑战,以期为区域生态环境保护和可持续发展提供有力的理论支持和实践指导。

冬季六盘山区地面烟炉作业碘化银扩散研究

文章基于高斯点源扩散模型,使用2022年1月六盘山自动气象观测站、激光云高仪、三维超声测风仪数据,对人工影响天气碘化银发生器生成催化剂在不同天气稳定度条件的水平、垂直范围扩散密度进行模拟研究。得出六盘山自动气象观测站人工影响天气作业-15~-25℃窗口高度在1.5 km附近,降水云层位置在1.6 km以下;催化剂有效密度103个/m ^(3)在强不稳定天气条件下垂直范围能达到4 km,水平范围能达到40 km;地面碘化银发生器生成的催化剂能够进入云层,密度能达到有效催化密度。研究结果可为六盘山区冬季地面碘化银发生器人工影响天气作业提供指导。

一种包含指纹识别的火箭发射装置安全锁定器的设计

火箭发射装置安全锁定器是用于发射火箭弹的工具,是通过电流激发控制火箭弹中的点火具激发来实现火箭弹发射的工作。火箭发射装置无线安全锁定器在原有的开机数字密码的基础上新增了一个指纹身份识别环节,指纹身份识别与数字密码一样是通过授权的管理员进行录入,只有已录入指纹的作业人员才能通过指纹身份识别单元的身份识别才能开启锁定器,从而在源头上确保火箭弹发射安全。