Possible Sources of Forecast Errors Generated by the Global/Regional Assimilation and Prediction System for Landfalling Tropical Cyclones.PartⅠ:Initial Uncertainties

This paper investigates the possible sources of errors associated with tropical cyclone （TC） tracks forecasted using the Global/Regional Assimilation and Prediction System （GRAPES）. The GRAPES forecasts were made for 16 landfaIling TCs in the western North Pacific basin during the 2008 and 2009 seasons, with a forecast length of 72 hours, and using the default initial conditions （＂initials＂, hereafter）, which are from the NCEP-FNL dataset, as well as ECMWF initials. The forecasts are compared with ECMWF forecasts. The results show that in most TCs, the GRAPES forecasts are improved when using the ECMWF initials compared with the default initials. Compared with the ECMWF initials, the default initials produce lower intensity TCs and a lower intensity subtropical high, but a higher intensity South Asia high and monsoon trough, as well as a higher temperature but lower specific humidity at the TC center. Replacement of the geopotential height and wind fields with the ECMWF initials in and around the TC center at the initial time was found to be the most efficient way to improve the forecasts. In addition, TCs that showed the greatest improvement in forecast accuracy usually had the largest initial uncertainties in TC intensity and were usually in the intensifying phase. The results demonstrate the importance of the initial intensity for TC track forecasts made using GRAPES, and indicate the model is better in describing the intensifying phase than the decaying phase of TCs. Finally, the limit of the improvement indicates that the model error associated with GRAPES forecasts may be the main cause of poor forecasts of landfalling TCs. Thus, further examinations of the model errors are required.

STUDY OF THE EFFECTS OF REDUCING SYSTEMATIC ERRORS ON MONTHLY REGIONAL CLIMATE DYNAMICAL FORECAST

A nested-model system is constructed by embedding the regional climate model RegCM3 into a general circulation model for monthly-scale regional climate forecast over East China. The systematic errors are formulated for the region on the basis of 10-yr (1991-2000) results of the nested-model system, and of the datasets of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the temperature analysis of the National Meteorological Center (NMC), U.S.A., which are then used for correcting the original forecast by the system for the period 2001-2005. After the assessment of the original and corrected forecasts for monthly precipitation and surface air temperature, it is found that the corrected forecast is apparently better than the original, suggesting that the approach can be applied for improving monthly-scale regional climate dynamical forecast.

On the Imminent Regional Seismic Activity Forecasting Using INTERMAGNET and Sun-Moon Tide Code Data

In this paper we present an approach for forecasting the imminent regional seismic activity by using geomagnetic data and Earth tide data. The time periods of seismic activity are the time periods around the Sun-Moon extreme of the diurnal average value of the tide vector module. For analyzing the geomagnetic data behaviour we use diurnal standard deviation of geomagnetic vector components F (North, East, Down) for calculating the time variance GeomagSignal. The Sun storm influence is avoided by using data for daily A-indexes (published by NOAA). The precursor signal for forecasting the incoming regional seismic activity is a simple function of the present and previous day GeomagSignal and A-indexes values. The reliability of the geomagnetic “when, regional” precursor is demonstrated by using statistical analysis of day difference between the times of “predicted” and occurred earthquakes. The base of the analysis is a natural hypothesis that the “predicted” earthquake is the one whose surface energy density in the monitoring point is bigger than the energy densities of all occurred earthquakes in the same period and region. The reliability of the approach was tested using the INTERMAGNET stations data located in Bulgaria, Panagurishte, PAG (Jan 1, 2008-Jan 29, 2014), Romania, Surlari, SUA (Jan 1, 2008-Jan 27, 2014), Italy, L’Aquila, AQU (Jan 1, 2008-May 30, 2013) in the time of EU IRSES BlackSeaHazNet (2011-2014) project. The steps of program for solving the “when, where and how” earthquake prediction problem are shortly described.

考虑光伏随机性的交直流混合配电网鲁棒机会约束安全域模型

为实现高比例光伏接入下交直流混合配电网的安全评估,构建了交直流混合配电网的鲁棒机会约束安全域模型。首先,提出一种基于区间划分的自适应带宽核密度估计方法,以获取光伏预测误差概率分布,构建光伏出力机会约束不确定集合;其次,采用功率与节点电压幅值比值为状态变量,在已有交流线性化潮流模型的基础上推导一种交直流混合配电网线性化潮流模型;然后,提出交直流混合配电网鲁棒机会约束安全域的概念和模型,结合列与约束生成算法和径向迭代搜索算法生成可观测的鲁棒机会约束安全域空间。最后,算例分析验证了所提模型能够在鲁棒机会约束安全域上准确、直观的反映光伏出力的随机性,进而为交直流混合配电网的准确安全评估提供支撑。

基于可解释机器学习的黄河源区径流分期组合预报

黄河源区是黄河流域重要的产流区和我国重要的清洁能源基地,提高黄河源区径流预报准确率可为流域水资源科学调配和水风光清洁能源高效利用提供重要支撑。以黄河源区唐乃亥和玛曲水文站为研究对象,基于不同月份径流组分的差异,考虑积雪覆盖率及融雪水当量变化,构建了中长期径流分期组合机器学习预报模型及其可解释性分析框架。研究结果表明:1)年内的径流预报时段可划分为融雪影响期(3—6月)和非融雪主导(以降雨和地下水补给为主)期(7月—次年2月);2)与传统不分期模型相比,唐乃亥站和玛曲站分期组合预报模型的纳什效率系数分别达0.897、0.835,确定系数(R2)分别达0.897、0.839,均方根误差分别降低了10%、17%,提高了径流预报准确率,通过分位数映射校正,唐乃亥站和玛曲站预报模型的R2分别进一步提升至0.926和0.850;3)基于SHAP机器学习可解释性分析框架,辨识了预报因子对径流预报结果的贡献程度,由高到低依次为降水、前一个月流量、蒸发、气温、相对湿度、融雪水当量等,发现了不同预报因子之间交互作用散点分布具有拖尾式或阶跃式的特征。

气象因子动态自适应的短期负荷预测方法

在加快构建新型电力系统背景下,提升负荷预测精度是保障电力系统经济、安全、稳定运行的重要措施,也是推动智能电网发展的关键所在。为增强对地区负荷的预测能力,提出一种气象影响因子动态自适应的短期负荷预测方法。首先,建立了基于并行多尺度时域卷积神经网络的负荷/气象信息融合模块,挖掘历史负荷与区域天气预报的多时间周期的变化模式;然后,提出了基于深度门控残差神经网络的气象因子动态辨识模块,通过动态调整特征贡献度并优化特征选择,增强对不同时空尺度特征权重的融合,提升模型对关键特征的提取能力;最后,以京津冀某区域的负荷数据进行算例分析,证明所提区域负荷预测方法具有更高的预测精度,对区域负荷的趋势性变化有较好的追踪效果。

面向“十四五”规划基于灰色GM(1,1)模型的沈阳市物流需求预测分析

“十四五”规划时期,推动东北全面振兴是重要的发展方向。其中物流的发展至关重要。东北地区因其地域特点,物流需求具有不确定性,亟待开展预测方法研究。文章以沈阳市为例,以2008—2022年货运量作为样本数据,提出基于灰色GM(1,1)模型的东北地区物流需求预测方法。通过仿真实验计算2008—2022年的物流需求,经过与实际值对比,对预测结果进行检验与分析,验证了文章所提出的模型是有效的;预测未来5年的物流需求量。实验结果表明,此方法能为沈阳市物流需求的定量分析提供较为准确的基础,同时为政府出台相关政策和企业进行物流规划建设提供参考价值。

Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula

The Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality forecasting.Meteorological data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the CCMM.In this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula.The nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA.When the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain.This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological predictions.Compared to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA.The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the case.Therefore,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.

珠海—斗山城际铁路互联互通方案研究

珠海作为粤港澳大湾区核心节点,是大湾区内部与国际接轨的重要门户,而作为珠西与珠东衔接门户的珠海市、江门市,区域内不同地区交通区位条件差异显著,珠三角东西两岸交通设施布局落差显著,东西布局不均衡、快速客运通道有所欠缺,极大地制约了城市功能的提升和产业转型的动力。在客流预测的基础上,充分利用区域内路网资源,实现区域协调发展、推动粤港澳大湾区轨道交通互联互通,以珠海—斗山城际铁路为例,重点分析其与区域内相关线路互联互通的关系,为后续类似地区城市组团的高铁、城际和城市轨道等多种交通运输功能互联互通、多网融合衔接方案提供参考。

基于SOM-FCM和KELM组合方法的短期光伏功率预测

为了提高短期光伏发电预测的精度,本文提出了一种将聚类后的自组织映射网络(SOM)与优化的核极值学习机(KELM)方法相结合的混合预测模型。首先,利用SOM来对训练数据集进行初始划分。然后,利用模糊C均值(FCM)对训练好的SOM网络进行聚类操作,同时利用Davies-Bouldin指数(DBI)来确定最佳聚类的大小。最后,在每个数据分区中,通过结合差分演化算法优化的KELM方法来建立区域KELM模型,或者结合最小二乘估计的多元线性回归(MR)方法来构建区域MR模型。此外,本文还提出了基于SOM的不同局部多元回归模型。将提出的结合SOM-FCM和KELM的混合预测模型分别应用于GEFCom2014三个不同太阳能电站,进行提前一小时的发电功率预测。与其他预测模型相比,光伏发电站1的平均绝对误差(MAE)降低了61.41%,光伏发电站2的MAE降低了60.19%,光伏发电站3的MAE降低了58.92%。光伏发电站1的均方根误差(RMSE)降低了52.06%,光伏发电站2的RMSE降低了54.56%,光伏发电站3的RMSE降低了51.43%。实验结果表明,提出的结合SOMFCM和KELM的方法可显著提高预测准确性。

基于2118台风“圆规”模式扰动方案对台风区域集合预报的影响

为了探讨不同模式扰动方案对台风区域集合预报的影响,本文以2021年18号台风“圆规”为例,基于WRF模式,采用了多物理过程参数化方法(MP)、随机变化参数扰动方法(SPP)和物理倾向随机扰动(SPPT)三种不同的方法,设计了EXP1(MP)、EXP2(SPP+SPPT)和EXP3(MP+SPP+SPPT)三组敏感性试验进行了比较研究。结果表明:三组区域集合预报试验都能较好地模拟台风路径和台风增强的过程,其中EXP3试验效果最好,EXP3试验的路径偏差值为三组集合预报试验最小的,其平均值为52.8 km,而控制试验CTRL(无模式扰动)、EXP1和EXP2的平均值分别为61.8、54.4和65.7 km;三组集合预报试验的扰动能量值基本都大于控制试验CTRL,且EXP3的扰动能量发展最快,扰动能量值基本为最大的;三组集合预报试验的Brier评分相较于控制试验CTRL有所改善,且EXP3的Brier评分值为三组集合预报试验改善最大的,EXP1和EXP2相对于CTRL试验的改善率为45%和48.76%,而EXP3能达到70%,EXP2与EXP1的预报效果相当,EXP3相较于EXP1和EXP2预报效果有所改善,其相对于EXP1和EXP2的改善率达到57.5%和40%。

多种机器学习方法在京津冀地区低能见度天气预报中的应用

利用2017—2021年的ERA5再分析资料和京津冀国家站地面资料,结合多种机器学习方法建立预报模型,开展轻雾、大雾客观预报。探讨了再分析资料、地形因素的影响,并结合多模型集成、统计消空进一步优化模型。结果表明:(1)XGBoost(eXtreme Gradient Boosting)、LightGBM(Light Gradient Boosting Machine)、随机森林等集成学习方法的预报效果均优于决策树方法;(2)在引入ERA5再分析资料、地形建模后,XGBoost、LightGBM模型的预报性能显著提高。相比仅使用地面要素建模,大雾预报的TS(Threat Score)提升了30%、32%,达到0.52、0.49,命中率分别为0.62、0.87。此外,经过多模型集成后,轻雾、大雾预报的TS提升到了0.51、0.54;(3)2022年秋季一次大雾过程中,本方法提前72 h准确预报了京津冀地区的大雾,其中以LightGBM模型表现最好。0~72 h轻雾预报和0~36 h逐小时大雾预报的TS均达到0.3,预报准确率、时效性均优于ECMWF(European Center for Medium Weather Forecasting)模式。

数据驱动的深井超长工作面支架载荷区域特征分析与分区预测

实现液压支架载荷预测分析对于及时改善支架适应性和实现安全支护具有重要作用,需要高质量、大数量的支架载荷时序数据和有效的预测方法作为支撑。然而,深部超长工作面上覆岩层应力环境和垮落步距的非同质同步引发工作面不同区域支架载荷差异化。因此,针对深井超长工作面顶板覆岩长期循环动载作用和分区破断造成工作面不同区域载荷差异明显以及无法实现动态区域更新下的液压支架载荷预测的问题,提出了一种数据驱动的深井超长工作面支架载荷区域特征分析与分区预测方法。首先,在获取工作面液压支架载荷数据的基础上,利用MeanShift聚类算法实现工作面区域动态划分,并分析深井超长工作面不同区域的支架载荷变化特征;然后,提出一种考虑多维时序数据特征和注意力机制LSTM预测方法,构建支架载荷一次性多输入多输出预测框架,实现了预测算法精度和输入输出特征结构的协同设计;最后,基于前述工作面区域划分结果,建立工作面区域化液压支架群组载荷预测模型,实现了综采工作面液压支架群组载荷时序数据循环训练和高精度预测。该方法通过考虑工作面载荷区域分布特征,建立多输入多输出特征工程,可实现基于工作面区域动态更新的液压支架群组载荷预测,能够为后续分析工作面矿压显现规律,超前适应采场环境变化和指导工作面正常回采提供依据。

基于华东区域模式云南短时强降水客观预报技术研究

为提高云南短时强降水的预报效率和预报准确率,使用华东区域模式基础物理量数据,计算云南短时强降水发生前1 h 700 hPa相对湿度、700 hPa比湿、K指数和6 km垂直风切变,对短时强降水的历史个例进行物理量阈值训练和单物理量的敏感性实验,确定物理量阈值。使用阈值判定法,对华东区域模式实时预报数据进行后处理,得到基于小时雨量和物理量的云南本地化短时强降水客观预报产品。研究结果表明:使用阈值判定法研发的基于华东区域模式云南短时强降水客观预报产品能较好地预报云南短时强降水的落区和走向,但是基于小时雨量的短时强降水客观预报产品漏报明显,基于物理量阈值的客观预报产品能有效降低云南短时强降水的漏报率。

Unveiling Global Human Trafficking Trends: A Comprehensive Analysis

This paper presents a comprehensive analysis of global human trafficking trends over a twenty-year period, leveraging a robust dataset from the Counter Trafficking Data Collaborative (CTDC). The study unfolds in a systematic manner, beginning with a detailed data collection phase, where ethical and legal standards for data usage and privacy are strictly observed. Following collection, the data undergoes a rigorous preprocessing stage, involving cleaning, integration, transformation, and normalization to ensure accuracy and consistency for analysis. The analytical phase employs time-series analysis to delineate historical trends and utilizes predictive modeling to forecast future trajectories of human trafficking using the advanced analytical capabilities of Power BI. A comparative analysis across regions—Africa, the Americas, Asia, and Europe—is conducted to identify and visualize the distribution of human trafficking, dissecting the data by victim demographics, types of exploitation, and duration of victimization. The findings of this study not only offer a descriptive and predictive outlook on trafficking patterns but also provide insights into the regional nuances that influence these trends. The article underscores the prevalence and persistence of human trafficking, identifies factors contributing to its evolution, and discusses the implications for policy and law enforcement. By integrating a methodological approach with quantitative analysis, this research contributes to the strategic planning and resource allocation for combating human trafficking. It highlights the necessity for continued research and international cooperation to effectively address and mitigate this global issue. The implications of this research are significant, offering actionable insights for policymakers, law enforcement, and advocates in the ongoing battle against human trafficking.

基于整体开发地块的能源中心设计运行实践——上海西岸传媒港能源中心

本文以上海西岸传媒港能源中心为例,介绍在城市核心区域整体开发的背景下建设能源中心的设计运行实例,着重介绍区域能源中心的负荷预测、技术方案确定、站址布置、运行策略设定及投产初期实际运行数据等方面。该能源中心采用燃气分布式系统结合电制冷系统、燃气直燃型溴化锂吸收式机组、水蓄能等复合技术手段,结合地块整体开发特征,合理利用地下空间设置了机房、蓄能装置,充分体现“因地制宜、多能互补”的设计原则。

基于SVM的区域物流需求建模与预测仿真——以浙江省为例

为了提升区域物流需求预测的准确度,提出了一种基于支持向量机的物流需求预测方法。首先,通过主成分分析法对区域物流需求影响指标进行筛选,然后输入样本数据进行学习,最终建立区域物流需求与影响指标之间的非线性模型。基于浙江省2002—2021年的物流需求进行仿真分析,结果显示,相比于BP神经网络,SVM在区域物流需求预测方面有更高的预测精度,具有广泛的应用前景。

地区诚信与分析师盈利预测准确性:来自中国A股上市公司的证据

地区诚信作为我国非正式制度的重要组成部分,能够有效监督和约束上市公司行为。以2007—2018年中国沪深A股上市公司为研究对象,从北大法宝各地级市诈骗案判决文书中获取构造年度地级市层面诚信水平衡量指标的数据,进而探究地区诚信对分析师盈利预测准确性的影响。结果表明:(1)地区诚信水平越高,分析师对该地区上市公司盈利预测的分歧度和平均误差越小,这种作用在国有企业、内部治理水平低和金融发展程度低的地区样本中更明显,表明地区诚信可以部分替代所有制、内部治理水平和金融发展程度对分析师盈利预测准确性的作用。(2)机制检验发现,地区诚信通过提高上市公司信息披露质量来降低分析师盈利预测的分歧度和平均误差。因此,应完善社会诚信体系建设,营造良好的营商环境和氛围,提升地区诚信水平,降低信息不对称程度和减少分析师盈利预测误差,进而推动资本市场高质量发展。

基于历史天气的区域电网负荷预测

随着社会经济的迅速发展,人们对电能的需要日益增加,但是在电网运行中,常常会出现电力产能过剩或者不足的情况,为保证电力系统安全稳定、经济运行,就必须掌握各种区域电网负荷的变化规律和发展趋势。论文对重庆市区供电分公司供电区域电网中长期负荷进行预测,提出一种预测区域电网中长期负荷的方法,即一种基于前12个月历史天气条件和区域电网负荷关联关系的多元非线性拟合的特征参数因子曲线的中长期负荷预测方法,建立基于不同算法的多种预测模型,通过归一化处理,得到的区域电网中长期负荷预测的精度高,与实际区域电网负荷之间的误差小,对于区域电网中长期负荷预测分析具有重要参考利用价值。

基于区域型综合能源系统的规划寻优分析

以某区域综合能源系统项目实例为基础,从区域能源资源评估、负荷预测、综合能源系统建模、规划寻优方法等方面,对区域综合能源系统规划项目进行详细的案例分析,以期达到冷、热、电区域型综合能源系统的多能互补优势和能量梯级利用,为区域型综合能源系统规划项目提供参考借鉴。