Journal of Atmospheric and Solar-Terrestrial Physics最新文献_第3页

Solar-geomagnetic activity effect on NmE over Ouagadougou, an equatorial anomaly station in the African sector 太阳地磁活动对非洲赤道异常站瓦加杜古NmE的影响

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.jastp.2026.106756

A.K. Kazeem , A.B. Rabiu , C. Amory-Mazaudier

The variability of the ionospheric E-region peak electron density (NmE) and its response to solar, solar wind, and geomagnetic forcing are investigated at Ouagadougou (12.4° N, 358.5° E; dip latitude +1.45°), an equatorial station in the African sector. Daytime hourly measurements of the E-layer critical frequency (foE) are analysed in conjunction with selected solar, interplanetary, and geomagnetic indices over the period 1976-1997, spanning solar cycles 21 and 22. The results show a pronounced diurnal variation in NmE and a strong dependence on solar activity. Seasonal variability is weak and not consistently observed across years, as represented by high, moderate, and low solar activity (1991, 1993, and 1996). No clear linear long-term relationship is found between NmE and sunspot number. Geomagnetic activity exerts a measurable influence on NmE, particularly during periods of high and low solar activity. Among the parameters examined, the interplanetary magnetic field magnitude exhibits the strongest association with NmE, while the planetary amplitude index, disturbance storm time, solar wind dynamic pressure, and the southward interplanetary magnetic field component show weaker but detectable effects. Solar wind speed and sunspot number contribute only marginally to NmE variability. These results emphasise the coupled roles of solar and magnetospheric processes in controlling the equatorial E-region ionosphere over the African sector.

在非洲扇区赤道站瓦加杜古（12.4°N, 358.5°E，倾角+1.45°）研究了电离层E区峰值电子密度（NmE）的变率及其对太阳、太阳风和地磁强迫的响应。在1976-1997年期间，结合选定的太阳、行星际和地磁指数，分析了e层临界频率（foE）的白天每小时测量值，涵盖了第21和22太阳周期。结果表明，NmE有明显的日变化，对太阳活动有很强的依赖性。季节变率较弱，而且在不同年份观测到的变化并不一致，如太阳活动高、中、低（1991年、1993年和1996年）所示。NmE与太阳黑子数之间没有明显的长期线性关系。地磁活动对NmE产生可测量的影响，特别是在太阳活动高峰和低谷期间。其中行星际磁场星等与NmE的相关性最强，而行星幅值指数、扰动风暴时间、太阳风动压和南向行星际磁场分量的影响较弱，但可以探测到。太阳风速度和太阳黑子数对NmE变率的贡献很小。这些结果强调了太阳和磁层过程在控制赤道e区非洲部分电离层中的耦合作用。

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引用次数: 0

Statistical analysis of lightning activity in recurrent lightning locations in northeastern Colombia 哥伦比亚东北部雷电多发地点闪电活动的统计分析

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-03-01 Epub Date: 2026-02-10 DOI: 10.1016/j.jastp.2026.106745

S. Ardila, E. Soto, K. Ríos, M. Romero

This study presents a temporal-based approach for identifying Recurrent Lightning Locations (RLLs) in the municipalities of Barrancabermeja and Yondó, in northeastern Colombia, using lightning data from 2014 to 2021. Results reveal that RLLs are predominantly situated near the Magdalena River. Temporally, lightning activity in RLSs shows a bimodal seasonal distribution with early morning peaks in stroke occurrence. The analysis of flash multiplicity and peak current distributions indicates that most events are single-stroke flashes with right-skewed current profiles, occasionally exceeding 75 kA. Notably, over 42% of RLLs are located outside high Ground Flash Density (GFD) zones, highlighting that lightning recurrence is not necessarily correlated with stroke density but is likely influenced by localized geographic and atmospheric factors. The study proposes a novel methodological framework centered on temporal recurrence rather than spatial density, offering practical implications for lightning risk assessment in infrastructure-critical regions. Results highlight the importance of considering both spatial and temporal dimensions in lightning risk assessment and offer a practical framework for improving lightning protection strategies in vulnerable regions. The application of temporal methodologies, as implemented in this study, constitutes a distinctive criterion for identifying sites with recurrent lightning activity, with a focus on the design of optimal lightning protection systems.

本研究利用2014年至2021年的闪电数据，提出了一种基于时间的方法，用于识别哥伦比亚东北部Barrancabermeja市和Yondó市的经常性闪电位置（rls）。结果表明，rll主要分布在Magdalena河附近。从时间上看，雷击活动呈双峰型季节性分布，清晨出现中暑高峰。对闪数和峰值电流分布的分析表明，大多数事件是电流曲线右斜的单次闪，偶尔超过75 kA。值得注意的是，超过42%的rls位于高地面闪电密度（GFD）区域之外，这突出表明闪电复发不一定与中风密度相关，而可能受到局部地理和大气因素的影响。该研究提出了一种新颖的方法框架，以时间重现而不是空间密度为中心，为基础设施关键地区的闪电风险评估提供了实际意义。研究结果强调了在雷电风险评估中同时考虑空间和时间维度的重要性，并为改进脆弱地区的雷电防护策略提供了一个实用框架。在本研究中实施的时间方法的应用，构成了识别经常性闪电活动地点的独特标准，重点是设计最佳的防雷系统。

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引用次数: 0

Comparison of precipitation-based drought indices: Drought assessment and monitoring in a TR82 region in Türkiye 基于降水的干旱指数比较：云南TR82地区干旱评价与监测

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.jastp.2026.106753

Utku Zeybekoglu

Drought is a multifaceted natural hazard with profound impacts on water resources, agriculture, ecosystems, and socio-economic systems. This study investigates meteorological drought in the TR82 Region of northern Türkiye, analysing precipitation data from ten meteorological stations spanning 1981–2023. Three precipitation-based drought indices—the Standardized Precipitation Index (SPI), China-Z Index (CZI), and Z-Score Index (ZSI)—were applied to assess drought characteristics, including frequency, severity, and spatial variability. Analysis revealed recurrent drought periods in 1981–1982, 1985–1986, 1989, 1992–1994, 1996, 2003–2004, 2006–2008, 2013, 2017, 2020, and 2022, indicating that drought occurrence in the region is neither sporadic nor isolated. Moderately dry and wet conditions were more frequent than extreme events, while prolonged droughts after 2000 suggest increasing persistence and intensification. Correlation analysis demonstrated very strong agreement among the indices, with mean correlation coefficients of 0.9956 (SPI–ZSI), 0.9953 (SPI–CZI), and 0.9943 (ZSI–CZI), confirming ZSI as a viable alternative to SPI for regional drought monitoring. In addition, the results are consistent with the presence of scale-invariant, nonlinear dynamics in precipitation variability, as indicated by observed power-law behavior and the framework of self-organized criticality (SOC) in rainfall systems, suggesting that short-term fluctuations are statistically related to longer-term patterns. The use of precipitation-based drought indices showed that the driest years align with the years of lowest annual precipitation, and similar correspondence is observed for wet years. These findings provide robust evidence of the TR82 Region's vulnerability to drought and underscore the importance of multi-index approaches and consideration of nonlinear climate dynamics for effective drought assessment and proactive water resource management.

干旱是一种多方面的自然灾害，对水资源、农业、生态系统和社会经济系统产生深远影响。利用1981-2023年10个气象站的降水资料，对云南北部TR82地区的气象干旱进行了研究。采用标准化降水指数（SPI）、中国- z指数（CZI）和z得分指数（ZSI） 3个基于降水的干旱指数来评估干旱特征，包括频率、严重程度和空间变异性。1981-1982年、1985-1986年、1989年、1992-1994年、1996年、2003-2004年、2006-2008年、2013年、2017年、2020年和2022年为干旱多发期，表明该地区的干旱既不是零星的，也不是孤立的。中度干湿条件比极端事件更频繁，而2000年之后的长期干旱表明持久性和强度增加。相关分析表明，指数间的平均相关系数分别为0.9956 （SPI - ZSI）、0.9953 （SPI - czi）和0.9943 (ZSI - czi)，表明ZSI可以替代SPI进行区域干旱监测。此外，观测到的幂律行为和降雨系统的自组织临界（SOC）框架表明，降水变率存在尺度不变的非线性动力学，结果与此一致，表明短期波动在统计上与长期模式相关。基于降水的干旱指数表明，最干旱年份与年降水量最低年份一致，湿润年份也存在类似的对应关系。这些发现为TR82地区的干旱脆弱性提供了强有力的证据，并强调了多指数方法和考虑非线性气候动力学对有效干旱评估和主动水资源管理的重要性。

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引用次数: 0

A simulation study on the key cloud microphysical processes in an extreme warm-sector heavy rainfall over the south China mountains 华南山区一次极端暖区强降水关键云微物理过程的模拟研究

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.jastp.2026.106740

Xuehan Dong , Jiangnan Li , Zhourong Liu , Jianfei Chen , Risheng Liu

This study employs the Weather Research and Forecasting (WRF) mesoscale model to simulate an extreme warm-sector heavy rainfall event that occurred on 17–18 June 2022, over Yuanbao Mountain in Liuzhou, Guangxi. The research focuses on the cloud microphysical characteristics and latent heat budget during the heavy precipitation stage, aiming to clarify the key physical mechanisms driving the intensification of the heavy rainfall. The simulation successfully reproduces the spatio-temporal evolution of this extreme precipitation. Solid-phase hydrometeors, namely snow and graupel, are found to dominate the precipitation process, exhibiting complementary vertical distributions that formed an efficient hydrometeor conversion chain and served as the primary source of rainwater. The intense release of condensation latent heat near the 0 °C level acted as the core energy source for precipitation, while the depositional latent heat release from ice-phase particles in the mid-upper levels served as a “leading indicator” for the extreme intensification of rainfall. The center of extreme precipitation was located on the southern windward slope of the mountains. There, warm and moist air parcels underwent adiabatic cooling during upslope ascent. Upon reaching saturation, water vapor condensed, releasing substantial latent heat and establishing a typical positive feedback mechanism of “orographic lifting–condensational heating.” This process significantly altered the local thermal structure and vertical motion of the atmosphere, representing the direct cause for the triggering of the extreme heavy rainfall.

本文利用WRF中尺度模式模拟了2022年6月17-18日发生在广西柳州元宝山的一次极端暖区强降水事件。研究重点是强降水阶段的云微物理特征和潜热收支，旨在阐明强降水增强的关键物理机制。模拟成功地再现了这次极端降水的时空演变过程。固相水成物，即雪和霰，在降水过程中占主导地位，呈现互补的垂直分布，形成有效的水成物转化链，是雨水的主要来源。0℃附近凝结潜热的强烈释放是降水的核心能量源，而中高层冰相颗粒的沉积潜热释放是降水极端强化的“先行指标”。极端降水中心位于山区南侧迎风坡。在那里，暖湿气团在上坡上升过程中经历绝热冷却。水汽达到饱和后冷凝，释放大量潜热，形成典型的“地形抬升-冷凝加热”正反馈机制。这一过程显著改变了局地热结构和大气垂直运动，是引发此次极端强降水的直接原因。

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引用次数: 0

Long-term spatiotemporal analysis of variation in soil moisture over Iran 伊朗土壤湿度变化的长期时空分析

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-02-01 Epub Date: 2026-01-05 DOI: 10.1016/j.jastp.2026.106727

Mohammad Darand , Ramtin Tashan

Soil moisture, as an important hydrological component, plays a crucial role in land–atmosphere interactions. Understanding variations in soil moisture content is highly valuable for effective water resource management, agricultural activities, and climate adaptation. The objective of this study is to analyze the spatiotemporal variations of surface soil moisture (0–7 cm) across Iran during the period 1979–2024. To achieve this, daily gridded data with a spatial resolution of 0.1° from the ERA5-Land dataset provided by ECMWF were used. The results showed that the spatial distribution pattern of soil moisture content follows the spatial patterns of precipitation, rainy days, and temperature across Iran. The modified Mann–Kendall test and Sen's slope estimator were applied to detect trends and their magnitudes at a 95 % confidence level. The findings indicated that soil moisture content across Iran has shown a decreasing trend, with an average reduction of 0.0032 m³ m⁻³ per decade. Temporally, the greatest reduction in soil moisture occurred during the cold and rainy seasons. Spatially, the decrease in soil moisture volume during winter was significantly higher in the northeastern part of the country compared to other regions. In some areas of the Alborz and Zagros highlands, however, soil moisture content exhibited an increasing trend. The findings of this study suggest that changes in soil moisture can be a potential predictor of climate change and can be applied to the fields of water resource management, agriculture, flood management, and hydrology.

土壤水分作为重要的水文成分，在陆地-大气相互作用中起着至关重要的作用。了解土壤水分含量的变化对有效的水资源管理、农业活动和气候适应具有重要价值。本研究的目的是分析1979-2024年伊朗地表土壤水分（0-7 cm）的时空变化。为了实现这一目标，使用了ECMWF提供的ERA5-Land数据集中空间分辨率为0.1°的每日网格数据。结果表明：伊朗土壤含水量的空间分布格局符合降水、阴雨天和气温的空间格局；采用改进的Mann-Kendall检验和Sen斜率估计器在95%的置信度水平上检测趋势及其幅度。结果表明，伊朗土壤含水量呈下降趋势，平均每10年减少0.0032 m3 m−3。从时间上看，土壤水分减少最大的季节是寒雨季节。从空间上看，东北地区冬季土壤水分体积降幅显著高于其他地区。然而，在阿尔博尔斯和扎格罗斯高原的一些地区，土壤含水量呈增加趋势。该研究结果表明，土壤湿度的变化可能是气候变化的潜在预测因子，并可应用于水资源管理、农业、洪水管理和水文学等领域。

{"title":"Long-term spatiotemporal analysis of variation in soil moisture over Iran","authors":"Mohammad Darand , Ramtin Tashan","doi":"10.1016/j.jastp.2026.106727","DOIUrl":"10.1016/j.jastp.2026.106727","url":null,"abstract":"<div><div>Soil moisture, as an important hydrological component, plays a crucial role in land–atmosphere interactions. Understanding variations in soil moisture content is highly valuable for effective water resource management, agricultural activities, and climate adaptation. The objective of this study is to analyze the spatiotemporal variations of surface soil moisture (0–7 cm) across Iran during the period 1979–2024. To achieve this, daily gridded data with a spatial resolution of 0.1° from the ERA5-Land dataset provided by ECMWF were used. The results showed that the spatial distribution pattern of soil moisture content follows the spatial patterns of precipitation, rainy days, and temperature across Iran. The modified Mann–Kendall test and Sen's slope estimator were applied to detect trends and their magnitudes at a 95 % confidence level. The findings indicated that soil moisture content across Iran has shown a decreasing trend, with an average reduction of 0.0032 m<sup>3</sup> m<sup>−3</sup> per decade. Temporally, the greatest reduction in soil moisture occurred during the cold and rainy seasons. Spatially, the decrease in soil moisture volume during winter was significantly higher in the northeastern part of the country compared to other regions. In some areas of the Alborz and Zagros highlands, however, soil moisture content exhibited an increasing trend. The findings of this study suggest that changes in soil moisture can be a potential predictor of climate change and can be applied to the fields of water resource management, agriculture, flood management, and hydrology.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"279 ","pages":"Article 106727"},"PeriodicalIF":1.9,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145940742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on rainfall prediction near Qinyang-Yichuan expressway based on deep learning 基于深度学习的秦益高速公路附近降水预测研究

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-02-01 Epub Date: 2025-12-31 DOI: 10.1016/j.jastp.2025.106716

Mingfei Zhang , Jiaheng Wang , Xiaogang Wei , Guotao Dou , Xiaorui Wang

In response to the challenges posed by complex terrain and the strong non-stationarity of rainfall—factors that complicate prediction efforts near the Qinyang-Yichuan Expressway and can easily trigger engineering disasters such as tunnel water ingress and slope landslides—this study aims to develop a high-precision rainfall prediction model for disaster prevention in engineering applications. Utilizing historical rainfall data from the region, we propose a hybrid model that integrates Empirical Mode Decomposition (EMD) and the Coyote Optimization Algorithm (COA) with Long Short-Term Memory (LSTM) networks (EMD-COA-LSTM), alongside a Comprehensive Index (CI). A comparative study of 13 deep learning models reveals that traditional models generally underestimate rainfall amounts in complex terrain predictions. The EMD-COA-LSTM model (CI = 5.78) effectively mitigates this bias and demonstrates significantly superior overall performance compared to the worst-performing baseline LSTM model (CI = 209.11). Further analysis shows that the ranking of overall model prediction performance and most evaluation metrics does not fully correspond with the coefficient of determination (R²), indicating that R² alone is insufficient for assessing model performance and should be combined with other metrics. Evaluation based on the CI indicator provides a more accurate reflection of the model's overall effectiveness. The findings of this study offer a more reliable scientific basis for geological disaster risk early warning, tunnel flood-season construction, and engineering safety management along expressway corridors, demonstrating substantial practical engineering value.

针对秦益高速公路沿线地形复杂、降雨非平稳性强等因素使预测工作复杂化、容易引发隧道突水、边坡滑坡等工程灾害的挑战，建立高精度的降雨预测模型，用于工程防灾应用。利用该地区的历史降雨数据，我们提出了一个将经验模态分解（EMD）和Coyote优化算法（COA）与长短期记忆（LSTM）网络（EMD-COA-LSTM）以及综合指数（CI）相结合的混合模型。一项对13种深度学习模型的比较研究表明，传统模型在复杂地形预测中通常低估了降雨量。EMD-COA-LSTM模型（CI = 5.78）有效地减轻了这种偏差，与表现最差的基线LSTM模型（CI = 209.11）相比，显示出显著优于整体性能。进一步分析发现，模型整体预测性能和大部分评价指标的排序与决定系数R2并不完全对应，说明单独使用R2不足以评价模型性能，应与其他指标结合使用。基于CI指标的评估可以更准确地反映模型的整体有效性。研究结果为高速公路沿线地质灾害风险预警、隧道汛期施工和工程安全管理提供了更为可靠的科学依据，具有较强的工程实用价值。

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引用次数: 0

Integrating LGBM machine learning methods with SHAP model to explain the impact of different environmental factors on precipitation in China 结合LGBM机器学习方法和SHAP模型解释不同环境因子对中国降水的影响

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-02-01 Epub Date: 2026-01-02 DOI: 10.1016/j.jastp.2025.106715

Yong Liu , Qingzu Luan , Pengguo Zhao

Precipitation processes are influenced by a combination of environmental variables, and it is crucial to identify the main contributing factors to rainfall. China has vast geographical diversity and complex terrain, with significant differences in precipitation formation mechanisms across climatic regions. Although machine learning models are efficient, they lack interpretability. Therefore, this study utilizes ground-based meteorological observation data, satellite remote sensing data, and atmospheric reanalysis data in conjunction with the explainable artificial intelligence (XAI) tool SHAP model combined with the Light Gradient Boosting Machine (LGBM) machine learning framework to investigate the impact of various environmental variables on precipitation in China and different climate regions, quantifying their contributions to precipitation. The results reveal that relative humidity (RH), Convective Available Potential Energy (CAPE), K index (KI), and ice water path (IWP) are the most critical factors influencing precipitation within China, ranking among the top four in terms of average SHAP values and significantly higher than other factors. Due to regional variations across different climate zones in China, land surface temperature (LST), wind direction (WD), evaporation (E), liquid water path (LWP), surface pressure (SP), cloud cover (CFC), aerosol optical depth (AOD), and relative humidity (RH) exert the most pronounced positive effects on precipitation at the national scale, while ice water path (IWP), K index (KI), cloud top height (CTH), and Convective Available Potential Energy (CAPE) demonstrate more significant negative impacts—stemming from the varying influences of each variable across different climate regions. Significant regional variations exist in precipitation drivers. CAPE shows stronger influence on precipitation in North Subtropical Humid climatic regions, while RH dominates in Marginal Tropical Humid and Mid-temperate Semi-humid zones. CTH is more pronounced in Plateau Temperate Semi-arid areas, IWP stands out in Mid-temperate Semi-arid climatic regions, KI predominates in Mid-temperate Arid areas, and LST plays a more significant role in Warm Temperate Semi-humid climatic regions.

降水过程受到多种环境变量的综合影响，确定影响降水的主要因素至关重要。中国地理多样性大，地形复杂，各气候区降水形成机制差异显著。虽然机器学习模型是有效的，但它们缺乏可解释性。因此，本研究利用地面气象观测资料、卫星遥感资料和大气再分析资料，结合可解释人工智能（XAI）工具SHAP模型和光梯度增强机（LGBM）机器学习框架，研究了中国和不同气候区各种环境变量对降水的影响，量化了它们对降水的贡献。结果表明，相对湿度（RH）、对流有效势能（CAPE）、K指数（KI）和冰水路径（IWP）是影响中国降水的最关键因子，其平均SHAP值均居前4位，且显著高于其他因子。由于中国不同气候区的区域差异，地表温度（LST）、风向（WD）、蒸发量(E)、液态水路径（LWP）、地表压力（SP）、云量（CFC）、气溶胶光学深度（AOD）和相对湿度（RH）对全国尺度降水的正向影响最为显著，而冰水路径（IWP）、K指数（KI）、云顶高度（CTH）、和对流有效势能（CAPE）表现出更显著的负影响，这是由于每个变量在不同气候区域的影响不同。降水驱动因素存在显著的区域差异。CAPE对北亚热带湿润气候区的降水影响较大，而RH对边缘热带湿润和中温带半湿润气候区的降水影响较大。CTH在高原温带半干旱区较为明显，IWP在中温带半干旱区较为突出，KI在中温带干旱区较为突出，而LST在暖温带半湿润气候区更为显著。

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引用次数: 0

A novel combination forecasting model for short-term wind power 一种新的短期风电组合预测模型

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-02-01 Epub Date: 2026-01-11 DOI: 10.1016/j.jastp.2026.106731

Na Guo, Hongyu Zheng, Qihuan Zhou, Xinjian Yin

The accurate prediction of short-term wind power is of great significance for wind power grid integration and grid stability. Short-term wind power is not only related to historical wind power, but also influenced by meteorological factors. This paper proposes a novel combination forecasting model for short-term wind power. The max-relevance min-redundancy feature selection algorithm is used to select meteorological feature data with high correlation and low redundancy. In response to the intermittent and non-stationary characteristics of short-term wind power, variational mode decomposition algorithm is used to decompose short-term wind power, and the generated components reduce the noise and redundancy of the original data. The components obtained by variational mode decomposition are combined with the main features of the extracted meteorological data as inputs to the long short-term memory network, and the outputs of each corresponding long short-term memory network are added to obtain the final prediction result. An improved sparrow search algorithm with better optimization performance is proposed and applied to hyper-parameters optimization of long short-term memory network. Two short-term wind power datasets from different regions and sampling intervals are selected as the research objects. The proposed combination forecasting model showed 28.99 %–89.31 % decrease in RMSE, 30.81 %–86.37 % decrease in MAPE, and 11.07 %–85.38 % decrease in MAE compared with other models on the first dataset. On the second dataset, three indicators decreased by 12.21 %–80.91 %, 50.18 %–87.54 %, and 9.99 %–83.01 %. The comparison results confirmed that the proposed combination forecasting model has high prediction accuracy for short-term wind power while ensuring small system deviations, and its real-time performance can also meet the needs of practical applications.

准确预测短期风电功率对风电并网和电网稳定具有重要意义。短期风电不仅与历史风电有关，还受气象因素的影响。提出了一种新的短期风电组合预测模型。采用最大相关最小冗余特征选择算法，对高相关性、低冗余的气象特征数据进行选择。针对短期风电间歇性、非平稳的特点，采用变分模态分解算法对短期风电进行分解，生成的分量降低了原始数据的噪声和冗余。将变分模态分解得到的分量与提取的气象数据的主要特征相结合，作为长短期记忆网络的输入，将各对应长短期记忆网络的输出相加，得到最终的预测结果。提出了一种优化性能更好的改进麻雀搜索算法，并将其应用于长短期记忆网络的超参数优化。选取两个不同地区、不同采样间隔的短期风电数据集作为研究对象。与第一个数据集上的其他模型相比，所提出的组合预测模型的RMSE下降28.99% ~ 89.31%，MAPE下降30.81% ~ 86.37%，MAE下降11.07% ~ 85.38%。在第二个数据集上，三个指标分别下降12.21% - 80.91%、50.18% - 87.54%和9.99% - 83.01%。对比结果表明，所提出的组合预测模型在保证系统偏差小的情况下，对短期风电具有较高的预测精度，实时性也能满足实际应用的需要。

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引用次数: 0

Corrigendum to ‘Optimized fuzzy logic algorithm for classifying meteorological and non-meteorological echoes in CINRAD/SA data in Poyang lake region’ [J. Atmos. Sol. Terr. Phys., Volume 278, 2026, 106708] “鄱阳湖地区CINRAD/SA数据中气象与非气象回波分类的优化模糊逻辑算法”的勘误表[J]。大气压。索尔,恐怖分子。理论物理。，卷278,2026,106708]

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-02-01 Epub Date: 2026-01-07 DOI: 10.1016/j.jastp.2026.106728

Landi Zhong , Haibo Zou , Xiaoyou Long , Jiaxin Wang , Yige Huang

引用次数: 0

Fine-tuning prior knowledge networks for seismic anomaly filtering in Schumann resonance 舒曼共振地震异常滤波的微调先验知识网络

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-02-01 Epub Date: 2026-01-09 DOI: 10.1016/j.jastp.2026.106729

Huang Yongming , Xie Yi , Que Mingyi , Lu Yong , Liu Gaochuan , Teng Yuntian

Detecting pre-earthquake anomalies in Schumann Resonance (SR) data is a significant challenge due to the low signal-to-noise ratio, with faint precursor signals often obscured by strong electromagnetic background noise. To address this, this paper proposes a novel, two-stage hybrid filtering method. The approach first uses a one-dimensional convolutional neural network (1D-CNN) to learn the patterns of a robust sliding interquartile range (IQR) detector, thereby acquiring “prior knowledge,” and then applies a fine-tuning stage to the network’s weights to selectively enhance pre-seismic patterns. The method was developed and validated using a multi-year dataset (2013–2021) of SR spectrograms and corresponding seismic events in California. Experimental results demonstrate a significant improvement in signal clarity: the average proportion of anomalies occurring within the 20 days prior to an earthquake increased from 69.91 % before filtering to 83.46 % after, representing a noteworthy average uplift of 13.55 %. This study confirms that our fine-tuned prior knowledge network is an effective approach for enhancing the visibility of potential seismic precursors in noisy SR data, reinforcing the potential of SR as a tool for short-term earthquake studies.

由于舒曼共振（Schumann Resonance， SR）数据的低信噪比，微弱的前兆信号往往被强电磁背景噪声所掩盖，因此检测震前异常是一项重大挑战。为了解决这个问题，本文提出了一种新的两级混合滤波方法。该方法首先使用一维卷积神经网络（1D-CNN）来学习鲁棒滑动四分位范围（IQR）检测器的模式，从而获得“先验知识”，然后对网络的权重应用微调阶段，以选择性地增强震前模式。该方法的开发和验证使用了多年数据集（2013-2021）的SR频谱图和相应的加利福尼亚地震事件。实验结果表明，信号的清晰性有了显著的提高：地震前20天内发生异常的平均比例从滤波前的69.91%增加到滤波后的83.46%，平均上升了13.55%。本研究证实，我们的微调先验知识网络是一种有效的方法，可以提高噪声SR数据中潜在地震前兆的可见性，从而增强SR作为短期地震研究工具的潜力。

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引用次数: 0