Journal of Atmospheric and Solar-Terrestrial Physics最新文献

A hybrid deep learning based framework for prediction of rice yield through integration of biophysical parameters and optical remote sensing data in India 基于混合深度学习的框架，通过整合印度的生物物理参数和光学遥感数据来预测水稻产量

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-14 DOI: 10.1016/j.jastp.2026.106734

Rafath Samrin , Pundru Chandra Shaker Reddy , K. Arun Kumar , Natha Deepthi , C. Mithra , S Bhargavi Latha , Sucharitha Yadala , Gopal Kumar Thakur

India is the dominant player in the cultivation of rice around the world. Rice yield prediction can be considered as a problem that needs to be solved. Accurate and timely prediction of rice yield can provide meaningful benefits to crop yield. Time series models are widely used for rice yield prediction, but their accuracy remains inadequate. Despite their prominence, they often fail to deliver the required precision. This study considers one of the most practical machine learning (ML) methods for predicting rice yield, allowing forecasts for the next five years. The research carried out shows the rice yield prediction done using the hybrid framework which integrates multiple linear regression (MLR) with long-short-term memory (LSTM) and its performance is compared with state-of-the-art models. The yield is forecasted from the current year through the next five years, up to 2029. The data used for the prediction model will be 1998 to 2023 from four districts of West Bengal and Uttar Pradesh. An important finding of the study was that it is possible to predict rice harvest five years in advance of actual harvest, providing useful information for agricultural decision making and planning. Researchers, policy makers and farmers can all benefit from better food security planning and resource management thanks to the study's findings, which shed light on the possibilities of combining remote sensing with biophysical parameters using ML models. Measures used to assess the suggested model include R², RMSE, MAE, MSE, accuracy (Acc), F1 score (F1), recall (Re) and precision (Pe), among others. The suggested approach yields improved accuracy, R², RMSE, MAE, and MSE values of 0.9823, 0.956, 0.1436, 0.021, and 0.198, respectively.

印度是世界水稻种植的主导者。水稻产量预测是一个亟待解决的问题。准确、及时地预测水稻产量对作物产量具有重要意义。时间序列模型被广泛应用于水稻产量预测，但其精度仍然不足。尽管它们很突出，但它们往往无法提供所需的精度。这项研究考虑了预测水稻产量的最实用的机器学习（ML）方法之一，可以预测未来五年的产量。研究结果表明，将多元线性回归（MLR）与长短期记忆（LSTM）相结合的混合框架进行了水稻产量预测，并与现有模型进行了性能比较。产量预测从今年到未来五年，直到2029年。用于预测模型的数据将是1998年至2023年，来自西孟加拉邦和北方邦的四个地区。这项研究的一个重要发现是，可以提前5年预测水稻的收成，为农业决策和规划提供有用的信息。由于这项研究的发现，研究人员、政策制定者和农民都可以从更好的粮食安全规划和资源管理中受益，它揭示了利用机器学习模型将遥感与生物物理参数结合起来的可能性。用于评估建议模型的措施包括R2， RMSE， MAE， MSE，准确度（Acc）， F1分数（F1），召回率（Re）和精度（Pe）等。该方法的准确率、R2、RMSE、MAE和MSE分别为0.9823、0.956、0.1436、0.021和0.198。

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引用次数: 0

How the East Asian subtropical westerly jet shapes the interannual variability of the South China Sea summer monsoon onset and the associated thermal forcing effect from the Tibetan plateau 东亚副热带西风急流如何影响南海夏季风爆发的年际变率及青藏高原相关的热强迫效应

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-13 DOI: 10.1016/j.jastp.2026.106736

Chengyu Song , Jing Wang , Yanju Liu

Based on the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and the Global Precipitation Climatology Project (GPCP) data, this research examines the atmospheric anomalies associated with the interannual variability of the South China Sea (SCS) summer monsoon (SCSSM) onset, focusing particularly on its connection with positional changes of the subtropical westerly jet (SWJ) and thermal conditions over the eastern Tibetan Plateau (TP). The analysis reveals distinct difference of the circulation patterns between early and late monsoon onset years, marked by pronounced cyclonic anomalies with intensified rainfall during early onsets, contrasted by anticyclonic patterns and reduced precipitation during delayed onsets. A key finding demonstrates that early SCSSM onset years coincide with a southward displacement of the upper-level SWJ north of the SCS. This positional shift generates upper-level ageostrophic southerly winds over the SCS, establishing a dipole pattern of vertical motion. Upper-level divergence and low-level convergence happen over the SCS (south of the jet core), and upper-level convergence with low-level divergence occur over the Yangtze River basin (north of the jet). Such configuration amplifies the meridional circulation anomaly, enhancing ascending motions in low-latitude East Asia while strengthening subsidence in mid-latitude regions. The study also further shows that thermal anomalies over the eastern TP significantly affect SWJ positioning and subsequent monsoon onset timing. Positive heating anomalies initiate an upper-tropospheric anticyclone, triggering eastward-propagating Rossby waves and downstream cyclonic circulation. This prompts a southward migration of the SWJ east of the TP, altering East Asian circulation patterns to facilitate an earlier SCSSM establishment. These results shed new light on the TP's role in regional climate modulation via upper-level jet dynamics, offering potential predictive value for monsoon onset forecasting.

基于美国国家环境预报中心/国家大气研究中心（NCEP/NCAR）再分析资料和全球降水气候学项目（GPCP）资料，研究了与南海夏季风（SCSSM）爆发年际变化相关的大气异常。重点分析了其与副热带西风急流（SWJ）位置变化和青藏高原东部热环境的关系。分析表明，季风早、晚开始年的环流模式存在明显差异，其特征是气旋异常明显，早开始时降水增强，而晚开始时则为反气旋模式，降水减少。一个关键的发现表明，南海ssm早期开始的年份与南海以北的上层SWJ向南移动的年份相吻合。这种位置变化在南海上空产生了上层的非地转偏南风，建立了垂直运动的偶极子模式。南海（急流核以南）上空出现高层辐散和低层辐合，长江流域（急流核以北）上空出现高层辐合和低层辐合。这种配置放大了经向环流异常，增强了东亚低纬度地区的上升运动，而加强了中纬度地区的下沉。研究还进一步表明，东太平洋的热异常显著影响SWJ的定位和随后的季风爆发时间。正加热异常引发对流层上层反气旋，触发向东传播的罗斯比波和下游气旋环流。这促使西南偏南在太平洋高压带以东向南迁移，改变了东亚环流模式，促进了南海高压的早期建立。这些结果揭示了青藏高原通过高空急流动力学在区域气候调节中的作用，为季风的发生预报提供了潜在的预测价值。

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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 : 2026-01-11 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

Nighttime light data as a proxy for assessing air pollution in urban landscapes of India: A remote sensing perspective 夜间灯光数据作为评估印度城市景观空气污染的代理：遥感视角

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-11 DOI: 10.1016/j.jastp.2026.106732

Anisha Jalathota , Mahesh Pathakoti , Jaya Saxena , Kanchana Lakshmi Asuri , Mahalakshmi Venkata Dangeti , Ramesh H. Gowda , Sampath Kumar , Srinivasa Rao Goru , Prakash Chauhan

Deterioration of air quality due to increasing anthropogenic activities adversely affect human health and the environment. This study presents the space-time variability of primary pollutants, namely columnar concentrations of atmospheric Carbon monoxide (XCO), Nitrogen dioxide (XNO₂), and Sulphur dioxide (XSO₂), across four major Indian cities, Hyderabad, New Delhi, Chandigarh, and Guwahati during 2019–2023 using the Sentinel-5P/TROPOMI open datasets from Google Earth Engine platform. Across the study sites, atmospheric CO indicates less variability, ranging from ±5.78 ppb to ±7.7 ppb, whereas atmospheric NO₂ and SO₂ observed moderate to high variability of distribution ranging from ±0.04 ppb to ±0.12 ppb, and ±0.99 ppb to ±1.52 ppb, respectively. The Suomi-NPP VIIRS night-time light product used in this study signifies the extent and intensity of urbanization. Alarming trends in spatially and temporally increased pollutant concentrations are observed in Guwahati due to rapid urban expansion and unregulated biomass burning. TROPOMI and MOPITT sensors demonstrated strong agreement in CO retrievals, with a relative biases ranging from −0.40 % to 5.16 %. TROPOMI derived XCO and XNO₂ retrievals show good agreement with Central Pollution Control Board measurements data. Thus, comprehensive analysis of these pollutants over these cites revealed a general increase in pollutant concentrations driven by urban development and seasonal wind patterns. The findings demonstrate the robustness of multi-sensor remote sensing datasets and urbanization indicators for monitoring air quality over rapidly developing Indian cities. The study provides valuable baseline information for developing and strengthening city-specific action plans to achieve clean air under the National Clean Air Programme (NCAP).

人为活动增加造成的空气质量恶化对人类健康和环境产生不利影响。利用谷歌Earth Engine平台的Sentinel-5P/TROPOMI开放数据集，研究了2019-2023年印度海得拉巴、新德里、昌迪加尔和古瓦哈蒂4个主要城市大气中一氧化碳（XCO）、二氧化氮（XNO2）和二氧化硫（XSO2）柱状浓度的时空变化。在整个研究地点，大气CO的变异性较小，范围为±5.78 ppb至±7.7 ppb，而大气NO2和SO2的分布变异性分别为±0.04 ppb至±0.12 ppb，±0.99 ppb至±1.52 ppb。本研究中使用的Suomi-NPP VIIRS夜间照明产品表明了城市化的程度和强度。由于快速的城市扩张和不受管制的生物质燃烧，在古瓦哈蒂观察到污染物浓度在空间和时间上增加的惊人趋势。TROPOMI和MOPITT传感器在CO检索中表现出很强的一致性，相对偏差范围为- 0.40%至5.16%。TROPOMI提取的XCO和XNO2数据与中央污染控制委员会的测量数据吻合良好。因此，对这些城市的这些污染物的综合分析表明，受城市发展和季节性风模式的驱动，污染物浓度普遍增加。研究结果表明，用于监测快速发展的印度城市空气质量的多传感器遥感数据集和城市化指标具有稳健性。这项研究为制定和加强城市具体行动计划提供了宝贵的基线信息，以实现国家清洁空气计划（NCAP）下的清洁空气。

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引用次数: 0

Long-term air quality forecasting in Korba, India (2025–2047): A hybrid model using 44-year satellite data 印度Korba的长期空气质量预测（2025-2047）：使用44年卫星数据的混合模型

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-07 DOI: 10.1016/j.jastp.2026.106730

Abhimanyu Kumar Gond, Aarif Jamal, Tarun Verma

This study employs a hybrid Temporal Convolutional Network (TCN) and Transformer model to forecast air quality trends in Korba, Chhattisgarh, a critically polluted industrial hub dominated by extensive coal mining (including Gevra, Dipka, and Kusmunda mines), multiple thermal power plants, aluminium smelters, and cement production, from 2025 to 2047. Utilizing 44 years of satellite-derived data (1980–2024), the model integrates meteorological variables, vegetation indices (NDVI and NDBI), and coal mining metrics. Under a moderate policy change scenario, it predicts gradually rising pollutant levels: PM₁₀ (32–84 μg/m³), PM₂.₅ (10–33 μg/m³), SO₂ (7.25–12 μg/m³), NO₂ (5.25–9 μg/m³), and AQI (45–110, Moderate per CPCB Delhi standards), with seasonal patterns showing reduced concentrations during monsoon due to rainfall washout and elevated levels in summer and winter owing to limited atmospheric dispersion. The model demonstrated strong performance (R² = 0.75–0.91; RMSE = 1.09–11.91), effectively capturing both short- and long-term trends driven by industrial emissions and environmental factors. A sensitivity analysis further revealed the model's robust response to ±10–20 % variations in key drivers, with the most decisive influence from coal production increases, which could be elevated by 20–30 % and add 10–15 AQI points, while reduced rainfall, higher temperatures, and lower NDVI amplified dust resuspension and secondary pollutant formation. Uncertainty analysis identified high-risk periods, including elevated PM₂.₅ variability in 2025–2026 and AQI in 2033–2038. Validation against ground-truth data from Urja Nagar, Rampur stations, and MODIS Satellite-derived AQI (From January–September 2025; R²: 0.72, 0.61, and 0.58) confirmed forecasted AQI (30–164, mostly Moderate), posing potential respiratory risks to vulnerable groups upon prolonged exposure. These projections highlight escalating public health threats, particularly respiratory and cardiovascular diseases, underscoring the need for urgent interventions, such as stricter emission controls, a transition to renewable energy sources, the adoption of an air quality health index, and enhanced reforestation for dust mitigation. This work offers a robust, data-driven baseline and scalable framework for sustainable air quality management in industrial regions, aligning with India's vision for balanced development by 2047.

本研究采用混合时间卷积网络（TCN）和变压器模型来预测恰蒂斯加尔邦科尔巴的空气质量趋势，科尔巴是一个严重污染的工业中心，主要是广泛的煤矿开采（包括Gevra， Dipka和Kusmunda矿山），多个热电厂，铝冶炼厂和水泥生产，从2025年到2047年。利用44年的卫星数据（1980-2024），该模型整合了气象变量、植被指数（NDVI和NDBI）和煤炭开采指标。在中等政策变化情景下，它预测污染物水平逐渐上升：PM10 (32-84 μg/m3), PM2.5 (10-33 μg/m3), SO2 (7.25-12 μg/m3), NO2 （5.25-9 μg/m3）和AQI （45-110， CPCB德里标准中等），季节性模式显示季风期间由于降雨冲洗而浓度降低，夏季和冬季由于大气分散有限而浓度升高。该模型表现出较强的性能（R2 = 0.75-0.91; RMSE = 1.09-11.91），有效地捕捉了工业排放和环境因素驱动的短期和长期趋势。敏感性分析进一步表明，该模型对关键驱动因素±10 - 20%的变化具有较强的响应，其中最具决定性的影响来自煤炭产量的增加，煤炭产量可能增加20 - 30%，并增加10-15个AQI点，而降雨量减少、温度升高和NDVI降低则会放大粉尘再悬浮和二次污染物的形成。不确定性分析确定了高风险时期，包括2025-2026年PM2.5变异性升高和2033-2038年空气质量指数升高。根据Urja Nagar、Rampur站点和MODIS卫星获得的空气质量指数（2025年1月至9月；R2: 0.72、0.61和0.58）的地面真实数据进行验证，证实了预测的空气质量指数（30-164，大部分为中等），对长期暴露在空气中的弱势群体构成潜在的呼吸风险。这些预测突出表明，公共健康威胁不断升级，特别是呼吸系统和心血管疾病，强调需要采取紧急干预措施，例如更严格的排放控制、向可再生能源过渡、采用空气质量健康指数以及加强重新造林以减少粉尘。这项工作为工业地区的可持续空气质量管理提供了一个强大的、数据驱动的基线和可扩展的框架，与印度到2047年实现平衡发展的愿景保持一致。

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引用次数: 0

Assessing meteorological drought indices for monitoring agricultural drought using SPEI: a remote sensing approach 利用SPEI评估农业干旱监测的气象干旱指数：遥感方法

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-06 DOI: 10.1016/j.jastp.2026.106726

Kanhu Charan Panda , Pradosh Kumar Paramaguru , Ram Mandir Singh , Sudhir Kumar Singh

Estimating agricultural drought is hindered by limited data availability. Conversely, assessing meteorological drought is very easy as it requires only meteorological data that are easy to obtain. Thus, the study aimed to find meteorological drought indices suitable for assessing agricultural drought in a tropical river basin using remote sensing satellite data. Seven meteorological drought indices, i.e., Standardised Precipitation Index (SPI), Deciles Index (DI), Percent of Normal Index (PNI), Rainfall Anomaly Index (RAI), China-Z Index (CZI), Modified China-Z Index (MCZI), and Z-score Index (ZSI) were compared with an efficient agricultural drought index, viz., Standardised Precipitation Evapotranspiration Index (SPEI) for the Subarnarekha River basin using the NASA POWER satellite data. It was noticed that the CZI had the highest correlation with the 3-month SPEI (R² = 0.88–0.94, followed by the RAI (R² = 0.85–0.90). The meteorological drought indices were divided into three groups based on the ability to replicate SPEI: ‘excellent’ for SPI, CZI, and MCZI; ‘good’ for ZSI and RAI; and ‘poor’ for PNI and DI. Relying solely on correlation coefficients to evaluate drought indices provided erroneous results. However, integrating correlation, RMSE, and statistical distribution enhanced the robustness of the assessment. This approach reclassified MCZI and SPI from “good” to “excellent”. SPI and CZI showed better alignment with SPEI distributions throughout both dry and wet periods, establishing them as the most reliable alternatives for evaluating agricultural drought. The findings emphasize the importance of comprehensive evaluation criteria that incorporate several statistical metrics for reliable drought assessment in tropical and data-scarce regions.

有限的可用数据阻碍了农业干旱的估计。相反，评估气象干旱非常容易，因为它只需要容易获得的气象数据。因此，本研究旨在利用遥感卫星数据寻找适合于评价热带河流流域农业干旱的气象干旱指数。利用NASA POWER卫星数据，对苏巴那列哈河流域的标准化降水指数（SPI）、十分位数指数（DI）、正常百分率指数（PNI）、降雨异常指数（RAI）、中国- z指数（CZI）、修正中国- z指数（MCZI）和z得分指数（ZSI）等7个气象干旱指数与标准化降水蒸散指数（SPEI）进行了比较。CZI与3个月SPEI的相关性最高（R2 = 0.88 ~ 0.94），其次是RAI （R2 = 0.85 ~ 0.90）。根据气象干旱指数对SPI、CZI和MCZI的复制能力，将气象干旱指数分为3类：SPI、CZI和MCZI为“优秀”；对ZSI和RAI来说“好”；PNI和DI为“穷”。单纯依靠相关系数来评价干旱指数的结果是错误的。然而，整合相关、RMSE和统计分布增强了评估的稳健性。该方法将MCZI和SPI从“良好”重新分类为“优秀”。SPI和CZI在干湿期均与SPEI分布具有较好的一致性，是评价农业干旱最可靠的替代指标。研究结果强调了综合评价标准的重要性，这些标准包括若干统计指标，以便在热带和数据匮乏地区进行可靠的干旱评估。

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引用次数: 0

Forecasting seasonal surface ozone trends using radial basis function networks and ground level monitoring data 利用径向基函数网络和地面监测资料预测季节性地表臭氧趋势

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-06 DOI: 10.1016/j.jastp.2026.106725

Sharanya Suraboyina , Gangagni Rao Anupoju , Anand Polumati

Accurate forecasting of tropospheric surface ozone (O₃) concentrations is increasingly vital due to rapid urbanization and industrial growth, which have intensified photochemical smog episodes and associated health and environmental risks. Traditional deterministic models often struggle to capture the complex nonlinear relationships among meteorological and precursor variables influencing O₃ formation. Addressing this limitation, the present study introduces a Radial Basis Function Network (RBFN) model for seasonal forecasting of surface O₃ concentrations using real-time ground monitoring data (2014–2017) from an urban air-quality station in Hyderabad, India (TIFR–NBF: Tata Institute of Fundamental Research–National Balloon Facility). The novelty of this work lies in applying RBFNs to capture seasonal and diurnal O₃ variations with high precision, using input parameters including NO, NO₂, NO_x, CO, VOCs, and NMHCs. The RBFN model was trained with 80 % of the dataset and validated using 20 %, and its performance was assessed through Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Willmott's index(WI)Comparative analysis with the nonlinear regression-based Response Surface Methodology (RSM) revealed that the RBFN achieved superior predictive accuracy with lower RMSE (0.0038 vs. 0.0185), MSE (0.000014 vs. 0.000342), MAE (0.0021 vs. 0.0094), and (WI = 0.9999 vs. 0.9812), The model effectively captured seasonal and diurnal O₃ peaks, particularly during summer and pre-monsoon periods. Overall, this study demonstrates the robustness, adaptability, and real-time forecasting potential of RBFNs, underscoring their application in early-warning systems, air-quality management, and policy-driven pollution mitigation strategies for rapidly urbanizing regions.

由于快速的城市化和工业增长加剧了光化学烟雾事件和相关的健康和环境风险，对流层表面臭氧（O3）浓度的准确预报变得越来越重要。传统的确定性模型往往难以捕捉影响臭氧形成的气象和前兆变量之间复杂的非线性关系。为了解决这一限制，本研究引入了径向基函数网络（RBFN）模型，利用印度海德拉巴城市空气质量站（TIFR-NBF：塔塔基础研究所-国家气球设施）的实时地面监测数据（2014-2017年）对地表O3浓度进行季节性预测。这项工作的新颖之处在于，利用输入参数，包括NO、NO2、NOx、CO、VOCs和NMHCs，应用rbfn以高精度捕获季节和日O3变化。RBFN模型使用80%的数据集进行训练，20%的数据集进行验证，并通过均方误差（MSE）、平均绝对误差（MAE）、均方根误差（RMSE）和Willmott指数（WI）对其性能进行评估。与基于非线性回归的响应面方法（RSM）的对比分析表明，RBFN模型在RMSE （0.0038 vs. 0.0185）、MSE （0.000014 vs. 0.000342）、MAE （0.0021 vs. 0.0094）、RBFN模型预测精度较低的情况下取得了较好的预测精度。和（WI = 0.9999 vs. 0.9812），该模式有效捕获了季节和日O3峰值，特别是在夏季和季风前期。总体而言，本研究证明了rbfn的鲁棒性、适应性和实时预测潜力，强调了其在快速城市化地区的预警系统、空气质量管理和政策驱动的污染缓解战略中的应用。

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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 : 2026-01-05 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。

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引用次数: 0

A note on effects of fossil fuel reduction policies on atmospheric carbon dioxide buildup and global warming 关于减少化石燃料政策对大气二氧化碳积累和全球变暖的影响的说明

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-05 DOI: 10.1016/j.jastp.2026.106724

Baris Baykant Alagoz , Cemal Keles , Burhan Baran

Global warming is a near-future major problem that has serious consequences on life on Earth. Accumulation of CO₂ emission in the atmosphere becomes a dominating factor in the global warming process. This study introduces a global temperature uptrend model that considers effects of atmospheric CO₂ buildup because of combustion of natural gas, oil and coal fuels. The model is based on earth's energy balance modeling and estimates impacts of fossil fuel-based CO₂ emission on the average global temperature. To evaluate effects of fossil fuel reduction policies, this model is used to study yearly 3 %, 5 % and 7 % fossil fuel reduction scenarios on 2013–2022 global fuel consumption data. This backward projection indicated that the cumulative carbon dioxide emission of 10-year global fuel consumption has potential of about +0.023 °C/y global temperature rise between 2013 and 2022 years. Moreover, this model is also used for forward projections to discuss the effects of these 10-year-long fossil fuel reduction programs on future trends of global temperature anomaly.

全球变暖是一个在不久的将来会对地球上的生命造成严重后果的重大问题。大气中CO2排放的积累成为全球变暖过程中的主导因素。本研究引入了一个全球温度上升趋势模型，该模型考虑了天然气、石油和煤炭燃料燃烧引起的大气二氧化碳积累的影响。该模型基于地球能量平衡模型，并估算了化石燃料二氧化碳排放对全球平均温度的影响。为了评估化石燃料减排政策的效果，该模型研究了2013-2022年全球燃料消耗数据中每年减少3%、5%和7%化石燃料的情景。这一逆向预测表明，在2013年至2022年期间，10年全球燃料消耗的累积二氧化碳排放量有可能使全球气温上升约+0.023°C/年。此外，该模型还用于正演预估，以讨论这些10年化石燃料减少计划对未来全球温度异常趋势的影响。

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引用次数: 0

Multifractal, recurrence, and information–Theoretic characterization of coupled proton dynamics in the solar wind 太阳风中耦合质子动力学的多重分形、递归和信息论表征

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2026-01-05 DOI: 10.1016/j.jastp.2025.106713

A.O. Adelakun

This study applies a unified nonlinear framework–combining multifractal detrended fluctuation analysis (MF–DFA), recurrence quantification analysis (RQA), and transfer entropy (TE)–to investigate the dynamical coupling between proton density (PD), temperature (PT), and speed (PS) in the solar wind. The results reveal a consistent hierarchy across all diagnostics: temperature-speed coupling dominates (

PT − PS > PD − PS > PD − PT

), governing the principal pathway of energy redistribution. The PT–PS pair exhibits the broadest joint singularity spectrum and the strongest multifractal asymmetry, while recurrence metrics show sustained synchronization and structured determinism. Transfer–entropy analysis confirms a thermal–compressive

\to

kinetic directionality, with PD and PT acting as information sources (

PD = + 0.869

,

PT = + 0.457

) and PS acting as the receiver (

PS = - 1.326

). These findings depict the solar wind as a self-organized, multiscale system in which intermittent synchronization between thermal and kinetic processes regulates flow variability. The integrated multifractal–recurrence–entropy framework provides a new diagnostic tool for identifying regime transitions and improving space-weather predictability.

本文采用统一的非线性框架，结合多重分形去趋势波动分析（MF-DFA）、递归量化分析（RQA）和传递熵（TE），研究了太阳风中质子密度（PD）、温度（PT）和速度（PS）之间的动力学耦合。结果表明，在所有诊断中存在一致的层次结构：温度-速度耦合占主导地位（PT - PS>PD - PS>PD - PT），控制着能量再分配的主要途径。PT-PS对表现出最宽的联合奇异谱和最强的多重分形不对称性，而递推度量则表现出持续的同步性和结构确定性。传递熵分析证实了热压→动力学的方向性，PD和PT为信息源（PD=+0.869， PT=+0.457）， PS为接收者（PS= - 1.326）。这些发现将太阳风描述为一个自组织的多尺度系统，在这个系统中，热过程和动力学过程之间的间歇性同步调节着流动的可变性。综合多重分形-递归-熵框架为识别状态转换和提高空间天气可预测性提供了一种新的诊断工具。

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引用次数: 0