Atmospheric Research最新文献

英文中文

Assimilating FY-4B satellite aerosol data to improve PM₂.₅ and surface shortwave radiation prediction 利用FY-4B卫星气溶胶资料改善PM 2。5、表面短波辐射预测

IF 5.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-15 DOI: 10.1016/j.atmosres.2026.108764

Fangzheng Hu, Feiyue Mao, Yi Zhang, Jia Hong, Lin Zang, Zhaoliang Zeng, Sicong Lin, Wei Gong, Daniel Rosenfeld

引用次数: 0

Synergy between land-sea breeze dynamics and photochemistry governs the diurnal variability of primary and secondary organic aerosols in a tropical coastal atmosphere 陆海风动力学和光化学的协同作用支配着热带沿海大气中初级和次级有机气溶胶的日变化

IF 5.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-15 DOI: 10.1016/j.atmosres.2026.108777

Suresh K.R. Boreddy, Vijayakumar S. Nair, Prashant Hegde, S. Suresh Babu

引用次数: 0

Cost-benefit analysis of the nesting approach in HARMONIE-AROME for a supercell outbreak case study 一个超级单体暴发案例研究中HARMONIE-AROME筑巢方法的成本效益分析

IF 5.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-14 DOI: 10.1016/j.atmosres.2026.108774

J. Díaz-Fernández, C. Calvo-Sancho, M. López-Reyes, P. Bolgiani, J.J. González-Alemán, A. Morata, D. Santos-Muñoz, M.L. Martín

引用次数: 0

Atmospheric aerosol light scattering and absorption properties in the urban area of Milan (Italy): A focus on aerosol typing 意大利米兰市区大气气溶胶光散射和吸收特性：气溶胶类型的重点

IF 5.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-14 DOI: 10.1016/j.atmosres.2026.108776

Stefano Rimoldi, Marcus Acton-Bond, Vera Bernardoni, Laura Cadeo, Gianluigi Valli, Cristina Colombi, Rosario Cosenza, Manousos-Ioannis Manousakas, Benjamin Chazeau, Roberta Vecchi

引用次数: 0

AdaBoost-based satellite detection of summer daytime sea fog and low clouds in ice floe fields of the Arctic 基于adaboost的北极浮冰区夏季日间海雾和低云的卫星探测

IF 5.5 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-13 DOI: 10.1016/j.atmosres.2026.108775

Huiyun Ma, Jiedong Liu, Zengwei Liu, Huihui Feng, Guannan Li, Runxi Gu

引用次数: 0

Unravelling the drivers of the April–May 2024 extreme precipitation event in Rio Grande do Sul 揭开2024年4 - 5月巴西南格兰德州极端降水事件的驱动因素

IF 4.4 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-12 DOI: 10.1016/j.atmosres.2026.108773

Albenis Pérez-Alarcón , Rogert Sorí , Milica Stojanovic , Renata Libonati , Ricardo M. Trigo , Raquel Nieto , Luis Gimeno

This study examines the extreme precipitation event that caused unprecedented flooding in Rio Grande do Sul (RS), Brazil, during April–May 2024, leading to record-breaking floods in Porto Alegre. Using data from the European Centre for Medium-Range Weather Forecasts ERA5, the high-resolution Multi-Source Weighted-Ensemble Precipitation dataset, a Lagrangian moisture tracking approach, and an analogue-based analysis, we identified the atmospheric drivers and moisture sources fueling this event. Results show that a persistent, quasi-stationary dipole configuration, consisting of a high over the South Atlantic and a deep low over southern South America, drove this extreme event. This configuration, reinforced by heat wave conditions over central and southeastern Brazil, favoured a sustained, atmospheric river-like moisture transport from the Amazon region, channelled by a strengthened South American Low-Level Jet east of the Andes, configuring a compound event. We also found that the highest moisture contribution occurred predominantly 1–3 days before the precipitation over RS. The analogue-based analysis revealed that while the mean sea level pressure (MSLP) pattern was uncommon but not unique within the 1991–2020 reference period, the RS extreme event exhibited significantly enhanced moisture transport and uptake from the Amazon region compared to similar past occurrences. Additionally, we found lower predictability and persistence of the MSLP pattern during this event than that of its analogues. Overall, this research underscores the critical role of specific large-scale atmospheric patterns and sustained anomalous moisture supply in driving extreme precipitation, reinforcing the need for an improved understanding of climate-weather interactions and the development of mitigation strategies to address intensifying extreme precipitation events in a changing climate.

本研究考察了2024年4月至5月在巴西南里约热内卢Grande do （RS）造成前所未有洪水的极端降水事件，该事件导致阿雷格里港（Porto Alegre）发生破纪录的洪水。利用来自欧洲中期天气预报中心ERA5的数据、高分辨率多源加权集合降水数据集、拉格朗日水分跟踪方法和基于模拟的分析，我们确定了这一事件的大气驱动因素和水分来源。结果表明，由南大西洋上空的高压和南美洲南部上空的深低压组成的持续的准平稳偶极子结构驱动了这次极端事件。巴西中部和东南部的热浪条件加强了这种结构，有利于亚马孙地区持续的、大气中河流般的水汽输送，由安第斯山脉以东加强的南美低空急流引导，形成一个复合事件。基于模拟的分析表明，虽然1991-2020年的平均海平面压力（MSLP）模式不常见，但并非唯一，但与过去类似事件相比，RS极端事件显著增强了亚马逊地区的水分输送和吸收。此外，我们发现MSLP模式在这次事件中的可预测性和持久性较低。总的来说，这项研究强调了特定的大尺度大气模式和持续的异常湿度供应在驱动极端降水中的关键作用，加强了对气候-天气相互作用的理解和制定缓解战略的必要性，以应对气候变化中日益加剧的极端降水事件。

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引用次数: 0

A novel approach for assimilating GNSS tropospheric gradient information to improve numerical weather prediction 同化GNSS对流层梯度信息以改进数值天气预报的新方法

IF 4.4 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-12 DOI: 10.1016/j.atmosres.2026.108769

Yuxin Zheng , Cuixian Lu , Jiafeng Li , Jan Dousa , Xiaohong Zhang

Global Navigation Satellite System (GNSS) has emerged as a well-established atmospheric observing system, with Zenith Total Delay (ZTD) and integrated water vapor routinely assimilated by several global and regional Numerical Weather Prediction (NWP) centers. While co-derived tropospheric gradients effectively capture water vapor horizontal anisotropy, their assimilation has yet to be widely adopted. Here, we introduce a novel approach for assimilating GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model by constructing pseudo-ZTD observations from GNSS-derived ZTD and gradient data. Through two comparative experiments, we evaluate the potential influence of GNSS tropospheric gradients on WRF forecasts. The results indicate that assimilating these gradients improves humidity and wind field predictions in the lower-to-middle troposphere (850–500 hPa), with a neutral impact on surface fields. Verification against radar estimates further demonstrates enhanced precipitation forecast skills, particularly for heavy precipitation events, by better resolving the spatial distribution and intensity of precipitation systems. A diagnosis of a precipitation event suggests that the assimilation of GNSS tropospheric gradients is able to adjust the forecast mid-level moisture distribution and modulate the forecast upward motion, thereby influencing the formation of spurious precipitation.

全球导航卫星系统（GNSS）是一种成熟的大气观测系统，其天顶总延迟（ZTD）和综合水汽通常被几个全球和区域数值天气预报（NWP）中心吸收。虽然共同导出的对流层梯度有效地捕获了水汽的水平各向异性，但它们的同化尚未被广泛采用。本文提出了一种新的方法，利用GNSS导出的ZTD和梯度数据构建伪ZTD观测值，将GNSS对流层梯度同化到天气研究与预报（WRF）模型中。通过两个对比实验，我们评估了GNSS对流层梯度对WRF预报的潜在影响。结果表明，同化这些梯度可以改善对流层中下层（850 ~ 500 hPa）的湿度和风场预测，对地面场的影响为中性。通过更好地解析降水系统的空间分布和强度，对雷达估计的验证进一步证明了降水预报技能的提高，特别是对于强降水事件。对一次降水事件的诊断表明，GNSS对流层梯度的同化能够调整预报的中层水汽分布并调节预报的上升运动，从而影响假降水的形成。

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引用次数: 0

Spatiotemporal Variability of China's 800 mm Precipitation Isohyet (1961–2022): Multi-scale Analysis of its Migration and Impact on Hydroclimatic Extremes 第1节中国800mm降水等雨量（1961-2022）的时空变异：多尺度迁移及其对水文气候极端事件的影响

IF 4.4 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-12 DOI: 10.1016/j.atmosres.2026.108770

Shaowei Ning , Le Chen , Rujian Long , Yuliang Zhou , Yi Cui , Min Zhang , Lichang Xu , Juliang Jin , Thapa bthapa

Under global climate change, precipitation variability and extreme events pose significant challenges to regional ecological security and water resource management. This study proposes the Dynamic Line Scanning Method (DLSM) to quantitatively assess the migration of China's 800 mm precipitation isohyet during 1961–2022. Using long-term precipitation data, we systematically examined its spatial and temporal variation, explored links with drought–flood regimes and extreme precipitation, and identified the main drivers. Results indicate that the isohyet experienced a two-phase shift: an initial southward retreat followed by accelerated northward movement after 2001, with the latter trend markedly intensifying. These shifts have directly influenced regional drought–flood patterns and altered the frequency and intensity of extreme precipitation events. Analysis further reveals that the East Asian Summer Monsoon Index (EASMI) is the dominant factor driving the isohyet's movement. Overall, this study provides novel methodological insights and robust empirical evidence regarding the dynamics of precipitation isohyets in China in the context of climate change. The findings enhance understanding of hydroclimatic variability and offer a scientific foundation for developing region-specific adaptation and water management strategies.

在全球气候变化背景下，降水变率和极端事件对区域生态安全和水资源管理提出了重大挑战。本文提出了动态线扫描法（DLSM）定量评价1961-2022年中国800mm降水等雨量线的迁移。利用长期降水数据，系统分析了其时空变化，探讨了其与旱涝和极端降水的联系，并确定了主要驱动因素。结果表明，该等雨量线经历了两个阶段的变化，即2001年以后先向南撤退，然后加速向北移动，后一阶段的趋势明显加剧。这些变化直接影响了区域旱涝格局，并改变了极端降水事件的频率和强度。进一步分析表明，东亚夏季风指数（EASMI）是驱动等雨量线运动的主导因素。总体而言，本研究为气候变化背景下中国降水等线的动态提供了新的方法见解和有力的经验证据。这些发现增强了对水文气候变率的认识，并为制定区域适应和水管理战略提供了科学基础。

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引用次数: 0

A machine learning-based backward extension of IMERG daily precipitation over the Greater Alpine Region 基于机器学习的大阿尔卑斯地区IMERG日降水的反向扩展

IF 4.4 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-12 DOI: 10.1016/j.atmosres.2026.108763

Iman Goudarzi , Davide Fazzini , Claudia Pasquero , Agostino Niyonkuru Meroni , Matteo Borgnino

Accurate knowledge of precipitation at high spatio-temporal resolution is essential for climate studies and hydrological applications, particularly in mountainous regions where traditional models often underperform due to coarse resolution and sparse observational networks. In this study, we present a machine learning-based approach to enhance ERA5 reanalysis precipitation estimates using the satellite-derived IMERG (Integrated Multi-satellite Retrievals for GPM) product as a reference. We focus on the Greater Alpine Region (GAR), using extreme gradient boosting combined with Shapley additive explanations to identify the most influential ERA5 variables. This method enables the creation of a new daily rainfall dataset, ML-IMEX-GAR (Machine Learning IMERG backward-EXtended precipitation dataset over GAR), at IMERG’s spatial resolution for the historical period 1960–2000.

Compared to ERA5, ML-IMEX-GAR reduces the spatiotemporal RMSD against IMERG by approximately 14%, and achieves strong agreement with in-situ observational monthly data, with an

R^{2}

of 0.87. These findings demonstrate the potential of machine learning to correct reanalysis biases, improve historical precipitation reconstructions, and support climate change research in data-scarce, complex terrains.

高时空分辨率降水的准确知识对于气候研究和水文应用至关重要，特别是在山区，传统模式往往由于分辨率粗和观测网络稀疏而表现不佳。在这项研究中，我们提出了一种基于机器学习的方法，以卫星衍生的IMERG （Integrated Multi-satellite Retrievals for GPM）产品为参考，增强ERA5再分析降水估计。我们将重点放在大高寒地区（GAR），使用极端梯度增强结合Shapley加性解释来确定最具影响力的ERA5变量。这种方法可以创建一个新的日降雨量数据集，ML-IMEX-GAR（机器学习IMERG在GAR上的向后扩展降水数据集），以IMERG的空间分辨率为1960-2000年的历史时期。与ERA5相比，ML-IMEX-GAR将相对于IMERG的时空RMSD降低了约14%，与逐月现场观测数据具有较强的一致性，R2为0.87。这些发现证明了机器学习在纠正再分析偏差、改善历史降水重建和支持数据稀缺、复杂地形的气候变化研究方面的潜力。

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引用次数: 0

High-precision forecasting of 1 km resolution PWV and ZTD over China based on the Pangu-Weather system 基于盘古天气系统的中国1公里分辨率PWV和ZTD高精度预报

IF 4.4 2区地球科学 Q1 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric Research

Pub Date : 2026-01-11 DOI: 10.1016/j.atmosres.2026.108771

Zhihao Wang , Hongzhou Chai , Yuhao Ye , Min Wang , Peng Chen

As key tropospheric parameters, zenith total delay (ZTD) and precipitable water vapor (PWV) serve as core data supports for precise positioning of the Global Navigation Satellite System (GNSS), meteorological monitoring, and disaster early warning. However, existing observation and estimation methods generally suffer from technical bottlenecks, including insufficient spatiotemporal resolution, poor real-time performance, and data blind spots. To address this problem, this study proposes a novel retrieval method based on the Pangu-Weather system, aiming to achieve 7-day high-precision real-time forecasting of PWV and ZTD with 1 km resolution over the China. Using ERA5 reanalysis data as the initial field, the Pangu-Weather system is utilized to forecast meteorological parameters for the next 7 days, and preliminary PWV and ZTD products (Pangu-PWV, Pangu-ZTD) are obtained through integral calculation. Subsequently, based on GNSS data, a multilayer perceptron (MLP) is employed to establish the mapping relationship between latitude, longitude, digital elevation model (DEM), Normalized Difference Vegetation Index (NDVI), time, and tropospheric parameters. The Pangu products are calibrated to the ground height, generating high-precision PWV and ZTD products with 1 km resolution (MLP-PWV, MLP-ZTD). Accuracy verification results indicate that the RMSE between MLP-PWV and GNSS-PWV is 3.63 mm, and the R² is 0.95. For MLP-ZTD and GNSS-ZTD, the RMSE is 26.55 mm and the R² is 0.99, both demonstrating excellent spatiotemporal consistency. Application analysis using typhoon Doksuri in 2023 as a case study shows that MLP-PWV can accurately depict the spatial distribution of PWV during hazardous weather processes, overcoming the limitation that Pangu-PWV is unable to reliably calculate PWV above the Earth's surface. This study provides reliable data support for real-time monitoring of tropospheric parameters with high spatiotemporal resolution.

天顶总延迟（ZTD）和可降水量（PWV）作为对流层的关键参数，是全球卫星导航系统（GNSS）精确定位、气象监测和灾害预警的核心数据支撑。然而，现有的观测和估算方法普遍存在时空分辨率不足、实时性差、数据盲点等技术瓶颈。针对这一问题，本研究提出了一种基于盘古气象系统的新型检索方法，旨在实现中国上空1 km分辨率的PWV和ZTD的7 d高精度实时预报。以ERA5再分析资料为初始场，利用盘古天气系统预报未来7 d的气象参数，通过积分计算得到盘古PWV和ZTD的初步产品（盘古PWV、盘古ZTD）。随后，基于GNSS数据，利用多层感知器（MLP）建立经纬度、数字高程模型（DEM）、归一化植被指数（NDVI）、时间和对流层参数之间的映射关系。盘古产品经地面高度标定，生成1公里分辨率的高精度PWV和ZTD产品（MLP-PWV, MLP-ZTD）。精度验证结果表明，MLP-PWV与GNSS-PWV的RMSE为3.63 mm， R2为0.95。MLP-ZTD和GNSS-ZTD的均方根误差为26.55 mm， R2为0.99，均具有较好的时空一致性。以2023年台风“Doksuri”为例进行的应用分析表明，MLP-PWV能够准确描述危险天气过程中PWV的空间分布，克服了Pangu-PWV无法可靠计算地表以上PWV的局限性。该研究为高时空分辨率对流层参数的实时监测提供了可靠的数据支持。

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