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Atmosphere
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Meteorological Extreme in China
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Meteorological Extreme in China

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 28 August 2026 | Viewed by 1743

Editor

Prof. Dr. Xiaorui Niu

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Interests: regional climate modeling; climate prediction

Special Issue Information

Dear Colleagues,

China is facing growing threats from meteorological extremes such as heatwaves, floods, droughts, and compound events, with increasingly severe impacts on society, infrastructure, and ecosystems. Under ongoing global warming and rapid urbanization, the frequency, intensity, duration, and spatial extent of many extremes are changing, and new forms of compound and sequential events are emerging, increasing the challenges of prediction, attribution, and impact assessment.

 This Special Issue aims to synthesize recent advances and bring together state-of-the-art studies on observed trends, driving mechanisms, prediction, and future projections of meteorological extremes across China. Contributions are welcome on topics including, but not limited to:

  • Observing and simulating extremes across multiple spatial and temporal scales;
  • Diagnosing physical mechanisms and circulation drivers behind recent major events;
  • The role of land–atmosphere interactions and land-use changes in shaping extremes;
  • Quantifying the influence of anthropogenic forcing versus internal variability using attribution and storyline approaches;
  • Emerging approaches such as machine learning and physics-informed AI for extreme-event prediction;
  • Impacts, risk assessment, and adaptation-relevant applications.

We hope this Special Issue will provide a timely platform for sharing new insights and methodologies, and for fostering interdisciplinary collaboration among meteorology, hydrology, risk management, and socio-economic studies.

Prof. Dr. Xiaorui Niu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • regional climate modeling
  • meteorological extremes
  • climate prediction

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Published Papers (4 papers)

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Research

18 pages, 4420 KB  
Article
Anomalous Ozone Pollution in Xiamen During Spring 2025
by Chen Chen, Guanjie Jiao, Jingyi Fan and Sijia Lou
Atmosphere 2026, 17(7), 628; https://doi.org/10.3390/atmos17070628 (registering DOI) - 24 Jun 2026
Abstract
Ozone (O3) pollution is highly sensitive to meteorological variability and regional transport, particularly in coastal southeastern China. During April–May 2025, Xiamen experienced an atypical, persistent springtime O3 episode substantially exceeding the 2014–2024 baseline. Using surface observations and ERA5 reanalysis data, [...] Read more.
Ozone (O3) pollution is highly sensitive to meteorological variability and regional transport, particularly in coastal southeastern China. During April–May 2025, Xiamen experienced an atypical, persistent springtime O3 episode substantially exceeding the 2014–2024 baseline. Using surface observations and ERA5 reanalysis data, this study investigates the meteorological drivers and formation mechanisms. At Hongwen station, the MDA8 O3 > 160 μg m−3 exceedance frequency reached 11.5% (historical average: 0.1%). This anomaly was closely linked to an anomalous Western Pacific Subtropical High (WPSH) configuration, characterized by northward displacement and accompanying westward extension. Compared to historical high-pollution conditions, surface temperature and downward solar radiation increased by 2.32 °C and 51 W m−2, while wind speed and planetary boundary layer height decreased by 15.3% and 24.2%, favoring O3 production and precursor accumulation. Two distinct pollution periods were identified. Period 1 (29 April–1 May) featured local photochemical enhancement under stagnant conditions; regional mean NO2 increased by 31 μg m−3 before the peak, indicating substantial precursor accumulation. Simultaneously, the mean nighttime O3 concentration at the Huli site during Period 1 was 50.5 μg m−3 (43% lower than that at Hongwen) due to enhanced NO titration from port emissions. Period 2 (12–14 May) involved regional transport, where persistent 850-hPa southwesterly flow facilitated pollutant transport along the coastal corridor, increasing O3 and PM2.5 by 40 μg m−3 and 38 μg m−3. Thus, extreme springtime O3 over southeastern coastal China resulted from anomalous large-scale circulation, regional transport, and local photochemical processes. Full article
(This article belongs to the Special Issue Meteorological Extreme in China)
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19 pages, 20182 KB  
Article
Optimal Initial Error and Targeted Observation Sensitive Area for Predicting the Northeast China Cold Vortex Revealed by a Deep Learning Model
by Chen Zhang, Junkai Qian and Qiang Wang
Atmosphere 2026, 17(6), 567; https://doi.org/10.3390/atmos17060567 - 30 May 2026
Viewed by 379
Abstract
The Northeast China Cold Vortex (NECV) is a key circulation system affecting weather patterns over North China, frequently triggering thunderstorms, hail, and other severe convective weather. Accurate prediction of NECVs is therefore of great importance. However, substantial forecast errors still remain, largely due [...] Read more.
The Northeast China Cold Vortex (NECV) is a key circulation system affecting weather patterns over North China, frequently triggering thunderstorms, hail, and other severe convective weather. Accurate prediction of NECVs is therefore of great importance. However, substantial forecast errors still remain, largely due to uncertainties in the initial conditions. To improve NECV forecast skills, we investigate the optimal initial errors and targeted observation sensitive areas using a sampling-based approximation of the conditional nonlinear optimal perturbation (CNOP) method together with the Pangu-Weather deep learning model. We first evaluate the model’s performance over Northeast China and find that Pangu-Weather exhibits forecast skill generally comparable to the ECMWF Integrated Forecasting System (IFS) during the May–August 2022 period over Northeast China. Then the CNOP-based approach is used to capture the optimal initial errors with the greatest impact on NECV forecasts. The largest error amplitudes are primarily located upstream of the vortex and near upper-level jet-entrance regions, which are identified as the targeted observation sensitive areas. Perturbation kinetic-energy diagnostics further indicate that baroclinic conversion is the dominant mechanism for error growth. Observing system simulation experiments suggest that, under an idealized assumption of completely eliminating errors in a given region, targeted observations over the sensitive area can produce the largest forecast improvement, with an average error reduction of approximately 13% relative to other areas. This study contributes to a deeper understanding of NECV predictability and may help improve forecasting capability. Full article
(This article belongs to the Special Issue Meteorological Extreme in China)
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19 pages, 5741 KB  
Article
Objective Classification of Convective Precipitation in Chengdu Terminal Area Using a Self-Organizing Map and Its Impacts on Terminal Area Operations
by Haotian Li, Haoya Liu, Lian Duan, Ran Li, Yecheng Zhang and Xiaowei Hu
Atmosphere 2026, 17(4), 421; https://doi.org/10.3390/atmos17040421 - 21 Apr 2026
Viewed by 366
Abstract
Based on hourly reanalysis data during 2010–2020, the Self-Organizing Map method is used to objectively classify convective precipitation events in the Chengdu terminal area. Combined with circulation background characteristics, the results are further grouped into three typical synoptic types. Among these three types, [...] Read more.
Based on hourly reanalysis data during 2010–2020, the Self-Organizing Map method is used to objectively classify convective precipitation events in the Chengdu terminal area. Combined with circulation background characteristics, the results are further grouped into three typical synoptic types. Among these three types, Type 1, characterized by a pattern with strong high pressure and abundant water vapor, yields the most intense precipitation. Type 2, a pattern with moderately strong high pressure and water vapor convergence, produces the second-highest precipitation. Type 3, associated with a low trough and weak water vapor conditions, has the weakest precipitation. Two indicators of the Weather Severity Index (WSI) and Node Coverage Index (NCI), respectively describing the coverage extent of heavy precipitation over the terminal area and over key arrival and departure nodes, are established and calculated based on heavy precipitation samples. The results show that Type 1 exhibits the highest WSI and NCI values, indicating the greatest potential impact. Type 2 displays a lower WSI than Type 1 but retains a relatively higher NCI, suggesting a more directionally biased impact, whereas Type 3 records the lowest values for both indicators, indicating a relatively weak impact. The integration of synoptic weather classification and spatial impact indicators offers a reference for weather-impact identification and scenario-based operational assessment in terminal areas. However, some limitations remain in the current study. The weather classification is primarily based on reanalysis data, and the correspondence between the WSI/NCI and actual airport operational constraints requires further validation. Full article
(This article belongs to the Special Issue Meteorological Extreme in China)
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21 pages, 8306 KB  
Article
How Well Do Reanalyses Capture Day-to-Day Temperature Variability?
by Xianchun Chen, Xiaorui Niu, Ping Li, Libin Huang, Jiajia Zhang and Yanjin Mao
Atmosphere 2026, 17(3), 235; https://doi.org/10.3390/atmos17030235 - 25 Feb 2026
Viewed by 633
Abstract
Day-to-day temperature variability (DTD) significantly affects human health and ecosystems, yet its representation in major reanalysis datasets has not been systematically evaluated. This study assesses the ability of four widely used reanalysis datasets, namely ERA-Interim, ERA5, JRA-55, and MERRA-2, against station observations to [...] Read more.
Day-to-day temperature variability (DTD) significantly affects human health and ecosystems, yet its representation in major reanalysis datasets has not been systematically evaluated. This study assesses the ability of four widely used reanalysis datasets, namely ERA-Interim, ERA5, JRA-55, and MERRA-2, against station observations to capture DTD’s spatial and temporal characteristics. All four datasets broadly reproduce the observed spatial pattern of DTD but generally underestimate its magnitude globally, except over eastern China. JRA-55 performs better at low-to-mid latitudes, while other datasets show closer agreement with observations at high latitudes. Regarding long-term trends, the reanalyses generally capture the observed pattern of decreasing DTD at high latitudes and increasing DTD at mid-low latitudes, but they show trends opposite to observations in summer over Eurasia, the low latitudes, and the Southern Hemisphere. Skill is highest in winter and lowest in summer, with ERA5 and ERA-Interim performing the best overall. Using ERA5 for further analysis, it is suggested that the recent weakening in global extreme DTD intensity is offset by an increase in extreme-event frequency, with both exhibiting substantial regional and seasonal variability. These findings advance understanding of short-term temperature variability and provide guidance for risk assessment, early warning, and mitigation. Full article
(This article belongs to the Special Issue Meteorological Extreme in China)
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