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Advances in Understanding Extreme Weather Events in the Anthropocene (2nd Edition)

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Dear Colleagues,

This Special Issue is the second volume of "Advances in Understanding Extreme Weather Events in the Anthropocene”, which was first published in Atmosphere in 2025 (https://www.mdpi.com/journal/atmosphere/special_issues/O8GFXP6VPC).

It is well recognised that climate changes are altering the frequency and intensity of extreme weather events worldwide. These events play an important role in Earth systems, having a devastating impact on society and the environment. Overall, understanding the dynamics behind these episodes is crucial for developing strategies to improve disaster preparedness and response, as well as mitigating their impacts on communities and ecosystems. Atmosphere is dedicating this Special Issue to publishing the latest studies in the context of extreme weather events as they relate to climate and weather variability.

The main topics to be presented in this Special Issue include, but are not limited to:

Floods, droughts, cold spells, heatwaves and climate studies;
Severe weather: hailstorms, tornadoes, heavy rainfall and lightning;
Polar lows, medicanes, tropical cyclones and torrential rains;
Extreme wildfires, smoke aerosol emission, transport and impacts on the atmosphere and air quality.
Use of remote sensing and Earth observations (EOs) for studying extreme events;
Modelling and forecasting extreme events;
Impact of extreme weather on society and early warning systems.

We encourage contributions which present innovative research, reviews and case studies examining all aspects of extreme weather events, from observation to numerical modelling results that are useful for understanding these events.

Dr. Flavio T. Couto
Dr. Stergios Kartsios
Dr. Ioannis Pytharoulis
Guest Editors

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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.

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18 pages, 1872 KB

Open AccessArticle

Single-Point Thunderstorm Forecasting Based on Second-Order Moist Potential Vorticity and Deep Learning

by Cha Yang, Xiaoqiang Xiao, Na Li, Daoyong Yang, Xiao Shi, Yue Yuan and Hu Wang

Atmosphere 2026, 17(5), 519; https://doi.org/10.3390/atmos17050519 - 19 May 2026

Viewed by 146

Abstract

Thunderstorms are the most frequent type of severe convective weather, which pose great threats to buildings, power transmission, communication facilities, and air transportation. Their analysis and forecasting have long been challenges in meteorological operations. Currently, deep learning-based lightning forecasting has a short valid period, mostly relying on satellite imagery, radar echoes, and lightning location data, focusing on very-short-range forecasting. The longest valid period does not exceed 6 h, and the forecasting accuracy is not high. Based on the physical quantities of the ECMWF numerical prediction model and the actual observations of single-point thunderstorms, this paper constructs a single-point thunderstorm forecasting model with a long validity period (>6 h). The study integrates multi-dimensional parameters such as thermal, dynamic, water vapor, and stratification instability, introduces the second-order moist potential vorticity S as a comprehensive predictor, systematically compares the forecasting performance of eight models, such as 1D PreRNN and ConvLSTM, and verifies the actual operational capability of the model through independent cases. The results show that the 1D PreRNN model has the best overall performance in all periods, which can effectively capture the temporal evolution characteristics of meteorological physical quantities and still has stable generalization performance under unbalanced samples. The model performs well in the 1st, 2nd, and 4th periods, and especially still has significant operational reference value in the 4th period with the longest forecasting validity period; only the 3rd period is weakly affected by the small number of samples. The effect of second-order moist potential vorticity has significant time-dependent differences. Its overall improvement effect is limited in short-term forecasting, but it can provide key disturbance signals in the 4th period with the longest forecasting validity period, and the model forecasting performance drops significantly after removal. The original binary cross-entropy loss is most suitable for the unbalanced sample scenario in this study, and weighted losses are prone to overcorrection. The method in this paper can achieve stable and reliable single-point thunderstorm forecasting for more than 6 h, and can provide long-term fixed-point meteorological support for key scenarios such as aerospace and new energy stations. Full article

(This article belongs to the Special Issue Advances in Understanding Extreme Weather Events in the Anthropocene (2nd Edition))

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