Topic Editors

Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, CE, Italy
Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, CE, Italy

Numerical Models and Weather Extreme Events (2nd Edition)

Abstract submission deadline
27 November 2025
Manuscript submission deadline
27 February 2026
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Topic Information

Dear Colleagues,
The topic of ‘Numerical Models and Weather Extreme Events’ comprises several interdisciplinary research areas that cover the main aspects of numerical weather predictions. Each year, hurricanes, extreme heat waves, tornadoes, and other extreme weather events occur, resulting in thousands of deaths and billions of dollars in damage. The more accurate prediction of extreme weather further in advance could allow targeted regions to better prepare in order to reduce loss of life and property damage. It is evident that the rate of climate change is increasing, as are the intensity and frequency of extreme weather events; thus, the prompt prediction of these events has never been more important. The development of accurate local forecasts is notoriously difficult due to the complex physics driving heavy precipitation and intense winds. Weather forecasting requires supercomputers and trained local practitioners, thus narrowing its accessibility to wealthy governments and communities. Moreover, traditional weather forecasts, with a predictive scope of several days in advance, are very coarse in terms of resolution and, therefore, do not capture local extreme events. One alternative developed in recent years is the use of local observations to forecast weather up to a couple of hours in advance. In this regard, next-generation satellites bring great opportunities to further improve short-term forecasting. Artificial intelligence and machine learning breakthroughs are changing weather forecasting, such that resource-heavy regional weather models might soon be completely replaced by machine learning approaches. These innovative approaches use specific networks (GANs), trained via global weather forecasts, to correct for the biases that exist in current weather models. The new model downscales global forecasts to be as accurate as a local forecast, without requiring the vast amounts of computational, financial, and human resources previously required for such a small scale. In light of this, we welcome the submission of manuscripts addressing these exciting areas of development. Some examples of related subjects include the following:

  • Current challenging areas in weather models;
  • The assessment of a weather model’s ability to represent extreme weather events;
  • Supercomputing applied to weather forecasting;
  • Ensemble modeling;
  • Monte Carlo simulations;
  • Stochastic weather generators;
  • The monitoring of weather and climate from space.

We look forward to receiving your submissions.

Yours faithfully,

Dr. Edoardo Bucchignani
Dr. Andrea Mastellone
Topic Editors

Keywords

  • numerical models
  • extreme weather
  • weather forecasts
  • satellites
  • ensemble modeling

 

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.5 2011 19.8 Days CHF 2400 Submit
Atmosphere
atmosphere
2.3 4.9 2010 16.9 Days CHF 2400 Submit
Climate
climate
3.2 5.7 2013 21.6 Days CHF 1800 Submit
Meteorology
meteorology
- - 2022 44.9 Days CHF 1000 Submit
Geosciences
geosciences
2.1 5.1 2011 23.4 Days CHF 1800 Submit

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Published Papers (1 paper)

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16 pages, 5543 KB  
Article
Trend Analysis of Precipitation in the South American Monsoon System (SAMS) Regions and Identification of Most Intense and Weakest Rainy Seasons
by Sâmia R. Garcia, Maria A. M. Rodrigues, Mary T. Kayano and Alan J. P. Calheiros
Meteorology 2025, 4(4), 26; https://doi.org/10.3390/meteorology4040026 - 25 Sep 2025
Abstract
Extreme precipitation events have become a central focus of the scientific community due to their increased occurrence in recent years. This study aims to analyze the variability and trends in aspects associated with the rainy seasons in the South American Monsoon System (SAMS) [...] Read more.
Extreme precipitation events have become a central focus of the scientific community due to their increased occurrence in recent years. This study aims to analyze the variability and trends in aspects associated with the rainy seasons in the South American Monsoon System (SAMS) area from 1979 to 2022. The dates for the onset and demise of the rainy season (ONR and DER, respectively) were determined using antisymmetric outgoing longwave radiation (OLR) data relative to the equator (AOLR) for the clustered regions defined in a previous work. Based on these dates, the duration of the rainy seasons and the total precipitation for each rainy season were also calculated. The main advantage of this study is the analysis of trends within homogeneous regions derived from cluster analysis, which enables a more reliable assessment of precipitation patterns across the spatially heterogeneous SAMS domain. The non-parametric Mann–Kendall test and Sen’s slope estimator were applied to the ONR, DER, rainy season length, and total precipitation time series for each group over the 1979–2022 period. Quartile analysis was performed on the total precipitation time series to identify the most and least intense rainy seasons in the SAMS’s regions. These analyses revealed a trend of shortening of the SAMS rainy season over the 44 years of analysis, with a positive trend in the ONR dates and a negative trend in the DER dates, which is further confirmed by the decreasing trends in rainy season length and accumulated precipitation in most analyzed regions. The most (above the third quartile) and least (below the first quartile) intense rainy seasons were found to be concentrated at the beginning and end of the study period, respectively, for all monsoon regions. After removing the linear trend, the distribution of events appeared more uniform over time, yet the major droughts that occurred after 2010 remained clear. The results of this study contribute to a better understanding of the precipitation characteristics in the SAMS area, and these findings may assist climate forecasting and monitoring centers in improving regional precipitation assessments. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events (2nd Edition))
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