Topic Editors

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

Numerical Models and Weather Extreme Events (2nd Edition)

Abstract submission deadline
closed (27 November 2025)
Manuscript submission deadline
closed (27 February 2026)
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3107

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 16 Days CHF 2400
Atmosphere
atmosphere
2.3 4.9 2010 19.7 Days CHF 2400
Climate
climate
3.2 5.7 2013 20.8 Days CHF 1800
Geosciences
geosciences
2.1 5.1 2011 23.6 Days CHF 1800
Meteorology
meteorology
- - 2022 27 Days CHF 1000

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Published Papers (2 papers)

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23 pages, 21995 KB  
Article
The Capabilities of WRF in Simulating Extreme Rainfall over the Mahalapye District of Botswana
by Khumo Cecil Monaka, Kgakgamatso Mphale, Thizwilondi Robert Maisha, Modise Wiston and Galebonwe Ramaphane
Atmosphere 2026, 17(2), 135; https://doi.org/10.3390/atmos17020135 - 27 Jan 2026
Viewed by 473
Abstract
Flooding episodes caused by a heavy rainfall event have become more frequent, especially during the rainfall season in Botswana, which poses some socio-economic and environmental risks. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating a heavy [...] Read more.
Flooding episodes caused by a heavy rainfall event have become more frequent, especially during the rainfall season in Botswana, which poses some socio-economic and environmental risks. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating a heavy rainfall event that occurred on 26 December 2023 in Mahalapye District, Botswana. This event is one among many that have negatively impacted the lives and infrastructures in Botswana. The WRF model was configured using the tropical-suite physics schemes, i.e., (Rapid Radiative Transfer Model, Yonsei University planetary boundary layer scheme, Unified Noah land surface model, New Tiedtke, Weather Research and Forecasting Single-Moment six-class) on a two-way nested domain (9 km and 3 km grid spacing) and was initialized with the GFS dataset. Gauged station data was used for verification alongside synoptic charts generated using ECMWF ERA5 dataset. The results show that the WRF model simulation using the tropical-suite physics schemes is able to reproduce the spatial and temporal patterns of the observed rainfall but with some notable biases. Performance metrics, including RMSE, correlation coefficient, and KGE, showed moderate to good agreement, highlighting the model’s sensitivity to physical parameterization and resolution. The results of this study conclude that the WRF model demonstrates promising potential in forecasting extreme rainfall events in Botswana, but more sensitivity tests to different parameterization schemes are needed in order to integrate the model into the early warning systems to enhance disaster preparedness and response. Full article
(This article belongs to the Topic Numerical Models and Weather Extreme Events (2nd Edition))
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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
Viewed by 1246
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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