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Atmosphere
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Highly Resolved Numerical Models in Regional Weather Forecasting
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Highly Resolved Numerical Models in Regional Weather Forecasting

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 1346

Special Issue Editors

Dr. Davide Cinquegrana

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Guest Editor
Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, CE, Italy
Interests: NWP; soil–atmosphere coupling; urban climate; model calibration; machine learning; icing in the aviation environment; multiphysics
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Ricchi

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Guest Editor
Department of Physical and Chemical Sciences, Università degli Studi dell'Aquila/CETEMPS, Via Vetoio, 67100 Coppito, AQ, Italy
Interests: air–sea interactions; extreme atmospheric and marine events; coupled atmosphere–ocean-wave numerical models; meteorology; oceanography
Special Issues, Collections and Topics in MDPI journals
Dr. Edoardo Bucchignani

E-Mail Website
Guest Editor
Meteorology Laboratory, CIRA Italian Aerospace Research Center, 81043 Capua, Italy
Interests: regional climate modeling; climate changes; numerical weather prediction models; extreme events
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Numerical Weather Prediction (NWP) models' accuracies depends significantly on grid resolution. High-resolution models are essential for properly capturing interactions between scales of different sizes. However, demonstrating the consistency property of a complex numerical system such as an NWP remains challenging, and not only because of the high computational power demand; key areas in which further advancements are required include, among others, the tuning of three-dimensional turbulence models such as Large Eddy Simulations (LES), the accurate modeling of exchange energy between soil and atmosphere in urban and rural areas, and complex orography.

This Special Issue invites scientific contributions focused on refining weather simulations, at a sub-kilometer scale, through LES turbulence models, particularly in their ability to capture local convective phenomena in urban environments and complex terrains. Studies that explore land–soil–atmosphere interactions and atmosphere–ocean interaction in this context are of special interest.

Validations of model outputs with observed data are particularly encouraged, especially those that aid in assessing the Planetary Boundary Layer or evapotranspiration processes, such as Eddy Covariance Towers. Additionally, studies that compare high-resolution model outputs against observational networks capable of resolving fine-scale phenomena are highly valued. Contributions demonstrating how these efforts can enhance forecasts of orographic winds, wind gusts, extreme rainfall events, urban temperature, urban heat island effects, and urban wind patterns are especially welcomed.

You may choose our Joint Special Issue in Meteorology.

Dr. Davide Cinquegrana
Dr. Antonio Ricchi
Dr. Edoardo Bucchignani
Guest Editors

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-blind 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

  • NWP
  • turbulence models
  • LES
  • soil–atmosphere interaction
  • evapotranspiration
  • UHI
  • urban climate
  • deep convective systems

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Published Papers (2 papers)

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Research

27 pages, 27225 KB  
Article
Can Hot Water Discharged from Industrial Processes Enhance the Likelihood of Waterspouts?
by Valerio Capecchi, Bernardo Gozzini and Mario Marcello Miglietta
Atmosphere 2026, 17(4), 345; https://doi.org/10.3390/atmos17040345 - 29 Mar 2026
Viewed by 420
Abstract
Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible [...] Read more.
Italy and the surrounding seas are recognised as one of the European hotspots for tornadoes and waterspouts. In recent years, the town of Rosignano Solvay (on the Northern Tyrrhenian coast) experienced repeated waterspouts affecting the same areas, raising local concern about the possible influence of heated wastewater discharged into the sea by a nearby industrial site. We reconstruct the mesoscale meteorological conditions of four intense waterspouts near Rosignano Solvay using a limited-area weather model at a high-to-very-high resolution (inner domain grid spacing of 500 m; sensitivity tests at 100 m). At the reported event times, the intensity of key mesoscale precursors (low-level wind shear, 1 km storm-relative helicity, maximum updraft intensity, and lifting condensation level) is consistent with the values typically associated with EF1 (or stronger) tornadoes and waterspouts. The model systematically predicts the peak of instability indices 2–3 h earlier than the reported event times. For one case study, we conduct two sea surface temperature sensitivity experiments to assess the potential atmospheric impact of heated wastewater discharge (temperature increases of +1.5 K and +5 K over a 10 km2 area). The resulting changes in instability indices are marginal, with differences of at most 3% relative to the control run. A simple mass-balance estimate for the modified sea patch suggests that, given the reported discharge rates, a plausible impact of the warm water released from the industrial site could lead to an increase in the local sea surface temperature of approximately +0.7 °C over two months. We conclude that synoptic and mesoscale conditions primarily govern waterspout initiation in this region, while the direct effect of the small warm coastal plume from the industrial discharge appears to be minor. Full article
(This article belongs to the Special Issue Highly Resolved Numerical Models in Regional Weather Forecasting)
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26 pages, 8387 KB  
Article
Machine Learning as a Lens on NWP ICON Configurations Validation over Southern Italy in Winter 2022–2023—Part I: Empirical Orthogonal Functions
by Davide Cinquegrana and Edoardo Bucchignani
Atmosphere 2026, 17(2), 132; https://doi.org/10.3390/atmos17020132 - 26 Jan 2026
Viewed by 392
Abstract
Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we [...] Read more.
Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II). Full article
(This article belongs to the Special Issue Highly Resolved Numerical Models in Regional Weather Forecasting)
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