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Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of...
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Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5956

Special Issue Editors

Dr. Miodrag Rancic

E-Mail Website
Guest Editor
Lynker at NOAA/NCEP/NWS/EMC Environmental Modeling Center, College Park, MD 20740, USA
Interests: numerical methods; modeling; data assimilation; machine learning/artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Ivana Tosic

E-Mail Website
Guest Editor
Institute of Meteorology School of Physics, University of Belgrade, Belgrade, Serbia
Interests: extreme events; precipitation; temperature; climate variability and numerical modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of meteorology is undergoing a transformative phase, driven by revolutionary breakthroughs in the application of artificial intelligence (AI), advancements in numerical modeling techniques, and the burgeoning potential of quantum computing. This Special Issue of Atmosphere aims to provide a comprehensive overview of these cutting-edge methodologies, offering insights into their applications, challenges, and prospects.

Numerical modeling techniques have been the backbone of modern weather prediction. These models simulate atmospheric processes using mathematical and algorithmic expressions of physical law that drive the atmosphere and require significant computational resources. Recent advancements have enhanced the accuracy and efficiency of these models, incorporating finer spatial resolutions, improved boundary conditions, and novel algorithms. This Special Issue will delve into the latest developments, highlighting key improvements and their impact on weather forecasting.

Artificial intelligence (AI) has revolutionized various scientific domains, and meteorology is no exception. Yet still, an open frontier is how and whether, by integrating AI with traditional physics-based models, researchers can create hybrid systems that leverage the strengths of both approaches. AI algorithms can analyze vast amounts of data, identify patterns, and make predictions with unprecedented speed. When combined with the rigorous foundation of physics models, these hybrid techniques may offer a powerful tool for weather prediction and data assimilation even for situations in which they were not trained, such as extreme weather events that arise as a natural outcome of ongoing climate change. This Special Issue will explore the methodologies and case studies demonstrating the benefits of or denying the efficacy of AI-integrated physics models.

Quantum computing represents the next frontier of computational power, promising to solve complex problems that are beyond the reach of classical computers. In meteorology, quantum computing holds the potential to revolutionize data assimilation, improve model accuracy, and significantly reduce computation times. Although still in its nascent stages, the application of quantum computing in meteorology is an exciting area of research. This Special Issue will discuss the current applications of quantum computing in meteorology, and the challenges that need to be addressed for its widespread adoption.

The intersection of advanced numerical modeling techniques, hybridization of AI-integrated physics models, and quantum computing heralds a new era in meteorology. By embracing these innovative approaches, meteorologists can enhance the accuracy of weather predictions, improve data assimilation processes, and tackle previously insurmountable challenges. This Special Issue of Atmosphere aims to foster collaboration, stimulate discussion, and inspire further research in these groundbreaking areas. We invite contributions from researchers, practitioners, and experts to share their insights, findings, and visions for the future of meteorological science.

You may choose our Joint Special Issue in Meteorology.

Dr. Miodrag Rancic
Dr. Ivana Tosic
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

  • numerical modeling
  • data assimilation
  • weather forecasting
  • artificial intelligence (AI)
  • quantum computing

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Published Papers (5 papers)

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Research

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22 pages, 5877 KB  
Article
Optimizing WRF Spectral Nudging to Improve Heatwave Forecasts: A Case Study of the Sichuan Electricity Grid
by Shuanglong Jin, Shun Li, Bo Wang, Hao Shi and Shanhong Gao
Atmosphere 2026, 17(2), 144; https://doi.org/10.3390/atmos17020144 - 28 Jan 2026
Viewed by 639
Abstract
Accurate forecasting of heatwaves is critical for ensuring the safe operation of electricity grids. Focusing on the complex terrain of Sichuan, China, this study investigates the optimization of spectral nudging parameters within the Weather Research and Forecasting (WRF) model to improve predictions of [...] Read more.
Accurate forecasting of heatwaves is critical for ensuring the safe operation of electricity grids. Focusing on the complex terrain of Sichuan, China, this study investigates the optimization of spectral nudging parameters within the Weather Research and Forecasting (WRF) model to improve predictions of heatwave events. To overcome the subjectivity inherent in the traditional selection of the spectral nudging cutoff wavenumber, we propose an objective method based on power-spectrum energy diagnostics of the background field. This method determines an optimal domain-specific cutoff wavenumber. A series of sensitivity experiments were designed for a significant heatwave event that affected the Sichuan electricity grid in August 2019. These experiments evaluated the impact of different spectral nudging configurations, which considered varying domain sizes and forecast lead times, on correcting large-scale circulation drift and enhancing near-surface air temperature forecasts. The results demonstrate the following: (1) For a smaller domain or a longer forecast lead time, spectral nudging effectively compensates for circulation drift induced by weakening lateral boundary constraints, significantly improving the forecast of heatwave intensity and spatial extent, representing a compensatory effect. (2) For a larger domain that already adequately resolves large-scale circulation evolution, spectral nudging can over-constrain the model’s internal dynamical processes, thereby degrading forecast performance, an outcome termed the over-constraint effect. (3) The proposed energy-threshold method provides an objective, physics-based strategy for identifying dominant large-scale waves and optimizing the spectral nudging cutoff wavenumber. This work offers practical insights for the operational application of spectral nudging over complex terrain to advance extreme temperature forecasting. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
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34 pages, 9217 KB  
Article
Collaborative Station Learning for Rainfall Forecasting
by Bagati Sudarsan Patro and Prashant P. Bartakke
Atmosphere 2025, 16(10), 1197; https://doi.org/10.3390/atmos16101197 - 16 Oct 2025
Cited by 1 | Viewed by 1844
Abstract
Cloudbursts and other extreme rainfall events are becoming more frequent and intense, making precise forecasts and disaster preparedness more challenging. Despite advances in meteorological monitoring, current models often lack the precision needed for hyperlocal extreme rainfall forecasts. This study addresses the research gap [...] Read more.
Cloudbursts and other extreme rainfall events are becoming more frequent and intense, making precise forecasts and disaster preparedness more challenging. Despite advances in meteorological monitoring, current models often lack the precision needed for hyperlocal extreme rainfall forecasts. This study addresses the research gap in spatial configuration-aware modeling by proposing a novel framework that combines geometry-based weather station selection with advanced deep learning architectures. The primary goal is to utilize real-time data from well-placed Automatic Weather Stations to enhance the precision and reliability of extreme rainfall predictions. Twelve unique datasets were generated using four different geometric topologies—linear, triangular, quadrilateral, and circular—centered around the target station Chinchwad in Pune, India, a site that has recorded diverse rainfall intensities, including a cloudburst event. Using common performance criteria, six deep learning models were trained and assessed across these topologies. The proposed Bi-GRU model under linear topology achieved the highest predictive accuracy (R2 = 0.9548, RMSE = 2.2120), outperforming other configurations. These findings underscore the significance of geometric topology in rainfall prediction and provide practical guidance for refining AWS network design in data-sparse regions. In contrast, the Transformer model showed poor generalization with high MAPE values. These results highlight the critical role of spatial station configuration and model architecture in improving prediction accuracy. The proposed framework enables real-time, location-specific early warning systems capable of issuing alerts 2 h before extreme rainfall events. Timely and reliable predictions support disaster risk reduction, infrastructure resilience, and community preparedness, which are essential for safeguarding lives and property in vulnerable regions. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
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22 pages, 8353 KB  
Article
Application of Hybrid Data Assimilation Methods for Mesoscale Eddy Simulation and Prediction in the South China Sea
by Yuewen Shan, Wentao Jia, Yan Chen and Meng Shen
Atmosphere 2025, 16(10), 1193; https://doi.org/10.3390/atmos16101193 - 16 Oct 2025
Viewed by 947
Abstract
In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while [...] Read more.
In this study, we compare two novel hybrid data assimilation (DA) methods: Localized Weighted Ensemble Kalman filter (LWEnKF) and Implicit Equal-Weights Variational Particle Smoother (IEWVPS). These methods integrate a particle filter (PF) with traditional DA methods. LWEnKF combines the PF with EnKF, while IEWVPS integrates the PF with the four-dimensional variational (4DVAR) method. These hybrid DA methods not only overcome the limitations of linear or Gaussian assumptions in traditional assimilation methods but also address the issue of filter degeneracy in high-dimensional models encountered by pure PFs. Using the Regional Ocean Model System (ROMS), the effects of different DA methods for mesoscale eddies in the northern South China Sea (SCS) are examined using simulation experiments. The hybrid DA methods outperform the linear deterministic variational and Kalman filter methods: compared to the control experiment (no assimilation), EnKF, LWEnKF, IS4DVar and IEWVPS reduce the sea level anomaly (SLA) root-mean-squared error (RMSE) by 55%, 65%, 65% and 80%, respectively, and reduce the sea surface temperature (SST) RMSE by 77%, 78%, 74% and 82%, respectively. In the short-term assimilation experiment, IEWVPS exhibits superior performance and greater stability compared to 4DVAR, and LWEnKF outperforms EnKF (LWEnKF’s posterior SLA RMSE is 0.03 m, lower than EnKF’s value of 0.04 m). Long-term forecasting experiments (16 days, starting on 20 July 2017) are also conducted for mesoscale eddy prediction. The variational methods (especially IEWVPS) perform better in simulating the flow field characteristics of eddies (maintaining accurate eddy structure for the first 10 days, with an average SLA RMSE of 0.05 m in the studied AE1 eddy region), while the filters are more advantageous in determining the total root-mean-squared error (RMSE), as well as the temperature under the sea surface. Overall, compared to EnKF and 4DVAR, the hybrid DA methods better predict mesoscale eddies across both short- and long-term timescales. Although the computational costs of hybrid DA are higher, they are still acceptable: specifically, IEWVPS takes approximately 907 s for a single assimilation cycle, whereas LWEnKF only takes 24 s, and its assimilation accuracy in the later stage can approach that of IEWVPS. Given the computational demands arising from increased model resolution, these hybrid DA methods have great potential for future applications. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
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15 pages, 1849 KB  
Article
Determining Wind Shear Threshold by Using Historical Sounding Data in Experimental Area
by Tingting Shu, Qinglin Zhu, Xiang Dong, Houcai Chen, Leke Lin and Xuan Liu
Atmosphere 2025, 16(9), 1064; https://doi.org/10.3390/atmos16091064 - 10 Sep 2025
Viewed by 1048
Abstract
This paper conducts a technical study on a method for determining the occurrence threshold of wind shear based on historical sounding data. After analyzing the impact of low-altitude wind shear on aircraft flight safety, a method for determining the occurrence threshold of wind [...] Read more.
This paper conducts a technical study on a method for determining the occurrence threshold of wind shear based on historical sounding data. After analyzing the impact of low-altitude wind shear on aircraft flight safety, a method for determining the occurrence threshold of wind shear based on historical sounding data is proposed. A statistical analysis of the sounding data from the test area over a period of 15 years from 2010 to 2024 has been conducted, which includes the occurrence events and probability statistics of 1000 m wind shear for all 12 months of the year. The simulation results validate the feasibility and effectiveness of the method for determining the occurrence threshold of wind shear based on historical sounding data in the test area, forming a method that can be extended to all altitude ranges of aircraft flight and all flight regions globally. This statistical method provides a technical foundation for the efficient detection of wind shear at local airports and enhances flight safety at these airports. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
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Review

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29 pages, 2576 KB  
Review
A Semi-Supervised SVM-Firefly Hybrid for Rainfall Estimation from MSG Data
by Ouiza Boukendour, Mourad Lazri, Rafik Absi, Fethi Ouallouche, Karim Labadi, Youcef Attaf, Amar Belghit and Soltane Ameur
Atmosphere 2026, 17(2), 133; https://doi.org/10.3390/atmos17020133 - 26 Jan 2026
Viewed by 720
Abstract
In this paper, two improvements in precipitation classification have been performed. Supervised machine learning has demonstrated considerable performances in classification tasks. However, supervised machine learning can only be applied to labeled data. In some cases, large amounts of unlabeled data contain valuable information [...] Read more.
In this paper, two improvements in precipitation classification have been performed. Supervised machine learning has demonstrated considerable performances in classification tasks. However, supervised machine learning can only be applied to labeled data. In some cases, large amounts of unlabeled data contain valuable information for better classification. In the classification of precipitation intensities from satellite images, unlabeled data constitute the majority and remain largely unexplored. To exploit both labeled and unlabeled data, a Semi-Supervised Support Vector Machine (S3VM) is implemented as the first improvement for classification results. The labeling of the limited available data is derived from radar measurements covering a small portion of the Meteosat Second Generation Satellite observations. The results show that the S3VM model outperforms the standard SVM model, with up to a 15% improvement in classification accuracy compared to the standard SVM. To achieve the second improvement, the S3VM was combined with the Firefly Algorithm (FFA) to optimize its hyperparameters. This hybridization (S3VM-FFA) enabled an even more robust performance. A comparative study showed that the S3VM-FFA approach yielded highly satisfactory results, achieving a 17% improvement in classification compared to the SVM results. Based on these classifications, precipitation quantities at different scales are estimated. Similarly to the classification results, statistical evaluation parameters indicate that the S3VM-FFA outperforms both the standard SVM and the conventional S3VM. Full article
(This article belongs to the Special Issue Advanced Numerical Modeling Techniques in Meteorology: Exploring the Frontier of Weather Prediction and Data Assimilation)
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