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Atmosphere
Special Issues
The Challenge of Weather and Climate Prediction
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The Challenge of Weather and Climate Prediction

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 5486

Special Issue Editors

Dr. Kreso Pandzic

E-Mail Website
Guest Editor
Retired, previously Croatian Meteorological and Hydrological Service, 10000 Zagreb, Croatia
Interests: data assimilation methods for numerical weather prediction; ensemble forecasts; seasonal and climate forecasting; verification of weather and climate forecasting and outlooks
Special Issues, Collections and Topics in MDPI journals
Dr. Tanja Likso

E-Mail Website
Guest Editor
Head of Data Processing and Management Department, Croatian Meteorological and Hydrological Service, Ravnice 48, 10000 Zagreb, Croatia
Interests: boundary-layer meteorology (application of Monin–Obukhov similarity theory for the wind speed estimation in the lower part of the atmospheric surface layer)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Weather and climate prediction (forecasting) are among the greatest challenges faced in theoretical and applied atmospheric science. Both require comprehensive theoretical knowledge and extensive operational systems. The first successful numerical weather forecast for one day in advance was obtained in the 1950s, when computers began to operate and so-called filtered atmospheric models were utilized. During the 1990s, a so-called global primitive equation atmospheric model was employed; this was operational after the application of a specific data assimilation procedure. Very soon after that, the application of coupled atmosphere and ocean models  resulted in the prolongation of the forecasting period up to five or six days in advance. Unfortunately, due to the presence of deterministic chaos in the atmosphere–ocean dynamic system, long-range forecasting is limited. Ensemble forecasting is a useful probabilistic component of the forecasting system as it is able to inform us of the reliability of medium-range weather forecasting. As time goes on, the reliability decreases; thus, after 10 days, medium-range weather forecasts are considered non-reliable. However, if atmosphere–ocean global models are considered as boundary condition problems instead of as initial condition problems, then useful seasonal and even centennial outlooks can be achieved, but with “condensed” products. Determining the limitations of weather and climate forecasting can be achieved by comparing forecasts with real observations, i.e., via forecast verification.  

Dr. Kreso Pandzic
Dr. Tanja Likso
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • data assimilation for numerical weather and climate forecasting
  • application of atmospheric and oceanic models
  • ensemble forecasting
  • weather and climate forecasting verification.

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Published Papers (5 papers)

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19 pages, 484 KiB  
Article
Improving Weather Forecasting in Remote Regions Through Machine Learning
by Kaushlendra Yadav, Saket Malviya and Arvind Kumar Tiwari
Atmosphere 2025, 16(5), 587; https://doi.org/10.3390/atmos16050587 - 14 May 2025
Viewed by 214
Abstract
The accuracy of weather forecasting hinges crucially on the availability of comprehensive historical weather data. In remote regions face the challenge of sparse data collection, impacting the accuracy of meteorological predictions. This study delves into the data scarcity issue and its repercussions on [...] Read more.
The accuracy of weather forecasting hinges crucially on the availability of comprehensive historical weather data. In remote regions face the challenge of sparse data collection, impacting the accuracy of meteorological predictions. This study delves into the data scarcity issue and its repercussions on weather forecasting in these regions. By evaluating the Meteorological Data Supply Portal of the India Meteorological Department and various climatic classifications, this paper gain insights into the present state of weather data accessibility and identify the regions with substantial gaps. This study investigates the extent to which Machine Learning Techniques can compensate for these deficiencies. By leveraging advanced machine learning and deep learning techniques on available data from well-documented regions, this paper propose a framework for generating reliable weather forecasts for remote territories. This paper not only highlights the current landscape of meteorological data availability in remote areas but also examines the potential of ML to democratize weather forecasting, thereby enabling better-preparedness for adverse weather conditions in these underserved regions. The hypothesis of this paper contends that with sufficient training on diverse datasets, ML can provide a significant predictive advantage, serving as a testament to the ingenuity of modern computational methods in confronting real-world challenges. Here, the deep learning model achieves a notable accuracy of 83%, showcasing a substantial improvement over traditional rule-based system. The integration of ML not only enhanced predictive accuracy but also demonstrated a nuanced understanding of complex weather dynamics through data-driven insights. Full article
(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)
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31 pages, 8044 KiB  
Article
High-Resolution Air Temperature Forecasts in Urban Areas: A Meteorological Perspective on Their Added Value
by Sandro M. Oswald, Stefan Schneider, Claudia Hahn, Maja Žuvela-Aloise, Polly Schmederer, Clemens Wastl and Brigitta Hollosi
Atmosphere 2024, 15(12), 1544; https://doi.org/10.3390/atmos15121544 - 23 Dec 2024
Cited by 2 | Viewed by 1336 | Correction
Abstract
Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled [...] Read more.
Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled modeling system, the numerical weather prediction AROME model and the land-surface model SURFace EXternalisée in a stand alone mode (SURFEX-SA), in forecasting air temperatures at high resolutions (2.5km to 100m) across four Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck). The system is updated with the, according to the author’s knowledge, most accurate land use and land cover input to evaluate the added value of incorporating detailed urban environmental representations. The analysis focuses on the years 2019, 2023, and 2024, examining both summer and winter seasons. SURFEX-SA demonstrates improved performance in specific scenarios, particularly during nighttime in rural and suburban areas during the warmer season. By comprehensively analyzing this prediction system with operational and citizen weather stations in a deterministic and probabilistic mode across several time periods and various skill scores, the findings of this study will enable readers to determine whether high-resolution forecasts are necessary in specific use cases. Full article
(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)
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19 pages, 906 KiB  
Article
Forecasting of Local Lightning Using Spatial–Channel-Enhanced Recurrent Convolutional Neural Network
by Wei Zhou, Jinliang Li, Hongjie Wang, Donglai Zhang and Xupeng Wang
Atmosphere 2024, 15(12), 1478; https://doi.org/10.3390/atmos15121478 - 11 Dec 2024
Viewed by 1054
Abstract
Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models [...] Read more.
Lightning is a hazardous weather phenomenon, characterized by sudden occurrences and complex local distributions. It poses significant challenges for accurate forecasting, which is crucial for public safety and economic stability. Deep learning methods are often better than traditional numerical weather prediction (NWP) models at capturing the spatiotemporal predictors of lightning events. However, these methods struggle to integrate predictors from diverse data sources, which leads to lower accuracy and interpretability. To address these challenges, the Multi-Scale Spatial–Channel-Enhanced Recurrent Convolutional Neural Network (SCE-RCNN) is proposed to improve forecasting accuracy and timeliness by utilizing multi-source data and enhanced attention mechanisms. The proposed model incorporates a multi-scale spatial–channel attention module and a cross-scale fusion module, which facilitates the integration of data from diverse sources. The multi-scale spatial–channel attention module utilizes a multi-scale convolutional network to extract spatial features at different spatial scales and employs a spatial–channel attention mechanism to focus on the most relevant regions for lightning prediction. Experimental results show that the SCE-RCNN model achieved a critical success index (CSI) of 0.83, a probability of detection (POD) of 0.991, and a false alarm rate (FAR) reduced to 0.351, outperforming conventional deep learning models across multiple prediction metrics. This research provides reliable lightning forecasts to support real-time decision-making, making significant contributions to aviation safety, outdoor event planning, and disaster risk management. The model’s high accuracy and low false alarm rate highlight its value in both academic research and practical applications. Full article
(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)
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23 pages, 4474 KiB  
Article
Development and Evaluation of a Short-Term Ensemble Forecasting Model on Sea Surface Wind and Waves across the Bohai and Yellow Sea
by Tonghui Zang, Jing Zou, Yunzhou Li, Zhijin Qiu, Bo Wang, Chaoran Cui, Zhiqian Li, Tong Hu and Yanping Guo
Atmosphere 2024, 15(2), 197; https://doi.org/10.3390/atmos15020197 - 4 Feb 2024
Cited by 2 | Viewed by 1561
Abstract
In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) [...] Read more.
In this study, an ensemble forecasting model for in situ wind speed and wave height was developed using the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This model utilized four bias correction algorithms—Model Output Statistics (MOS), Back Propagation Neural Network (BPNN), Long Short-Term Memory (LSTM) neural network, and Convolutional Neural Network (CNN)—to construct ensemble forecasts. The training data were derived from the COAWST simulations of one year and observations from three buoy stations (Laohutan, Zhifudao, and Lianyungang) in the Yellow Sea and Bohai Sea. After the optimization of the bias correction model training, the subsequent evaluations on the ensemble forecasts showed that the in situ forecasting accuracy of wind speed and wave height was significantly improved. Although there were some uncertainties on bias correction performance levels for individual algorithms, the uncertainties were greatly reduced by the ensemble forecasts. Depending on the dynamic weight assignment, the ensemble forecasts presented a stable performance even when the corrected forecasts by three algorithms had an obvious negative bias. Specifically, the ensemble forecasting bias was found with a mean reduction of about 96%~99% and 91%~95% for wind speed and wave height, and a reduction of about 91%~98% and 16%~54% during the period of Typhoon “Muifa”. For the four correction algorithms, the performance of bias correction was not directly related to the algorithm complexity. However, the strategies with more complex algorithms (i.e., CNN) were more conservative, and simple algorithms (i.e., MOS) might have induced unstable performance levels despite their lower bias in some cases. Full article
(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)
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2 pages, 704 KiB  
Correction
Correction: Oswald et al. High-Resolution Air Temperature Forecasts in Urban Areas: A Meteorological Perspective on Their Added Value. Atmosphere 2024, 15, 1544
by Sandro M. Oswald, Stefan Schneider, Claudia Hahn, Maja Žuvela-Aloise, Polly Schmederer, Clemens Wastl and Brigitta Hollosi
Atmosphere 2025, 16(2), 200; https://doi.org/10.3390/atmos16020200 - 10 Feb 2025
Viewed by 305
Abstract
In the original publication [...] Full article
(This article belongs to the Special Issue The Challenge of Weather and Climate Prediction)
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