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Atmosphere
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Challenges in Weather and Climate Modelling: Model Development, Validation, and...
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Challenges in Weather and Climate Modelling: Model Development, Validation, and Perspectives

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 3579

Special Issue Editors

Dr. Benjamin Lantei Lamptey

E-Mail Website
Guest Editor
School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK
Interests: modeling; climate change; environmental services (includes weather, water, and climate services); applications of meteorology
Dr. Lai Yung Ruby Leung

E-Mail Website
Guest Editor
Atmospheric Sci & Global Change, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Interests: regional and global climate modeling; land-atmosphere interactions; regional hydrologic cycle; orographic precipitation; climate extremes; climate variability and change
Dr. Jason Hickey

E-Mail Website
Guest Editor
Google Inc., Mountain View, CA 94043, USA
Interests: artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Challenges in Weather and Climate Modelling: Model Development, Validation, and Perspectives" aims to investigate the characteristics (e.g., numerical schemes, effective resolution, physics parameterizations) of Numerical Weather Prediction (NWP) and climate modeling (CM) systems, as well as the evaluation and metrics for these modeling systems. Additionally, this Special Issue intends to explore the potential role of machine learning (ML) and artificial intelligence (AI) methods in complementing or addressing some limitations of the conventional models and the impact of the characteristics of the NWP and CM modeling systems on the impact models (e. g. hydrology, agriculture, energy, etc.) driven by these models.

Relevant topics include:

  1. Characteristics (e. g. numerical schemes, effective resolution, etc.) of the modeling system that make them suitable for weather versus climate simulations.
  2. Implications of model biases in capturing the variability at diurnal, seasonal, interannual timescales.
  3. Connecting model characteristics to model skill in simulating extreme events.Evaluation metrics for NWP and Climate Modeling systems from both model development and model output perspectives.
  4. Implications of the characteristics of weather and climate modeling systems on impact models that use the meteorological output as input.
  5. Challenges related to convection permitting modeling and ensemble modeling.
  6. Seamless prediction across the weather and climate continuum.
  7. Prospects (including strengths and weaknesses) of AI and ML approaches in generating weather and climate information particularly at the community or sub-national scale.

Overall, this Special Issue welcomes submissions about the challenges and perspectives in Numerical Weather Prediction and climate modeling systems and the provision of weather and climate information for climate resilience.

Dr. Benjamin Lantei Lamptey
Dr. Lai Yung Ruby Leung
Dr. Jason Hickey
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

  • weather and climate modeling systems
  • model development
  • model verification
  • machine learning
  • artificial intelligence
  • impact models
  • climate resilience
  • convection permitting models
  • seamless prediction
  • weather and climate continuum
  • ensembles

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

29 pages, 14964 KiB  
Article
Evaluation of Eight Decomposition-Hybrid Models for Short-Term Daily Reference Evapotranspiration Prediction
by Yunfei Chen, Zuyu Liu, Ting Long, Xiuhua Liu, Yaowei Gao and Sibo Wang
Atmosphere 2025, 16(5), 535; https://doi.org/10.3390/atmos16050535 (registering DOI) - 30 Apr 2025
Abstract
Accurate reference evapotranspiration (ETo) prediction is important for water resource management, particularly in arid regions where water availability is highly variable. However, the nonlinear and non-stationary characteristics of ETo time series pose challenges for conventional prediction models. Given this, in [...] Read more.
Accurate reference evapotranspiration (ETo) prediction is important for water resource management, particularly in arid regions where water availability is highly variable. However, the nonlinear and non-stationary characteristics of ETo time series pose challenges for conventional prediction models. Given this, in this study we evaluate eight decomposition-hybrid models that integrate various decomposition techniques with a long short-term memory (LSTM) network to enhance short-term (5-day, 7-day, and 10-day) ETo forecasting. Using a 40-year dataset from a meteorological station, we employ the Penman-Monteith equation to calculate ETo and systematically compare model performance. Results show that VMD-LSTM and EWT-LSTM achieve the highest accuracy in the testing set (R² = 0.983 and 0.992, respectively) but exhibit reduced robustness in the prediction phase due to excessive high-frequency components. In contrast, EMD-LSTM and ESMD-LSTM demonstrate superior predictive stability, with no significant differences from actual values (p > 0.05). These findings underscore the importance of selecting appropriate decomposition methods to balance high-frequency information and predictive accuracy, offering insights for improving ETo forecasting in arid regions. Full article
(This article belongs to the Special Issue Challenges in Weather and Climate Modelling: Model Development, Validation, and Perspectives)
23 pages, 3531 KiB  
Article
Performance Evaluation of Weather@home2 Simulations over West African Region
by Kamoru Abiodun Lawal, Oluwatosin Motunrayo Akintomide, Eniola Olaniyan, Andrew Bowery, Sarah N. Sparrow, Michael F. Wehner and Dáithí A. Stone
Atmosphere 2025, 16(4), 392; https://doi.org/10.3390/atmos16040392 - 28 Mar 2025
Viewed by 1101
Abstract
Weather and climate forecasting, using climate models, have become essential tools and life-savers in the West African region; in spite of the fact that climate models do not fully comply with attributes of forecast qualities—RASAP: reliability, association, skill, accuracy, and precision. The objective [...] Read more.
Weather and climate forecasting, using climate models, have become essential tools and life-savers in the West African region; in spite of the fact that climate models do not fully comply with attributes of forecast qualities—RASAP: reliability, association, skill, accuracy, and precision. The objective of this paper is to quantitatively evaluate, in comparison to CRU and ERA5 datasets, the RASAP compliance-level of the weather@home2 modeling system (w@h2). Findings from some statistical evaluations show that, to a moderately significant extent, w@h2 model provides useful information during the monsoon seasons; skills to capture the Little Dry Season over the Guinea zone; predictive skills for the onset season; ability to reproduce all the annual characteristics of the surface maximum air temperature over the region; as well as skill to detect heat waves that usually ravage West Africa during the boreal spring. The model displays traces of attributes that are needed for seasonal climate predictions and applications. Deficiencies in the quantitative reproducibility point to the facts that the model does provide a reliability akin to that of regional climate models. This paper further furnishes a prospective user with information on whether the model might be “useful or not” for a particular application. Full article
(This article belongs to the Special Issue Challenges in Weather and Climate Modelling: Model Development, Validation, and Perspectives)
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24 pages, 16815 KiB  
Article
Impact of Weather Types on Weather Research and Forecasting Model Skill for Temperature and Precipitation Forecasting in Northwest Greece
by Dimitrios C. Chaskos, Christos J. Lolis, Vassiliki Kotroni, Nikolaos Hatzianastassiou and Aristides Bartzokas
Atmosphere 2024, 15(12), 1516; https://doi.org/10.3390/atmos15121516 - 18 Dec 2024
Viewed by 599
Abstract
The accuracy of the Weather Research and Forecasting (WRF) model’s predictions for air temperature and precipitation in northwestern Greece varies under different weather conditions. However, there is a lack of understanding regarding how well the model performs for specific Weather Types (WTs), especially [...] Read more.
The accuracy of the Weather Research and Forecasting (WRF) model’s predictions for air temperature and precipitation in northwestern Greece varies under different weather conditions. However, there is a lack of understanding regarding how well the model performs for specific Weather Types (WTs), especially in regions with a complex topography like NW Greece. This study evaluates the WRF model’s ability to predict 2 m air temperature and precipitation for 10 objectively defined WTs. Forecasts are validated against observations from the station network of the National Observatory of Athens, focusing on biases and skill variation across WTs. The results indicate that anticyclonic WTs lead to a significant overestimation of early morning air temperatures, especially for inland stations. The precipitation forecast skill varies depending on the threshold and characteristics of each WT, showing optimal results for WTs where precipitation is associated with a combination of depression activity, and orographic effects. These findings indicate the need for adjustments based on WT in operational forecasting systems for regions with similar topographical complexities. Full article
(This article belongs to the Special Issue Challenges in Weather and Climate Modelling: Model Development, Validation, and Perspectives)
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14 pages, 3024 KiB  
Article
Monthly Precipitation Outlooks for Mexico Using El Niño Southern Oscillation Indices Approach
by Miguel Angel González-González and Arturo Corrales-Suastegui
Atmosphere 2024, 15(8), 981; https://doi.org/10.3390/atmos15080981 - 16 Aug 2024
Viewed by 1247
Abstract
The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO [...] Read more.
The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model’s performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (<0.40) are observed in more extensive areas during summer and fall, indicative of rainy seasons, due to increased variability in precipitation. The choice of averaging method influences the degree of underestimations and overestimations, impacting the model’s variability. Spearman correlations highlight regions with significant model performance, revealing potential misinterpretations of high hit rates during winter and spring. Notably, during the fall, the model demonstrates spatial skill across most of Mexico, while in the spring, it performs well in the southern and northeastern regions and, in the summer, in the northwestern areas. Integration of accurate forecasts of ENSO Indices to predict precipitation months ahead is crucial for the operational efficacy of this model, given its heavy reliance on anticipating ENSO behavior. Overall, the empirical method exhibits great promise and potential for application in other developing countries directly impacted by the El Niño phenomenon, owing to its low resource costs. Full article
(This article belongs to the Special Issue Challenges in Weather and Climate Modelling: Model Development, Validation, and Perspectives)
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