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Atmosphere
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High-Performance Computing for Atmospheric Modeling
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High-Performance Computing for Atmospheric Modeling

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

Deadline for manuscript submissions: 10 January 2025 | Viewed by 5622

Special Issue Editors

Dr. Lars Hoffmann

E-Mail Website
Guest Editor
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Interests: atmospheric science; computational science; Earth system modeling; high performance computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Heng

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Interests: inverse problems; high-performance computing; AI for science; optimal experimental design

Special Issue Information

Dear Colleagues,

The primary objectives of atmospheric modeling are to improve our understanding, prediction, and assessment of atmospheric phenomena, ranging from short-term weather to long-term climate changes, air quality, atmospheric composition, and the interactions between the atmosphere and other components of the Earth system.

High-performance computing (HPC) empowers atmospheric modeling by enabling higher resolutions, complex configurations, ensemble simulations, data assimilation, parameter space exploration, and faster model development. It enhances the accuracy, realism, and scientific understanding of atmospheric processes, thereby improving weather prediction, climate projections, air quality assessments, and our overall knowledge of the Earth's atmosphere.

Developing and maintaining the complex software of atmospheric models for current and future HPC systems is challenging. Collaborations between atmospheric scientists and computational experts are crucial for successfully utilizing HPC in atmospheric modeling.

We invite scientists to contribute original research and review articles on future directions for HPC for atmospheric modeling. Topics of interest include, but are not limited to, the following:

  • Computational complexity and efficient HPC implementation of numerical algorithms used to simulate the behavior of the atmosphere;
  • Scalability of highly parallel codes, requiring careful load balancing, minimization of communication overhead, and optimization of data transfer between computing units;
  • Code optimization to exploit the full potential of HPC systems, including specialized hardware features such as vectorization, multi-core processors, and accelerators such as GPUs or FPGAs;
  • Studies on software complexity, considering that atmospheric models are large, complex software systems with many interacting components;
  • Efficient data transfer and storage techniques for terabytes to petabytes of data, including meteorological observations and simulation results.

Dr. Lars Hoffmann
Prof. Dr. Yi Heng
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

  • atmospheric modeling
  • numerical weather prediction
  • climate modeling
  • high-performance computing
  • software complexity
  • numerical algorithms
  • performance optimization
  • parallel scalability
  • data management
  • new hardware architectures

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

Published Papers (4 papers)

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Research

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16 pages, 927 KiB  
Article
PT-ESM: A Parameter-Testing and Integration Framework for Earth System Models Oriented towards High-Performance Computing
by Jiaxu Guo, Liang Hu, Gaochao Xu, Juncheng Hu and Xilong Che
Atmosphere 2024, 15(8), 935; https://doi.org/10.3390/atmos15080935 - 5 Aug 2024
Viewed by 393
Abstract
High-performance computing (HPC) plays a crucial role in scientific computing, and the efficient utilization of HPC to accomplish computational tasks remains a focal point of research. This study addresses the issue of parameter tuning for Earth system models by proposing a comprehensive solution [...] Read more.
High-performance computing (HPC) plays a crucial role in scientific computing, and the efficient utilization of HPC to accomplish computational tasks remains a focal point of research. This study addresses the issue of parameter tuning for Earth system models by proposing a comprehensive solution based on the concept of scientific workflows. This solution encompasses detailed methods from sensitivity analysis to parameter tuning and incorporates various approaches to enhance result accuracy. We validated the reliability of our methods using five cases in the Single Column Atmosphere Model (SCAM). Specifically, we investigated the influence of fluctuations of 11 typical parameters on 10 output variables. The experimental results show that the magnitude of the impact on the results varies significantly when different parameters are perturbed. These findings will help researchers develop more reasonable parameterization schemes for different regions and seasons. Full article
(This article belongs to the Special Issue High-Performance Computing for Atmospheric Modeling)
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24 pages, 21073 KiB  
Article
Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications
by Mohammad Barooni, Shiva Ghaderpour Taleghani, Masoumeh Bahrami, Parviz Sedigh and Deniz Velioglu Sogut
Atmosphere 2024, 15(6), 640; https://doi.org/10.3390/atmos15060640 - 26 May 2024
Viewed by 1034
Abstract
The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean [...] Read more.
The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting. Full article
(This article belongs to the Special Issue High-Performance Computing for Atmospheric Modeling)
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13 pages, 11891 KiB  
Article
Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021
by Ye Liao, Xuying Deng, Mingming Huang, Mingzhao Liu, Jia Yi and Lars Hoffmann
Atmosphere 2024, 15(4), 429; https://doi.org/10.3390/atmos15040429 - 29 Mar 2024
Viewed by 949
Abstract
The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment [...] Read more.
The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible. Full article
(This article belongs to the Special Issue High-Performance Computing for Atmospheric Modeling)
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Review

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19 pages, 1580 KiB  
Review
Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development
by Yuting Wu and Wei Xue
Atmosphere 2024, 15(6), 689; https://doi.org/10.3390/atmos15060689 - 6 Jun 2024
Cited by 1 | Viewed by 2318
Abstract
Accurate and rapid weather forecasting and climate modeling are universal goals in human development. While Numerical Weather Prediction (NWP) remains the gold standard, it faces challenges like inherent atmospheric uncertainties and computational costs, especially in the post-Moore era. With the advent of deep [...] Read more.
Accurate and rapid weather forecasting and climate modeling are universal goals in human development. While Numerical Weather Prediction (NWP) remains the gold standard, it faces challenges like inherent atmospheric uncertainties and computational costs, especially in the post-Moore era. With the advent of deep learning, the field has been revolutionized through data-driven models. This paper reviews the key models and significant developments in data-driven weather forecasting and climate modeling. It provides an overview of these models, covering aspects such as dataset selection, model design, training process, computational acceleration, and prediction effectiveness. Data-driven models trained on reanalysis data can provide effective forecasts with an accuracy (ACC) greater than 0.6 for up to 15 days at a spatial resolution of 0.25°. These models outperform or match the most advanced NWP methods for 90% of variables, reducing forecast generation time from hours to seconds. Data-driven climate models can reliably simulate climate patterns for decades to 100 years, offering a magnitude of computational savings and competitive performance. Despite their advantages, data-driven methods have limitations, including poor interpretability, challenges in evaluating model uncertainty, and conservative predictions in extreme cases. Future research should focus on larger models, integrating more physical constraints, and enhancing evaluation methods. Full article
(This article belongs to the Special Issue High-Performance Computing for Atmospheric Modeling)
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