Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.6
2.5
Journals
Atmosphere
Special Issues
Artificial Intelligence in Atmospheric Modelling, Prediction, and Data Assimilation
share announcement

Artificial Intelligence in Atmospheric Modelling, 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: closed (15 October 2021) | Viewed by 49533

Special Issue Editors

Dr. Amin Talei

E-Mail Website
Guest Editor
Discipline of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway 47500, Selangor, Malaysia
Interests: sustainable urban water management; nature-based solutions for stormwater management; biofiltration systems; flood modeling and management
Special Issues, Collections and Topics in MDPI journals
Dr. Miodrag Rancic

E-Mail Website
Guest Editor
IMSG at NOAA/NWS/NCEP 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

Special Issue Information

Dear Colleagues,

To date, several computer-aided techniques have been developed for modeling, prediction, and data assimilation of atmospheric systems. These methods range from numerical models developed based on physical laws of the problem to statistical methods and data-driven techniques which are generally focused on associations between inputs and outputs. These techniques can help researchers in solving problems in atmospheric systems such as dynamic systems prediction, ensemble weather forecasting, climate data downscaling, etc. In the past two decades, through improvements in computing power, artificial intelligence techniques have attracted a lot of attention due to their automatic repetitive learning and recognition through data, unique capability in capturing complex associations between variables, and their power in analyzing big data.

This Special Issue aims to present some of the latest achievements in using AI techniques, including artificial neural networks (ANN), neurofuzzy systems (NFS), support vector machines (SVM), fuzzy reasoning, evolutionary computation techniques, etc. in atmospheric modeling, prediction, and data assimilation.

Dr. Amin Talei
Dr. Miodrag Rancic
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
  • Atmospheric prediction
  • Data assimilation
  • Artificial intelligence

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 1432 KiB  
Article
Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
by Bogdan Bochenek and Zbigniew Ustrnul
Atmosphere 2022, 13(2), 180; https://doi.org/10.3390/atmos13020180 - 23 Jan 2022
Cited by 120 | Viewed by 37824
Abstract
In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in [...] Read more.
In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting. Full article
(This article belongs to the Special Issue Artificial Intelligence in Atmospheric Modelling, Prediction, and Data Assimilation)
►▼ Show Figures

Figure 1

20 pages, 6560 KiB  
Article
A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 Hour Precipitation Nowcasting
by Fuhan Zhang, Xiaodong Wang and Jiping Guan
Atmosphere 2021, 12(12), 1596; https://doi.org/10.3390/atmos12121596 - 29 Nov 2021
Cited by 15 | Viewed by 3373
Abstract
Multi-source meteorological data can reflect the development process of single meteorological elements from different angles. Making full use of multi-source meteorological data is an effective method to improve the performance of weather nowcasting. For precipitation nowcasting, this paper proposes a novel multi-input multi-output [...] Read more.
Multi-source meteorological data can reflect the development process of single meteorological elements from different angles. Making full use of multi-source meteorological data is an effective method to improve the performance of weather nowcasting. For precipitation nowcasting, this paper proposes a novel multi-input multi-output recurrent neural network model based on multimodal fusion and spatiotemporal prediction, named MFSP-Net. It uses precipitation grid data, radar echo data, and reanalysis data as input data and simultaneously realizes 0–4 h precipitation amount nowcasting and precipitation intensity nowcasting. MFSP-Net can perform the spatiotemporal-scale fusion of the three sources of input data while retaining the spatiotemporal information flow of them. The multi-task learning strategy is used to train the network. We conduct experiments on the dataset of Southeast China, and the results show that MFSP-Net comprehensively improves the performance of the nowcasting of precipitation amounts. For precipitation intensity nowcasting, MFSP-Net has obvious advantages in heavy precipitation nowcasting and the middle and late stages of nowcasting. Full article
(This article belongs to the Special Issue Artificial Intelligence in Atmospheric Modelling, Prediction, and Data Assimilation)
►▼ Show Figures

Figure 1

20 pages, 11443 KiB  
Article
Model Sensitivity Evaluation for 3DVAR Data Assimilation Applied on WRF with a Nested Domain Configuration
by Mingchun Lam and Jimmy Chihung Fung
Atmosphere 2021, 12(6), 682; https://doi.org/10.3390/atmos12060682 - 26 May 2021
Cited by 2 | Viewed by 2924
Abstract
An initial condition that closely represents the true atmospheric state can minimize errors that propagate into the future, and could theoretically lead to improvements in the forecast. This study aims to evaluate and understand the impacts of 3DVAR on the state-of-the-art Weather Research [...] Read more.
An initial condition that closely represents the true atmospheric state can minimize errors that propagate into the future, and could theoretically lead to improvements in the forecast. This study aims to evaluate and understand the impacts of 3DVAR on the state-of-the-art Weather Research and Forecasting (WRF) model with a two nested domains setup. The domain configuration of the model covers China with an emphasis on Guangdong province, with a resolution of 27 km, 9 km, and 3 km. Improvements in the forecasts for the Winter and Summer season of all the domains are systematically compared and are quantified in terms of 2 m temperature, 10 m wind speed, sea level pressure, and 2 m relative humidity. The results show that 3DVAR provides significant improvements in the winter case and surprisingly improvements were also found after the 48 h of the forecast. Evaluations of performance of 3DVAR in different domains and between two different seasons were done to further understand the reasons behind the discrepancies. Full article
(This article belongs to the Special Issue Artificial Intelligence in Atmospheric Modelling, Prediction, and Data Assimilation)
►▼ Show Figures

Figure 1

27 pages, 1729 KiB  
Article
Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas
by Fengyang Long, Wusheng Hu, Yanfeng Dong and Jinling Wang
Atmosphere 2021, 12(2), 169; https://doi.org/10.3390/atmos12020169 - 28 Jan 2021
Cited by 16 | Viewed by 2633
Abstract
The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric [...] Read more.
The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error. Full article
(This article belongs to the Special Issue Artificial Intelligence in Atmospheric Modelling, Prediction, and Data Assimilation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop