Atmospheric and Land Surface Process Modeling
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".
Deadline for manuscript submissions: closed (6 October 2023) | Viewed by 229
Special Issue Editors
2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
3. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: artificial intelligence applied in the atmospheric science; artificial intelligence applied in severe weather predict; artificial intelligence applied in climate change; convective weather; data mining and knowledge discovery; cloud computing; applied meteorology; big data analytics
Special Issues, Collections and Topics in MDPI journals
Interests: data science; machine learning; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; numerical modeling; extended-range forecast; nonlinear dynamics; extreme events; complex network
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) is playing a more and more essential role in atmospheric and land surface process modeling, including predicting climate change, natural disasters, and optimizing agricultural production. AI can analyze data from atmospheric and surface processes through machine learning algorithms to achieve more accurate prediction and optimization. These applications help to better respond to various changes and challenges in nature, such as predicting extreme weather events, earthquakes, floods, and other natural disasters, thereby helping people take preventive and response measures. This Special Issue aims to bring together top academic scientists, researchers, and research scholars to explore the application of AI in atmospheric and land surface process modeling. It also provides an important interdisciplinary platform for researchers, practitioners, and educators to show and discuss the latest innovations, trends, and concerns in the field, as well as the practical challenges and solutions.
Prof. Dr. Wei Fang
Prof. Dr. Victor S. Sheng
Prof. Dr. Qiguang Wang
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
- application of artificial intelligence in atmospheric science
- use artificial intelligence to better predict severe weather
- machine learning and big data analytics in natural disaster warning
- artificial intelligence promotes the development of climate prediction
- machine learning in weather forecasting
- land use and cover change
- optimization of agricultural production and precision agricultural management
- air pollution prediction and control
- observation networks and weather forecasting
- forecasting different types of convective weather
- applications of meteorology
- agricultural meteorology
- weather impact modeling
- uncertainty quantification
