Application of Machine Learning in Atmospheric Sciences and Climate Physics
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 (1 June 2019) | Viewed by 12809
Special Issue Editor
Interests: soft-computing and machine learning algorithms; meta-heuristics optimization techniques; energy; climate and environmental applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nowadays, the development of Information Technologies has led to a new era in computation, affecting almost all fields in Science and Engineering. Specifically, Machine Learning techniques have proven to be excellent tools to cope with difficult problems that arise in a huge variety of applications in Atmospheric Science and Climate Physics. These are usually data-driven problems concerning optimization, classification, or prediction, among others. In many cases, these problems are in close connection with alternative applications such as renewable energy resource evaluation, etc. This Special Issue deals with machine learning methods in atmospheric and climate sciences, from a broad range, from the viewpoints of both algorithms and applications. Articles discussing new algorithms with applications in atmospheric or climate problems, or revisited algorithms providing good solutions to difficult problems in atmospheric science and related areas, are welcome. Alternative applications with a close connection to atmospheric science, such as renewable energy resource evaluation (wind, solar), will be also considered if the article highlights the relationship between machine learning and atmospheric science. Articles discussing machine learning algorithms for climate change problems are especially welcome.
Methods and applications:
Machine learning algorithms (among many others):
- Neural networks
- Extreme learning machines
- Support vector machines
- Learning systems
- Bayesian inference approaches
- Reinforcement learning
- Time-series-related methods
- Evolutionary computation-based approaches
- Meta-heuristics
- Fuzzy logic methods
- Deep-learning technologies
- Problems in atmospheric science, climate physics, renewable energy, climate change, etc.
Dr. Sancho Salcedo-Sanz
Guest Editor
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
- machine learning
- algorithms
- atmospheric sciences
- meteorology
- climatology
- renewable energy
- climate change
