Special Issue "Wind Energy Resource Numerical Simulation - Challenges and Outlooks"
Deadline for manuscript submissions: 30 May 2021.
Interests: Numerical weather prediction; Atmospheric modelling; Renewable energies; Climate simulation and modelling; Climate variability and change; Data assimilation; Atmospheric motion vectors; Observation System Simulation Experiments (OSSEs)
Special Issues and Collections in MDPI journals
Special Issue in Atmosphere: Modeling of Surface-Atmosphere Interactions
Interests: Wind energy consultancy, computational fluid dynamics, software development, R&D management, mesoscale modelling
Interests: coastal oceanography; coastal engineering; computational fluid dynamics; climate change; offshore wind energy resource simulation; climate change impacts in offshore wind energy resource
Climate change impacts and adaptation for virtually all human activities are perhaps the greatest challenge humankind as ever faced. To mitigate climate change impacts on the environment, population, economy, and society in general, the replacement of fossil fuels by renewable energy sources is vital for the urgent reduction of greenhouse gas emissions. Wind energy is currently one of the most technologically mature renewable energy sources and still with a high growth potential, particularly offshore. Numerical simulation of wind energy is a practical and cost-effective tool to reliably estimate and map wind energy resources, which can be applied to virtually any area of interest.
This Special Issue aims to present new contributions on numerical simulation of wind energy resource, both offshore and onshore, with a special focus on current challenges and future outlooks in this active research area. We encourage submissions that address wind energy resource assessment under the following topics:
- Numerical weather prediction (NWP) models, exploring existing or novel atmospheric physics options and innovative atmospheric modeling techniques and algorithms;
- Make use of atmospheric products such as re-analyses, new or existing satellite-derived data and other observational/modeled blended products;
- Novel data assimilation techniques that improve the simulation of the wind energy resource;
- Statistical algorithms and models such as neural networks and machine learning;
- Challenges on the current wind energy resource simulation techniques;
- Future outlooks of the wind energy resource worldwide under climate change scenarios.Dr. David Carvalho
Prof. Dr. Carlos Santos
Prof. Dr. Moncho Gómez Gesteira
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 papers will be 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. Processes 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 2000 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.
- Wind energy resource assessment
- numerical weather prediction models
- atmospheric modelling
- renewable energies
- climate change
- neural networks
- machine learning
- data assimilation