Journal Menu► ▼ Journal Menu
Journal Browser► ▼ Journal Browser
Special Issue "Weather Forecasting and Modeling Using Satellite Data"
Deadline for manuscript submissions: 31 March 2020.
Civil and Environmental Engineering, University of Connecticut, USA
Interests: prediction of extreme weather events; uncertainties in atmospheric and air quality modeling systems; anthropogenic activities that alter the atmospheric and aquatic environment
Weather forecasting employs numerical weather prediction, which has evolved through increased computational power, the ingestion of observations from various platforms (in-situ and remote sensing), multi-agency and international collaborations, and advancements in the representation of physics and dynamics in the model structure. We increasingly rely on weather forecasts to protect life, infrastructure, and the environment—especially in the event of imminent extreme atmospheric conditions. Moreover, our capability to observe Earth from space over the last 60 years has catalyzed our understanding of the dynamic processes that govern air, ocean, land, and soil at various spatial and temporal scales. Space-based observations are used not only to understand the planet, but also to make assessments and predictions of major environmental problems such as eutrophication, extreme storms, precipitation, wildfires, ice melt, and sea level rise, among others. Satellite observations combined with numerical weather prediction models and data assimilation techniques have become essential components in the fully coupled Earth system framework (NAS, 2018), and will continue to be in the future. In the event of extreme weather phenomena such as hurricanes, we rely on satellites to track the location and intensity of the storm and inform weather prediction systems in real-time to provide more accurate forecasts for the next 3–7 day outlook.
This Special Issue “Weather Forecasting and Modeling Using Satellite Data” aims to bring together current state-of-the-art research about the use of geostationary and/or polar orbiting satellite data in weather prediction from short-term to sub-seasonal and seasonal scales. Weather prediction can refer to deterministic or probabilistic frameworks with single or multi-model ensembles that utilize satellite data and/or develop new techniques to integrate the two and improve weather forecasts. Research related to the above topics will be considered for publication in Remote Sensing under the Special Issue.Prof. Marina Astitha
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 1800 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.
- numerical weather prediction
- satellite data
- extreme weather events
- data assimilation
- seasonal forecast
- short-term forecast