Synergy of Remote Sensing Data for Exploring the Complexity of the Clouds
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".
Deadline for manuscript submissions: closed (18 July 2023) | Viewed by 681
Special Issue Editors
Interests: cloud remote sensing; cloud radiative forcing; cloud detection and classification; cloud microphysical properties; surface solar irradiance
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing data analysis; data science; data mining; data fusion; algorithms; cloud remote sensing; geospatial model development; machine learning; meteorology; atmospheric physics and dynamics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Observations of clouds are essential for understanding the global hydrological cycle, the Earth’s radiation budgets, and monitoring extreme weather events. Remote sensing technologies provide in-depth insights into the formation and development of clouds thanks to the development of a wide variety of observing instruments, such as infrared/visible and microwave sensors, radars, lidars, etc. These different instruments, carried by multiple platforms, e.g., vehicles, satellites, and ships, bring about an unprecedented opportunity to observe clouds with a synergy of observations. A large variety of algorithms have been proposed and developed for synergetic retrievals, such as remote sensing and in situ, active, and passive remote sensing, radar and lidar, and infrared and microwave, to understand the complexity of clouds.
This Special Issue will focus on recent advances in synergetic remote sensing of clouds, including algorithm development, comparison, and evaluation of cloud products. The topics include but are not limited to research on cloud physics, nowcasting, impact weather extreme events, etc.
Dr. Filomena Romano
Dr. Salvatore Larosa
Guest Editors
Manuscript Submission Information
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Keywords
- clouds
- synergetic remote sensing
- radars
- lidars
- spectrometers
- microwave radiometers
- interferometer instruments
- infrared radiometers
- microphysical parameter clouds
- cloud classification
- cloud detection
- artificial intelligence
