Techniques for Improving the Exploitation of Satellite Data in Atmospheric Science
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 (31 December 2022) | Viewed by 1620
Special Issue Editors
Interests: application of machine learning techniques; calibration of satellite data at microwave wavelengths; intercalibration of satellite data using Global Positioning System (GPS) Radio Occultation (RO) data; simulation of microwave instrument using Community Radiative Transfer Model (CRTM), Monochromatic Radiative Transfer Model (MonoRTM) and Line-by-Line Radiative Transfer Model (LBLRTM); development of quality control techniques for improving uses of satellite data in numerical weather prediction models; direct assimilation of microwave radiances in Hurricane Weather Research and Forecasting (HWRF) system; impact of direct assimilation of microwave radiances on hurricane forecast; study of cloudy radiances through advanced radiative transfer models
Special Issues, Collections and Topics in MDPI journals
Interests: satellite remote sensing; satellite data assimilation; inverse theory applied to geoscience fields; weather forecasting; earth system science; small satellites; and the design of radiometer systems for earth observations based on emerging technologies
Interests: hyperspectral remote sensing techniques; inverse methods for the retrieval of atmospheric temperature and constituents; climate feedbacks to greenhouse gas forcing
Special Issue Information
Dear Colleagues,
Since the majority of the Earth’s surface is covered by water (oceans and inland waters) as well as mountains, deserts, and polar ice, meteorological observations are very limited and mostly come from buoys, island stations, ships, in situ sensors equipped on airplanes and aircraft dropsondes. Even for areas where traditional observations are relatively dense, it is difficult to meet the spatial and temporal requirements of mesoscale weather forecasts. In addition, the common ground-based observation system does not measure the important atmospheric composition information, e.g., ozone, dust, methane, sulfur dioxide, and carbon monoxide.
The modern growing constellation of polar and geostationary satellites orbiting the Earth provides continuous spectral observations of the global atmosphere and oceans at various spatial and temporal scales. Satellite data can help improve regional and global numerical weather forecasts via the data assimilation of radiance in numerical weather prediction models. The satellite radiation measurements on the different spectral regions can also be used to derive atmospheric state/compositions at different heights that are essential for the observation of the Earth’s systems at the microscale, mesoscale, synoptic, and global scales.
Improving the usage of satellite observations is highly important, since they contain valuable information to satisfy present and future observational needs and are critical for enabling applications with significant societal, economic and scientific benefits. Meanwhile, the quick development of information technology, e.g., artificial intelligence and machine learning (AI/ML), also provides the opportunity to handle the large size of the satellite data efficiently and leads to a growth of scientific usage of the satellite data observations.
The aim of this Special Issue is to investigate the techniques for improving the uses of the satellite observations across the different portions of the electromagnetic spectrum (microwave, infrared, visible, etc.) onboard both operational and experimental environmental satellites, including but not limited to the satellites from international agencies, e.g., NOAA, NASA, EUMETSAT, ESA, CMA, JMA, et al., and commercial satellite data.
In particular, but not exclusively, we encourage manuscripts that address the uses of satellite data to observe the atmospheric state and composition, severe weather events, weather and climate studies, and numerical weather forecasts.
Dr. Lin Lin
Dr. Flavio Iturbide-Sanchez
Dr. Antonia Gambacorta
Guest Editors
Manuscript Submission Information
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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
- polar satellite
- geostationary satellite
- atmospheric state and composition
- weather
- climate
- numerical weather forecasts
- severe weather events
- sounding of the atmosphere
- emerging technologies(machine learning/Artificial Intelligence)
