Special Issue "Satellite Earth Observation for Atmospheric Modeling"
Deadline for manuscript submissions: closed (30 April 2021).
Interests: GNSS positioning; GNSS-based tropospheric analysis
Interests: numerical weather prediction models; data assimilation; lightning forecast
Special Issues and Collections in MDPI journals
Interests: cloud physics; atmospheric electricity; satellite precipitation measurements; lightning meteorology; transient luminous events; satellite meteorology; climate change impacts
Atmospheric modeling relies on several parameters affecting atmospheric processes, including air/land/sea temperature, radiation, pressure, wind, water vapor, and precipitation. In recent years, the possibility to remotely sense such parameters has widened due both to the usage of dedicated satellite platforms (e.g., Sentinels, GPM, CloudSat, MetOp, Meteosat, GOES, Himawari), and to the exploitation of signals from platforms originally designed for other purposes (e.g., GNSS, InSAR).
Remote sensing by GNSS is carried out by analyzing signals from GNSS receivers either on the ground or on low Earth orbit platforms (i.e., GNSS radio occultation). Examples of promising research topics in this field are the monitoring of local-scale water vapor variations associated with deep convection, water vapor monitoring over the ocean, and atmosphere tomography.
Satellite-based interferometric synthetic aperture radar (InSAR) has been growing steadily as a technique to detect surface deformation signals with unprecedented spatial resolution. However, it is also possible to estimate the delay undergone by satellite-borne SAR signals due to their passage through the atmosphere, providing high-resolution maps of the delay.
Improving initial conditions of numerical weather prediction models is a crucial point for a good forecast. Initial conditions can be improved through data assimilation of observations at different scales. Atmospheric modeling benefits from the availability of satellite observations on different components of the Earth system through data assimilation. Data assimilation is continuously developing and improving to consider new observations and new methods to assimilate observations.
This Special Issue invites contributions on:
- Remote sensing of parameters of interest for atmospheric modeling, including those retrieved from the satellite platform mentioned above, as well as from GNSS and SAR;
- Data assimilation systems using satellite Earth observations of different components of the Earth system (land, soil, vegetation, water, atmosphere, cryosphere), including progress in the development of data assimilation systems for operational applications and research on advanced methods for data assimilation on various scales;
- Numerical weather prediction at different scales using data assimilation of satellite observations with different methods (nudging, variational methods, ensemble Kalman filters, etc.);
- Simulating and forecasting high impact weather events using data assimilation of satellite observations.
Submissions addressing the impact of data assimilation of satellite observations on numerical weather prediction and simulation of atmospheric processes are encouraged.
Dr. Eugenio Realini
Dr. Stefano Federico
Dr. Stefano Dietrich
Manuscript Submission Information
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- numerical weather models
- data assimilation