Special Issue "Satellite Earth Observation for Atmospheric Modeling"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 2344

Special Issue Editors

Geomatics Research & Development s.r.l., GNSS R&D, Lomazzo, Italy
Interests: GNSS positioning; GNSS-based tropospheric analysis
National Research Council of Italy, Institute of Atmospheric Sciences and Climate (CNR-ISAC), Via del Fosso del Cavaliere 100, Rome, Italy
Interests: numerical weather prediction; data assimilation; lightning forecast; precipitation
Special Issues, Collections and Topics in MDPI journals
Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Area di Ricerca di Roma Tor Vergata, Via Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: satellite meteorology; atmospheric electricity; cloud microphysics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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
Guest Editors

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  • atmosphere
  • satellite
  • numerical weather models
  • data assimilation
  • GNSS
  • SAR

Published Papers (1 paper)

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18 pages, 2359 KiB  
A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data
Appl. Sci. 2021, 11(10), 4686; https://doi.org/10.3390/app11104686 - 20 May 2021
Cited by 2 | Viewed by 1704
A new data fusion technique based on Artificial Neural Networks (ANN) for the design of a rainfall retrieval algorithm is presented. The use of both VIS/IR (VISible and InfraRed) data from GEO (Geostationary Earth Orbit) satellite and of passive microwave data from LEO [...] Read more.
A new data fusion technique based on Artificial Neural Networks (ANN) for the design of a rainfall retrieval algorithm is presented. The use of both VIS/IR (VISible and InfraRed) data from GEO (Geostationary Earth Orbit) satellite and of passive microwave data from LEO (Low Earth Orbit) satellite can take advantage of both types of sensors reducing their limitations. The technique can reconstruct the surface rain field with the MSG-SEVIRI (Meteosat Second Generation–Spinning Enhanced Visible Infrared Imager) spatial and temporal resolution, which means 3 km at the sub satellite point and 5 km at mid-latitudes, every 15 min, respectively. Rainfall estimations are also compared with H-SAF (Hydrology Satellite Application Facility) PR-OBS3A operational product showing better performance both on the identification of rainy areas and on the retrieval of the amount of precipitation. In particular, in the considered test cases, results report an improvement in average of 83% in terms of probability of rainy areas detection, of 45% in terms of false alarm rate, and of 47% in terms of root mean square error in the retrieval of the amount of precipitation. Full article
(This article belongs to the Special Issue Satellite Earth Observation for Atmospheric Modeling)
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