A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data
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 ca...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/10/4686 |
| _version_ | 1827691982940536832 |
|---|---|
| author | Massimiliano Sist Giovanni Schiavon Fabio Del Frate |
| author_facet | Massimiliano Sist Giovanni Schiavon Fabio Del Frate |
| author_sort | Massimiliano Sist |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:14:05Z |
| format | Article |
| id | doaj.art-4d9ebcc7e6d04821aae58f28084a6fc2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T11:14:05Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4d9ebcc7e6d04821aae58f28084a6fc22023-11-21T20:34:05ZengMDPI AGApplied Sciences2076-34172021-05-011110468610.3390/app11104686A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite DataMassimiliano Sist0Giovanni Schiavon1Fabio Del Frate2Department of Civil Engineering and Computer Science Engineering, University of Rome “Tor Vergata”, Via del Politecnico, 00133 Rome, ItalyDepartment of Civil Engineering and Computer Science Engineering, University of Rome “Tor Vergata”, Via del Politecnico, 00133 Rome, ItalyDepartment of Civil Engineering and Computer Science Engineering, University of Rome “Tor Vergata”, Via del Politecnico, 00133 Rome, ItalyA 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.https://www.mdpi.com/2076-3417/11/10/4686precipitationhydrologymeteorological satellitesglobal precipitation measurement missionMSGSEVIRI |
| spellingShingle | Massimiliano Sist Giovanni Schiavon Fabio Del Frate A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data Applied Sciences precipitation hydrology meteorological satellites global precipitation measurement mission MSG SEVIRI |
| title | A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data |
| title_full | A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data |
| title_fullStr | A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data |
| title_full_unstemmed | A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data |
| title_short | A New Data Fusion Neural Network Scheme for Rainfall Retrieval Using Passive Microwave and Visible/Infrared Satellite Data |
| title_sort | new data fusion neural network scheme for rainfall retrieval using passive microwave and visible infrared satellite data |
| topic | precipitation hydrology meteorological satellites global precipitation measurement mission MSG SEVIRI |
| url | https://www.mdpi.com/2076-3417/11/10/4686 |
| work_keys_str_mv | AT massimilianosist anewdatafusionneuralnetworkschemeforrainfallretrievalusingpassivemicrowaveandvisibleinfraredsatellitedata AT giovannischiavon anewdatafusionneuralnetworkschemeforrainfallretrievalusingpassivemicrowaveandvisibleinfraredsatellitedata AT fabiodelfrate anewdatafusionneuralnetworkschemeforrainfallretrievalusingpassivemicrowaveandvisibleinfraredsatellitedata AT massimilianosist newdatafusionneuralnetworkschemeforrainfallretrievalusingpassivemicrowaveandvisibleinfraredsatellitedata AT giovannischiavon newdatafusionneuralnetworkschemeforrainfallretrievalusingpassivemicrowaveandvisibleinfraredsatellitedata AT fabiodelfrate newdatafusionneuralnetworkschemeforrainfallretrievalusingpassivemicrowaveandvisibleinfraredsatellitedata |