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.

Sist, M., Schiavon, G., Del Frate, F. (2021). A new data fusion neural network scheme for rainfall retrieval using passive microwave and visible/infrared satellite data. APPLIED SCIENCES, 11(10) [10.3390/app11104686].

A new data fusion neural network scheme for rainfall retrieval using passive microwave and visible/infrared satellite data

Sist M.;Schiavon G.;Del Frate F.
2021-01-01

Abstract

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.
2021
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore ING-INF/02 - CAMPI ELETTROMAGNETICI
English
Con Impact Factor ISI
Artificial neural networks; Data fusion; Global precipitation measurement mission; Hydrology; Meteorological satellites; MSG; Precipitation; SEVIRI
https://www.mdpi.com/2076-3417/11/10/4686
Sist, M., Schiavon, G., Del Frate, F. (2021). A new data fusion neural network scheme for rainfall retrieval using passive microwave and visible/infrared satellite data. APPLIED SCIENCES, 11(10) [10.3390/app11104686].
Sist, M; Schiavon, G; Del Frate, F
Articolo su rivista
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/313350
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact