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Advances in Geosciences An open-access journal for refereed proceedings and special publications
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Volume 7
Adv. Geosci., 7, 97–103, 2006
https://doi.org/10.5194/adgeo-7-97-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Adv. Geosci., 7, 97–103, 2006
https://doi.org/10.5194/adgeo-7-97-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  02 Feb 2006

02 Feb 2006

Artificial neural-network technique for precipitation nowcasting from satellite imagery

G. Rivolta1, F. S. Marzano2,1, E. Coppola1, and M. Verdecchia1 G. Rivolta et al.
  • 1Centro di Eccellenza CETEMPS, Università dell’Aquila, L’Aquila, Italy
  • 2Dipartimento di Ingegneria Elettronica, Universit`a di Roma “La Sapienza", Rome, Italy

Abstract. The term nowcasting reflects the need of timely and accurate predictions of risky situations related to the development of severe meteorological events. In this work the objective is the very short term prediction of the rainfall field from geostationary satellite imagery entirely based on neural network approach. The very short-time prediction (or nowcasting) process consists of two steps: first, the infrared radiance field measured from geostationary satellite (Meteosat 7) is projected ahead in time (30 min or 1 h); secondly, the projected radiances are used to estimate the rainfall field by means of a calibrated microwave-based combined algorithm. The methodology is discussed and its accuracy is quantified by means of error indicators. An application to a satellite observation of a rainfall event over Central Italy is finally shown and evaluated.

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