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Advances in Geosciences An open-access journal for refereed proceedings and special publications
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Volume 2
Adv. Geosci., 2, 267–272, 2005
https://doi.org/10.5194/adgeo-2-267-2005
© Author(s) 2005. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Adv. Geosci., 2, 267–272, 2005
https://doi.org/10.5194/adgeo-2-267-2005
© Author(s) 2005. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  22 Jul 2005

22 Jul 2005

Satellite radiometric remote sensing of rainfall fields: multi-sensor retrieval techniques at geostationary scale

F. S. Marzano2,1, D. Cimini1, E. Coppola1, M. Verdecchia1, V. Levizzani3, F. Tapiador4, and J. F. Turk5 F. S. Marzano et al.
  • 1Centro di Eccellenza CETEMPS, Università dell’Aquila, Via Vetoio – 67010, L’Aquila, Italy
  • 2Dipartimento di Ingegneria Elettronica, Università “La Sapienza" di Roma, Rome, Italy
  • 3Istituo di Scienze dell’Atmosfera e Clima (ISAC), Consiglio Nazionale delle Ricerche, Bologna, Italy
  • 4Instituto de Ciencias Ambientales, Universidad de Castilla-La Mancha (UCLM), Toledo, Spain
  • 5Marine Meteorology Division, Naval Research Laboratory (NRL), Monterey, California, USA

Abstract. The Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated.

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