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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 6, issue 3
Nat. Hazards Earth Syst. Sci., 6, 439–450, 2006
https://doi.org/10.5194/nhess-6-439-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Mediterranean Storms (Plinius 2004)

Nat. Hazards Earth Syst. Sci., 6, 439–450, 2006
https://doi.org/10.5194/nhess-6-439-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  06 Jun 2006

06 Jun 2006

Rainfall rate retrieval in presence of path attenuation using C-band polarimetric weather radars

G. Vulpiani1, F. S. Marzano1,2, V. Chandrasekar3, A. Berne4, and R. Uijlenhoet4 G. Vulpiani et al.
  • 1Center of Excellence CETEMPS, University of L’Aquila, L’Aquila, Italy
  • 2University La Sapienza, Rome, Italy
  • 3Colorado State University, Fort Collins, Colorado
  • 4Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands

Abstract. Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors.

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