Articles | Volume 10, issue 1
https://doi.org/10.5194/nhess-10-149-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/nhess-10-149-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
26 Jan 2010
Time-dependent Z-R relationships for estimating rainfall fields from radar measurements
L. Alfieri
Dipartimento di Idraulica, Trasporti ed Infrastrutture Civili (DITIC), Politecnico di Torino, Torino, Italy
Institute for Environment and Sustainability (IES), Joint Research Centre, EC, Ispra, Italy
P. Claps
Dipartimento di Idraulica, Trasporti ed Infrastrutture Civili (DITIC), Politecnico di Torino, Torino, Italy
F. Laio
Dipartimento di Idraulica, Trasporti ed Infrastrutture Civili (DITIC), Politecnico di Torino, Torino, Italy
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- Bias adjustment and advection interpolation of long-term high resolution radar rainfall series S. Thorndahl et al. https://doi.org/10.1016/j.jhydrol.2013.10.056
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