Natural Hazards and Earth System Science
www.nat-hazards-earth-syst-sci.net
1561-8633
1684-9981
10
1
2010
10.5194/nhess-10-149-2010
http://www.nat-hazards-earth-syst-sci.net/10/149/2010/
http://www.nat-hazards-earth-syst-sci.net/10/149/2010/nhess-10-149-2010.html
http://www.nat-hazards-earth-syst-sci.net/10/149/2010/nhess-10-149-2010.pdf
149
158
2010-01-26
Time-dependent <i>Z-R</i> relationships for estimating rainfall fields from radar measurements
L. Alfieri
lorenzo.alfieri@jrc.ec.europa.eu
P. Claps
F. Laio
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
The operational use of weather radars has become a widespread and useful tool
for estimating rainfall fields. The radar-gauge adjustment is a commonly
adopted technique which allows one to reduce bias and dispersion between
radar rainfall estimates and the corresponding ground measurements provided
by rain gauges.
<br><br>
This paper investigates a new methodology for estimating radar-based rainfall
fields by recalibrating at each time step the reflectivity-rainfall rate
(<i>Z-R</i>) relationship on the basis of ground measurements provided by a rain
gauge network. The power-law equation for converting reflectivity
measurements into rainfall rates is readjusted at each time step, by
calibrating its parameters using hourly <i>Z-R</i> pairs collected in the proximity
of the considered time step. Calibration windows with duration between 1 and
24 h are used for estimating the parameters of the <i>Z-R</i> relationship. A case
study pertaining to 19 rainfall events occurred in the north-western Italy is
considered, in an area located within 25 km from the radar site, with
available measurements of rainfall rate at the ground and radar reflectivity
aloft. Results obtained with the proposed method are compared to those of
three other literature methods. Applications are described for a posteriori
evaluation of rainfall fields and for real-time estimation. Results suggest
that the use of a calibration window of 2–5 h yields the best performances,
with improvements that reach the 28% of the standard error obtained by using
the most accurate fixed (climatological) <i>Z-R</i> relationship.
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