This study evaluates the improvement of the radar Quantitative
Precipitation Estimation (QPE) by involving microphysical processes in the
determination of Z-R algorithms. Within the framework of
the AMMA campaign, measurements of an X-band radar (Xport), a vertical pointing
Micro Rain Radar (MRR) to investigate microphysical processes and a dense
network of rain gauges deployed in Northern
Benin (West Africa) in 2006 and 2007 were used as support to establish
such estimators and evaluate their performance compared to other estimators in
the literature. By carefully considering and correcting MRR attenuation and
calibration issues, the Z-R estimator developed with the contribution of microphysical processes
and non-linear least-squares adjustment proves to be more efficient for quantitative rainfall
estimation and produces the best statistic scores than other optimal Z-R algorithms in the literature. We also find that it gives results comparable to
some polarimetric algorithms including microphysical information through DSD
integrated parameter retrievals.
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