Abstract
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.
Highlights
An important step in the quantitative estimation of rain by radar is the determination of a suitable algorithm to efficiently transform radar information into rain characteristics
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
Summary
An important step in the quantitative estimation of rain by radar is the determination of a suitable algorithm to efficiently transform radar information into rain characteristics. As discussed by [12] and [13], for rain rates considered separately as stratiform or convective, there is great variability in the relationships determined by considering only one type of rainfall The reason for this lies in the non-injective nature of the relationship itself, i.e. a single value of radar reflectivity may correspond to different DSD spectra and rain rate values [13] [14]. The fact that they are only interested in the raindrop size distri-
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