Hence, the first aim of this present study is to evaluate the accuracy and reliability of this radar for quantitative precipitation estimation. This will be achieved by comparing the radar data with rain gauges data collected during extreme precipitation events that occurred in La Reunion in 2022. This study also highlighted the challenges of using an X-band radar in complex orographic conditions such as, for instance, the blockage of the radar beam inducing a significant altitude difference between the radar sampled area and the rain gauges located in valleys.
Likewise, the attenuation of the X-band radar signal due to heavy precipitations close to the radar is an important parameter to account for in order to effectively exploit the measurements far from the radar. To correct for the attenuation of the radar reflectivity, we used two different approaches: i) the Hitschfeld and Bordan algorithm (1954), which is mainly used for single-polarization radar, and ii) the Philinear algorithm, which relies on the linear relationship between reflectivity and differential phase PHIDP. The results indicate that in heavy rainfall, the Philinear algorithm is more effective than the Hitschfeld and Bordan algorithm. Then, to convert radar observations into rain rates (R), two empirical models were used: a) rain rate-reflectivity relationship R(ZH), and b) rain rate-specific differential phase relationship R(KDP). It has been observed that for stratiform precipitation, R(ZH) provides a good correlation between radar and rain gauges measurement. However, for intense precipitation, R(KDP) was found to be more effective. The results of this study can be applied to Islands located in the Southeast of the Indian Ocean as part of the ongoing campaign.

