A novel algorithm for remote sensing rainfall retrieval

Dual-polarization radar rainfall rate estimates are based on scaling laws involving the horizontal reflectivity Z_h and the ratio between horizontal and vertical reflectivity Z_DR. Scaling law parameters obtained from disdrometric observations are highly dependent on the data set used. As a consequence ZR scaling laws do not generalize well. Using the jargon of data science, a ZR scaling law has an accpetable training accuracy and a poor validation accuracy. 

To overcome this limitation, we propose the Formula-R algorithm based on the adoption of the data science parametrization of drop size distributions and its universal shape factors [https://doi.org/10.1175/JHM-D-21-0211.1]. We show, using a worldwide catalog of disdrometric observations, how the Formula-R outperforms the ZR scaling law both in training and validation accuracy. 

The Formula-R algorithm could be used as the foundation of a universal remote sensing retrieval algorithm making the question "which ZR-relationship should we use?" a question of the past.