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arXiv · 2611.08659

The Re-Foundation of Radar Meteorology

Massimiliano Ignaccolo·Carlo De Michele
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Abstract

Since the early development of radar meteorology in the 1940s, rainfall retrieval has traditionally relied on empirical scaling relationships between radar variables and rainfall rate. Here, we revisit the premise that horizontal and differential reflectivity sufficiently constrain the rainfall rate despite the variability of the underlying drop size distribution. Motivated by evidences that this variability requires at least three degrees of freedom, we develop a nonparametric statistical retrieval algorithm that considers different combinations of drop count and distribution shape compatible with the same radar observations. The method separates the contributions of drop count and distribution shape. It then empirically estimates how the shape factors for rainfall rate and horizontal reflectivity, as well as differential reflectivity, vary with the mean and skewness of the drop diameter distribution. These relationships identify compatible combinations of drop count and shape, from which rainfall rates are calculated and their median returned as the estimate. Neither a prescribed analytical drop size distribution nor a scaling rainfall estimator or explicit classification of precipitation regimes is required. We evaluate the method using a worldwide database (149 disdrometer datasets from 76 locations), with radar observables calculated from measured drop spectra. In cross-dataset validation, the method achieves lower median dispersion of relative retrieval errors across all 16 combinations of training and validation instrument types with respect two benchmark methods consisting of the traditional scaling law relationship, and a direct nonparametric fit. These findings motivate a reassessment of the foundations of radar rainfall retrieval by incorporating the range of rainfall rates compatible with radar observation.

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Massimiliano Ignaccolo, Carlo De Michele. 2026-11-05. The Re-Foundation of Radar Meteorology. https://arxiv.org/abs/2611.08659

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