Unraveling High-Resolution Temporal–Spatial Variability of Raindrop Size Distributions in Extreme Rainfall Events With Dual-Polarization Radar Optimal Estimates

Catastrophic extreme rainfall events pose significant threats on economics and human life, while such events are poorly predicted by current numerical models, partly due to a poor understanding and parameterization of extreme rainfall storm microphysics. However, fine-resolution raindrop size distribution (DSD) observations are usually not available due to the sparse deployment of disdrometers. To filling in this gap, this study applies an Optimal Estimation (OE) algorithm using the three parameters of the Gamma distribution as state variables to retrieve DSD parameters from S-band dual-polarization radar observations, thereby revealing the high-resolution spatiotemporal evolution of extreme precipitation. Results show that the radar retrievals significantly outperform traditional empirical approaches and demonstrate high consistency with disdrometer observations. The maximum hourly rainfall recorded by rain gauges during Zhengzhou and Qinzhou extreme rainfall events was 201.9 mm and 189.6 mm, respectively, while the corresponding radar-retrieved values were 193.23 mm and 203.7 mm. We show that the Zhengzhou event with is characterized by a combination of high particle number concentration (log<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub>N<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub>≈ 4.8 log<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub>m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup>mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>) and small to medium-sized droplets (D<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>m</i></sub> ≈ 1.8 mm); in contrast, the Qinzhou event exhibits lower concentrations (log<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub>N<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub>≈ 4.5 log<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sub>m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup>mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>) and larger droplet sizes (D<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>m</i></sub> ≈ 2.0mm), suggesting more active warm rain processes. This study offers a robust technical approach for high-resolution characterization of microphysical processes in extreme precipitation events, with potential applications in improving microphysical parameterization schemes in numerical models, enabling the development of tailored forecasting strategies for different types of precipitation, and providing scientific support for early warning systems and disaster risk reduction decision-making.