High-Resolution Classification of Convective and Stratiform Precipitation Using DSD Parameters

Understanding and accurately classifying precipitation types is essential for hydrological and climate-related applications. In our study, we employ advanced disdrometer measurements to derive detailed drop size distribution (DSD) parameters, which in turn facilitate a robust separation between convective and stratiform rainfall. The proposed methodology relies on both traditional and novel statistical approaches to analyze high-resolution disdrometer data. By deriving key DSD parameters and establishing an empirically optimized separation line, we aim to improve radar-based precipitation classification and quantitative precipitation estimation.

Data for this study were collected in the Czech Republic, representing a mid-latitude, temperate climate. Unlike tropical regions, where convective precipitation is often characterized by more intense and variable DSD features, precipitation in Central Europe exhibits distinct properties. These differences are critical since the evolution and microphysical processes governing precipitation vary with geographical and climatic conditions. By focusing on region-specific data, our work provides refined DSD parameter estimates tailored for moderate latitudes, ultimately enhancing the performance of radar algorithms in these environments.

Preliminary results indicate that spatial composite radar products generated over the Czech territory can effectively validate the DSD-based classification. The computed DSD parameters and the derived separation line clearly delineate convective from stratiform rainfall, with notable improvements in precipitation estimates when region-specific characteristics are accounted for. Furthermore, the integrated analysis of high-resolution disdrometer and radar data demonstrates the potential to significantly reduce uncertainties in radar-based quantitative precipitation estimation.

Our findings contribute to the growing body of literature that emphasizes the importance of adapting radar retrieval algorithms to the specific precipitation regimes of different climatic zones. This study underscores the necessity of incorporating local microphysical variability into precipitation monitoring systems, thereby providing more accurate inputs for hydrological models and climate studies.