Region-specific Z-R relationships from disdrometer observations for improved radar precipitation nowcasting over Kolkata, India: convective–stratiform characterization and QPE validation

Accurate quantitative precipitation estimation (QPE) from weather radar is foundational to precipitation nowcasting, flash flood warnings, and disaster management in tropical urban regions. This study derives region-specific radar reflectivity–rain rate (Z-R) relationships for the Dumdum area of Kolkata, India, using four years (March 2022 – March 2026) of continuous drop size distribution (DSD) measurements from a Joss-Waldvogel DISTROMET RD-80 impact disdrometer. From 73,788 quality-controlled one-minute rain samples, the overall Z-R relationship is Z = 244R ^1.36 (R² = 0.909). Precipitation classified into convective and stratiform types using mass-weighted mean diameter (D_m), rain rate, and temporal variability criteria yields distinct microphysical regimes: convective rainfall (14.5% of samples, D_m = 2.02 mm) follows Z = 798R ^1.01 , exhibiting a near-unity exponent characteristic of DSD equilibrium, while stratiform rainfall (75.4%, D_m = 1.08 mm) follows Z = 216R ^1.27 , consistent with narrow DSDs dominated by small drops. QPE validation reveals that the widely used Marshall-Palmer relation (Z = 200R ^1.60 ) systematically underestimates heavy rainfall (R > 10 mm h⁻¹) by 20–31% — a critical deficiency for flash flood nowcasting in this flood-prone metropolitan region. The Dumdum Z-R corrects this bias, producing conservative overestimates (+ 13–18%) that represent the safer error direction for disaster warning systems. At light rain rates, the Dumdum Z-R reduces relative bias from 46% (Marshall-Palmer) to 16%. The seasonal and type-specific Z-R parameterizations, validated through a case study of a heavy rain event, provide an operationally ready framework for improving real-time radar QPE at the Kolkata DWR. These results demonstrate that region-specific, DSD-informed Z-R calibration is essential for reliable precipitation nowcasting in tropical India, with direct implications for urban flood early warning systems supporting climate adaptation (SDG 13).