Naik et al. (2026) Investigating raindrop size distribution and intensity patterns using disdrometer observations over Pune, India

Identification

• Journal: Journal of Earth System Science
• Year: 2026
• Date: 2026-01-03
• Authors: Mrunal Naik, Nayan Talmale, Sonali Shete, Snehal Ghadge, SHWETA MUKIM, S. M. Sonbawne, Pallath Pradeep Kumar, Rohini Bhawar
• DOI: 10.1007/s12040-025-02707-x

Research Groups

• Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, India
• Indian Institute of Tropical Meteorology, Pashan, Pune, India

Short Summary

This study investigates the variability of raindrop size distribution (DSD) and intensity patterns over Pune, India, using disdrometer observations from 2018 to 2022. It reveals that smaller raindrops consistently dominate, with significant annual and monthly variations, and demonstrates that reduced aerosol loading during the COVID-19 pandemic influenced DSD, leading to a higher concentration of larger drops at moderate to high intensities in 2020.

Objective

• To examine the variability in raindrop size distribution (DSD) and intensity patterns of rainfall over Pune, India, during the monsoon months (June to September) from 2018 to 2022.
• To assess the influence of rainfall intensity, seasonal changes, and aerosol loading (specifically during the COVID-19 lockdown) on DSD characteristics and the Z-R relationship.

Study Configuration

• Spatial Scale: Pune, India (18.55421° N, 73.82252° E), specifically at the Department of Atmospheric and Space Sciences (DASS) at Savitribai Phule Pune University (SPPU) campus.
• Temporal Scale: Monsoon seasons (June-September) from 2018 to 2022, with data reported at 10-second intervals.

Methodology and Data

• Models used: Empirical Z-R relationship ($Z = aR^b$) for radar reflectivity (Z) and rainfall rate (R), with coefficients 'a' and 'b' derived through regression analysis.
• Data sources:
  • OTT Parsivel laser-optical disdrometer observations (installed at DASS, SPPU).
  • India Meteorological Department (IMD) high-resolution (0.25° × 0.25°) daily gridded rainfall data (for validation).
  • Moderate Resolution Imaging Spectroradiometer (MODIS) data (referenced for aerosol loading context).

Main Results

• Annual rainfall totals varied significantly, with 2019 recording the highest rainfall at 998.44 mm.
• Smaller raindrops (diameter < 1 mm) consistently exhibited the highest number concentration across all monsoon seasons (2018–2022), with concentration decreasing sharply as diameter increased.
• June and September showed larger droplet sizes during high-intensity rainfall events (>50 mm/h), while July and August maintained a more consistent droplet diameter range of 0–5 mm with a peak concentration of smaller droplets (<2 mm).
• Derived Z-R relationship coefficients 'a' ranged from 66.06 to 317.55, and 'b' from 1.2 to 1.67, indicating consistent precipitation patterns with variations linked to specific weather events.
• The monthly variation of 'b' (higher in September, moderate in June) suggests a seasonal shift from convective to stratiform precipitation regimes.
• During the COVID-19 lockdown year (2020), a comparison with 2019 revealed a slightly higher concentration of larger droplets at moderate to high intensities (20–50 mm/h), attributed to reduced aerosol loading and fewer cloud condensation nuclei (CCN).
• Lower intensity rainfall (5 mm/h) in both 2019 and 2020 exhibited higher droplet concentrations, which decreased as intensity increased.
• Years 2018, 2019, and 2022 showed a notable increase in the frequency of events in the highest intensity category (>40 mm/h) compared to 2020 and 2021.

Contributions

• Provides a comprehensive, high-resolution disdrometer-based analysis of monsoonal DSD variability over Pune, addressing a gap in regional literature.
• Offers unique insights into the impact of reduced anthropogenic aerosol loading during the COVID-19 lockdown on cloud microphysics and precipitation characteristics, highlighting the complex interplay between air quality and rainfall.
• Contributes to refining region-specific radar-based rainfall estimation algorithms and improving weather forecasting accuracy and hydrological modeling.
• Emphasizes the critical role of DSD variability in water resource management and flood prediction, particularly in the context of extreme weather events.

Funding

• DST-PURSE Program (for OTT Parsivel Disdrometer acquisition).
• ISRO-SPPU Respond project funded by ISRO-SPPU-STC.

Citation

@article{Naik2026Investigating,
  author = {Naik, Mrunal and Talmale, Nayan and Shete, Sonali and Ghadge, Snehal and MUKIM, SHWETA and Sonbawne, S. M. and Kumar, Pallath Pradeep and Bhawar, Rohini},
  title = {Investigating raindrop size distribution and intensity patterns using disdrometer observations over Pune, India},
  journal = {Journal of Earth System Science},
  year = {2026},
  doi = {10.1007/s12040-025-02707-x},
  url = {https://doi.org/10.1007/s12040-025-02707-x}
}