Radar-Based Raindrop Size Distribution Prediction: Comparing Analytical, Neural Network, and Decision Tree Approaches

TL;DR

This study compares analytical, neural network, and decision tree methods for estimating raindrop size distribution parameters from polarimetric radar data, highlighting the strengths and limitations of each approach under various data conditions.

Contribution

It provides a comprehensive evaluation of multiple retrieval models, emphasizing the importance of model-data alignment for effective RSD estimation from radar observations.

Findings

Machine learning models generally outperform analytical methods.

Decision trees show robustness with limited radar features.

Model performance varies depending on RSD parameter and data availability.

Abstract

Reliable estimation of the raindrop size distribution (RSD) is important for applications including quantitative precipitation estimation, soil erosion modelling, and wind turbine blade erosion. While in situ instruments such as disdrometers provide detailed RSD measurements, they are spatially limited, motivating the use of polarimetric radar for remote retrieval of rain microphysical properties. This study presents a comparative evaluation of analytical and machine-learning approaches for retrieving RSD parameters from polarimetric radar observables. One-minute OTT Parsivel2 disdrometer measurements collected between September 2020 and May 2022 at Sheepdrove Farm, UK, were quality-controlled using collocated weighing and tipping-bucket rain gauges. Measured RSDs were fitted to a normalised three-parameter gamma distribution, from which a range of polarimetric radar variables were analytically simulated. Analytical retrievals, neural networks, and decision tree models were then trained to estimate the gamma distribution parameters across multiple radar feature sets and model architectures. To assess robustness and equifinality, each model configuration was trained 100 times using random 70/30 train-test splits, yielding approximately 17,000 trained models in total. Machine-learning approaches generally outperform analytical methods; however, no single model class or architecture is uniformly optimal. Model performance depends strongly on both the target RSD parameter and the available radar observables, with decision trees showing particular robustness in reduced-feature regimes. These results highlight the importance of aligning retrieval model structure with operational data constraints rather than adopting a single universal approach.