Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation
In one sentence. Deep network for polarimetric radar rainfall estimation paired with SHAP analysis showing how each radar variable drives rain-rate estimates across intensities.
Abstract
Abstract Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities.
Labels
- Topic
- Observations & remote sensing, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- Radar, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1029/2023gl107898 OpenAlex:W4399356710
- Added
- 2026-10-22
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