Abstract

Highlights: What are the main findings? A Random Forest-based model using dual-polarimetric radar features achieves >99% accuracy in classifying precipitation and non-precipitation echoes. Multi-scale spatial variability features enhance discrimination between genuine precipitation and spurious echoes. Fusion of GOES-16 infrared satellite data with NEXRAD radar effectively removes non-precipitation echoes with precipitation-like signatures, including wind turbine clutter. A CAPPI scan strategy improves near-radar precipitation detection by recovering valid echoes misclassified at the lowest elevation due to side-lobe interference and limited sampling volume. What are the implications of the main findings? The model's robustness to noise and overfitting, combined with minimal hyperparameter tuning, supports operational scalability and facilitates straightforward adaptation to other radar systems (e.g., C- and X-band) with minimal retraining. Multi-scale texture analysis ensures consistent quality control across varied precipitation regimes and spatial patterns. Satellite-radar integration improves rainfall estimation by reducing false detections from non-meteorological sources. CAPPI-based enhancement strengthens short-range rainfall monitoring in operational settings. In this paper, the authors developed a data-driven model to classify radar measurements into precipitation (P) and non-precipitation (NP) echoes using the Random Forest machine learning algorithm. Dual-polarimetric radar variables and their local variability exhibit distinctive characteristics between P and NP echoes. The authors found that using larger search window sizes generally improves classification accuracy, though it involves a trade-off: while it helps eliminate small clusters of NP echoes, it may also suppress weak precipitation signals near storm edges. Incorporating multiscale local variability estimates computed with varying window sizes further enhances classification performance by capturing spatial-scale-dependent features characteristic of P and NP echoes. The main model uses radar variables obtained from a single scan and demonstrates consistent performance across all distances from the radar. This consistency allows reliable use of the model out to 230 km—the maximum range at which dual-polarimetric variables are used for rainfall estimation from NEXRAD radars—without significant degradation in accuracy due to range effects. Supplementing the model with independent information from GOES-16 infrared channel products further improves classification by helping to eliminate localized NP echoes remaining after the main model, particularly those caused by wind turbines that mimic precipitation in dual-polarimetric signatures. This is based on the tendency of water vapor and/or raindrops to absorb terrestrial radiation, thereby lowering brightness temperatures. A practical challenge remains near the radar, where the sampling volume is small and signal processing (e.g., sidelobe impact and ground clutter suppression) can distort radar measurements. The under-detection of precipitation in these regions is likely due to such corrupted data. This issue may be mitigated by adopting a hybrid scan strategy—such as a Constant Altitude Plan Position Indicator (CAPPI)—specifically for regions close to the radar.

Department(s)

Civil, Architectural and Environmental Engineering

Publication Status

Open Access

Comments

National Research Foundation of Korea, Grant 202412100001

Keywords and Phrases

GOES-16; NEXRAD; non-precipitation echo removal; random forest

International Standard Serial Number (ISSN)

2072-4292

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2026 The Authors, All rights reserved.

Creative Commons Licensing

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

01 Mar 2026

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