Data-Driven Non-Precipitation Echo Removal of NEXRAD Radars Based on a Random Forest Classifier Using Polarimetric Observations and GOES-16 Data

Munsung Keem; Bong-Chul Seo; Witold F. Krajewski; Sangdan Kim

doi:10.3390/rs18050827

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Data-Driven Non-Precipitation Echo Removal of NEXRAD Radars Based on a Random Forest Classifier Using Polarimetric Observations and GOES-16 Data

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Data-Driven Non-Precipitation Echo Removal of NEXRAD Radars Based on a Random Forest Classifier Using Polarimetric Observations and GOES-16 Data

Munsung Keem, Bong-Chul Seo, Witold F. Krajewski and Sangdan Kim

Remote sensing (Basel, Switzerland), Vol.18(5), 827

03/01/2026

DOI: 10.3390/rs18050827

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Abstract

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.

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Details

Title: Subtitle: Data-Driven Non-Precipitation Echo Removal of NEXRAD Radars Based on a Random Forest Classifier Using Polarimetric Observations and GOES-16 Data
Creators: Munsung Keem - University of Iowa
Bong-Chul Seo - Missouri University of Science and Technology
Witold F. Krajewski - University of Iowa
Sangdan Kim - Pukyong National University
Resource Type: Journal article
Publication Details: Remote sensing (Basel, Switzerland), Vol.18(5), 827
DOI: 10.3390/rs18050827
ISSN: 2072-4292
eISSN: 2072-4292
Publisher: MDPI
Number of pages: 27
Grant note: 202412100001 / Pukyong National University RS-2025-00563294 / National Research Foundation of Korea (NRF); National Research Foundation of Korea Iowa Flood Center at The University of Iowa
Language: English
Date published: 03/01/2026
Academic Unit: Civil and Environmental Engineering
Record Identifier: 9985147188002771

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