Spatially-Adaptive Neural-Network Based Extraction of Significant Wave Heights from HF Radar Data
Abstract
Significant wave height (SWH) retrieval from high-frequency (HF) radar data conventionally relies on Doppler-spectrum processing and interpretation of the weak second-order scattering continuum. This article investigates adaptive spatial processing for time-domain SWH estimation using ordered envelope statistics. In our previous study, a physics-guided linear ordered-statistics estimator (LOSE), motivated by first-order rough-surface scattering assumptions and radar-envelope statistics, mapped the largest rank-ordered magnitudes of Bartlett-beamformed voltages to buoy-observed SWH through least-squares regression. The LOSE framework is extended using three trainable complex spatial filters initialized around the buoy look direction. Ordered magnitudes are extracted independently from each spatial output, concatenated, and mapped to SWH through least-squares regression, while the spatial weights are optimized using gradient-based updates. Both estimators use causal sliding-window averaging. Evaluation used measurements from a 13.385-MHz, 12-receiver element WERA radar at Argentia, Canada, paired with directional-wave buoy observations. Under a common chronological training-evaluation partition, the selected adaptive configuration reduced root-mean-square error by approximately 14.3%, while maintaining the Pearson correlation coefficient at $0.949$. These results support the potential of adaptive multi-beam processing to improve time-domain SWH estimation relative to fixed Bartlett-based LOSE. Feature counts and smoothing windows were selected using the evaluation interval; independent testing is therefore needed to establish the generality of the improvement.
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BibTeX
@misc{chandrasekara2026spatiallyadaptive,
title = {{Spatially-Adaptive Neural-Network Based Extraction of Significant Wave Heights from HF Radar Data}},
author = {Senal Chandrasekara and Reza Shahidi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36301},
eprint = {2610.36301},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36301},
}
RIS
TY - UNPB
TI - Spatially-Adaptive Neural-Network Based Extraction of Significant Wave Heights from HF Radar Data
AU - Senal Chandrasekara
AU - Reza Shahidi
PY - 2026
DA - 2026/10/26
PB - arXiv
DO - 10.48550/arxiv.2610.36301
UR - https://arxiv.org/abs/2610.36301
AB - Significant wave height (SWH) retrieval from high-frequency (HF) radar data conventionally relies on Doppler-spectrum processing and interpretation of the weak second-order scattering continuum. This article investigates adaptive spatial processing for time-domain SWH estimation using ordered envelope statistics. In our previous study, a physics-guided linear ordered-statistics estimator (LOSE), motivated by first-order rough-surface scattering assumptions and radar-envelope statistics, mapped the largest rank-ordered magnitudes of Bartlett-beamformed voltages to buoy-observed SWH through least-squares regression. The LOSE framework is extended using three trainable complex spatial filters initialized around the buoy look direction. Ordered magnitudes are extracted independently from each spatial output, concatenated, and mapped to SWH through least-squares regression, while the spatial weights are optimized using gradient-based updates. Both estimators use causal sliding-window averaging. Evaluation used measurements from a 13.385-MHz, 12-receiver element WERA radar at Argentia, Canada, paired with directional-wave buoy observations. Under a common chronological training-evaluation partition, the selected adaptive configuration reduced root-mean-square error by approximately 14.3%, while maintaining the Pearson correlation coefficient at $0.949$. These results support the potential of adaptive multi-beam processing to improve time-domain SWH estimation relative to fixed Bartlett-based LOSE. Feature counts and smoothing windows were selected using the evaluation interval; independent testing is therefore needed to establish the generality of the improvement.
ER -