Comparisons of Tidal Currents in the Pearl River Estuary between High-Frequency Radar Data and Model Simulations

High-frequency (HF) radar data, derived from a pair of newly developed radar stations in the Pearl River Estuary (PRE) of China, were validated through comparison with in situ surface buoys, ADCP measurements, and model simulations in this study. Since no in situ observations are available in the radar observing domain, a regional high-resolution ocean model covering the entire PRE and its adjacent seas was first established and validated with in situ measurements, and then the HF radar data quality was examined against the model simulations. The results show that mean flows and tidal ellipses derived from the in situ buoys and ADCP were in very good agreement with the model. The model-radar data comparison indicated that the radar obtained the best data quality within the central overlapping area between the two radar stations, with the errors increasing toward the coast and the open ocean. Near the coast, the radar data quality was affected by coastlines and islands that prevent HF radar from delivering high-quality information for determining surface currents. This is one of the major drawbacks of the HF radar technique. Toward the open ocean, where the wind is the only dominant forcing on the tidal currents, we found that the poor data quality was most likely contaminated by data inversion algorithms from the Shangchuan radar station. A hybrid machine-learning-based inversion algorithm including traditional electromagnetic analysis and physical oceanography factors is needed to develop and improve radar data quality. A new radar observing network with about 10 radar stations is developing in the PRE and its adjacent shelf, this work assesses the data quality of the existing radars and identifies the error sources, serving as the first step toward the full deployment of the entire radar network.

Automated summary

This study validates the data quality of high-frequency radar data obtained from newly developed radar stations in the Pearl River Estuary through comparison with in situ measurements and model simulations. The results show that the radar data quality is affected by coastlines and algorithms, and a hybrid machine learning approach is needed to improve the data quality.