This study investigates the impact of sea surface roughness, influenced by both wind and wave characteristics, on GNSS-R observations using TRITON data. Traditional wind speed inversion algorithms often simplify Delay-Doppler Maps (DDM) to represent sea surface roughness caused solely by wind. However, this research highlights the critical role that wave-induced roughness plays in the scattering of microwave signals. To analyze these effects, the study employs the ResNet18 deep convolutional neural network, which excels at handling the complex features present in DDM data. The model integrates parameters such as wave conditions and GNSS-R incident angles to extract relevant features, enhancing wind speed prediction accuracy. The research uses TRITON data along with ECMWF wind speed and sea surface parameters for model training and evaluation. The findings indicate substantial improvements in wind speed prediction accuracy when accounting for both wind- and wave-induced roughness. This comprehensive approach reduces prediction errors and provides more reliable data for applications such as weather forecasting and climate modeling. These results underscore the potential of deep learning to integrate detailed sea surface characteristics into GNSS-R observations, offering significant advancements in predictive accuracy and operational applications.
High-frequency radar (HFR) observations of meteotsunamis offer real-time measurements of surface ocean currents across extensive spatial areas. On the northern shore of Taiwan, HFR remote sensing systems operating in the high-frequency band have successfully detected meteotsunami-induced surface current triggered by convective storms. During identified meteotsunami occurrences, the amplified radial velocity of 0.1-0.2 m/s is observed and closely followed by the maximum wave height. Changes of the radial velocity between 3 and 18 km is crucial for early meteotsunami detections.
KEYWORDS: Quality control, Radar, Associative arrays, Data storage, Data analysis, Environmental monitoring, Data conversion, Wind speed, Statistical analysis, Scientific research
This study aims to improve the online Quality Control (QC) process for Taiwan's Very High-Frequency (VHF) coastal radar system, specifically for ocean current mapping. Implementing a robust multi-level data system ensures the accuracy and reliability of real-time data collected step-by-step for ocean monitoring. The methodology combines automated monitoring, statistical analysis, and manual inspections to classify data quality using several QC measures. These measures are organized into levels, including raw data verification, initial data calibration, and advanced spatial-temporal data integration. This approach enhances the precision of ocean current measurements for maritime navigation, resource management, and scientific research.
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