Abstract: Reconstructing sea-surface meteorological and hydrological data offers an effective solution to issues such as missing data, data anomalies, and discontinuous sequences in buoy observations. This approach significantly enhances the completeness and accuracy of observational data, thereby providing reliable data support for marine environmental analysis.This study is centered on the high - precision reconstruction of missing buoy meteorological and hydrological data. Initially, we evaluated the suitability of reanalysis data for reconstructing buoy - measured meteorological and hydrological elements. The findings reveal that, owing to factors like inadequate spatial resolution and disparities in data sources, systematic deviations exist between reanalysis data and buoy - measured data. Consequently, it is challenging to directly meet the requirements for high-precision reconstruction of these elements using reanalysis data.To overcome this challenge, we developed two types of reconstruction models: a wavelet-decomposition-based multi-time- cale weighted reconstruction model (WD-MTSWR) and a random - forest - based temporal downscaling reconstruction model (RF-TDR). These models are designed to conduct high - frequency reconstruction of five core meteorological and hydrological elements: air temperature (T), zonal wind component (U), meridional wind component (V), sea - level pressure (MSL), and sea-surface temperature (SST). The WD-MTSWR model rectifies the discrepancies between reanalysis data and buoy observation data, thereby improving the reconstruction accuracy of hourly - scale elements. The RF-TDR model performs temporal downscaling by learning the variation patterns between hourly observations and 10 - minute high - frequency observations.Validation results demonstrate that the WD - MTSWR model exhibits outstanding reconstruction accuracy. The correlation coefficients (r) for each reconstructed element range from 0.75 to 1.00, while the root mean square error (RMSE) and mean absolute error (MAE) range from 0.24 to 1.89 and 0.20 to 1.40, respectively. This reconstruction accuracy is significantly higher than that of the original reanalysis data and traditional reconstruction models based on the original sequences.The RF-TDR model effectively enhances the accuracy of temporal downscaling. Compared with traditional linear interpolation methods, the correlation coefficient (r) of the downscaling results increases by 0.00 to 0.03, while the RMSE and MAE decrease by 0.02 to 0.20 and 0.00 to 0.10, respectively.The WD - MTSWR and RF-TDR models developed in this study can effectively improve the reconstruction accuracy of missing meteorological and hydrological elements from buoys, offering high-quality, high-temporal-resolution data support for marine environmental monitoring.

Key words: Moored buoys, wavelet decomposition, multilayer perceptron model, time downscaling, time series reconstruction