Abstract
Though the ocean is sparsely populated by buoys that feature co-located instruments to measure surface winds and waves, their data is of vital importance. However, due to either minor instrumentation failure or maintenance, intermittency can be a problem for either variable. This paper attempts to mitigate the loss of valuable data from two opposite but equivalent perspectives: the conventional reconstruction of significant wave height (SWH) from Caribbean Sea buoy-observed surface wind speeds (WSP) and the inverse modeling of WSP from SWH using the long short-term memory (LSTM) network. In either direction, LSTM is strongly able to recreate either variable from its counterpart with the lowest correlation coefficient (r2) measured at 0.95, the highest root mean square error (RMSE) is 0.26 m/s for WSP, and 0.16 m for SWH. The highest mean absolute percentage errors (MAPE) for WSP and SWH are 1.22% and 5%, respectively. Additionally, in the event of complete instrument failure or the absence of a buoy in a specific area, the Simulating WAves Nearshore (SWAN) wave model is first validated and used to simulate mean and extreme SWH before, during, and after the passage of Hurricane Matthew (2016). Synthetic SWH is then fed to LSTM in a joint SWAN—LSTM model, and the corresponding WSP is reconstructed and compared with observations. Although the reconstruction is highly accurate (r2 > 0.9, RMSE < 1.3 m/s, MAPE < 0.8%), there remains great room for improvement in minimizing error and capturing high-frequency events.
Highlights
For an incredibly diverse range of coastal and open ocean studies, numerical model output, satellite data, and reanalysis products dominate the methodologies employed by researchers worldwide and are often used to supplement if not completely replace in situ platforms such as buoys
Extreme peaks were identically not captured. These results can be strongly contrasted with the wind speed reconstructions of the previous section, where when wave observations were used for inversions, r2 was much higher, and root mean square error (RMSE) and mean absolute percentage errors (MAPE) were much lower than where model simulations were used
For a wide range of coastal and oceanic applications, observations of basic metocean variables are of paramount value, and it is for this reason that wealthy nations have deployed them in large numbers
Summary
For an incredibly diverse range of coastal and open ocean studies, numerical model output, satellite data, and reanalysis products dominate the methodologies employed by researchers worldwide and are often used to supplement if not completely replace in situ platforms such as buoys. Kambekar and Deo used two data-driven models, GP, and MT to simulate and forecast waves using WSP at eight different buoys and found that while both methods performed satisfactorily, MT estimated higher waves more accurately [16]. Charhate et al compared GP and an ANN in the inverse modeling of deriving wind parameters from wave information and found that GP produced more accurate results [18]. In this study, two fundamental observations made by buoys, surface winds and significant wave height, are converted from its counterpart through the usage of the LSTM network. LSTM is coupled with SWAN and used to reconstruct mean and extreme (hurricane) wind speed from modelled significant wave height.
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