Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

Abstract

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

A Technique for SAR Significant Wave Height Retrieval Using Azimuthal Cut-Off Wavelength Based on Machine Learning

“…However, these observations have significant limitations, especially regarding marine research over large spatial coverage. Thanks to advancements in computer science and oceanography theory, numeric wave models such as WAVEWATCH-III (WW3) [1] and Simulation Wave Nearshore (SWAN) [2] have been developed. However, these models typically have a coarse spatial resolution of 10 km, limiting the hindcasting wave application for regional studies on marine science, especially in coastal waters.…”

“…Monitoring the sea surface in near real-time has become a crucial area of research thanks to the increasing popularity of remote-sensing technology for earth observation since the 1970s. Currently, the remote-sensed products are officially released, including sea surface wind from a scatterometer [2], significant wave height (SWH) from an altimeter [3], and wave spectrum from Surface Wave Investigation and Monitoring (SWIM) onboard Chinese-French Oceanography SATellite (CFOSAT) [4,5]. However, monitoring nearshore waves with high precision remains a challenge for the remote sensing community, although two high-frequency phased-array radars [6] can detect swell and current in coastal areas.…”

Feasibility of Wave Simulation in Typhoon Using WAVEWATCH-III Forced by Remote-Sensed Wind

“…This difficulty could be solved using real measurements from remote-sensed wind products. Although assembling winds from remote-sensed products has served as the forcing field for the numeric wave model over the global ocean [24], the feasibility and accuracy of remote-sensed wind data used in typhoon-induced wave simulation are important questions worth investigating. At present, typhoon-induced waves are usually simulated via the wave model forced by reconstructed wind products [25] and predicted via artificial intelligence methods with remote-sensed products [26].…”