The Wide Swath Significant Wave Height: An Innovative Reconstruction of Significant Wave Heights From CFOSAT's SWIM and Scatterometer Using Deep Learning. Issue 6 (18th March 2021)

• Record Type: Journal Article
• Authors: Wang, J. K.; Aouf, L.; Dalphinet, A.; Zhang, Y. G.; Xu, Y.; Hauser, D.; Liu, J. Q.
• Is Part Of: Geophysical research letters. Volume 48:Issue 6(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-03-18
• Subjects: assimilation -- CFOSAT -- deep neural network -- wide swath SWH; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2020GL091276
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: The accuracy of a wave model can be improved by assimilating an adequate number of remotely sensed wave heights. The Surface Waves Investigation and Monitoring (SWIM) and Scatterometer (SCAT) instruments onboard China‐France Oceanic SATellite provide simultaneous observations of waves and wide swath wind fields. Based on these synchronous observations, a method for retrieving the SWH over an extended swath is developed using the deep neural network approach. With the combination of observations from both SWIM and SCAT, the SWH estimates achieve significantly increased spatial coverage and promising accuracy. As evidenced by the assessments of assimilation experiments, the assimilation of this "wide swath SWH" achieves an equivalent or better accuracy than the assimilation of the traditional nadir SWH alone and enhances the positive impact when assimilated with the nadir SWH. Therefore, insights into the better utilization of wave remote sensing in assimilation are presented. Plain Language Summary: Data assimilation is an effective way to improve wave numerical simulations, and its impact is related to both the quality and the quantity of wave observations. China‐France Oceanic SATellite (CFOSAT) carries two instruments, namely, Surface Waves Investigation and Monitoring (SWIM) and a scatterometer (SCAT), which are designed to provide along‐track wave parameters and wind observations over a wide swath, respectively. By combining observations from both instruments, we propose a method to estimate the significant wave height (SWH) over a wide swath (typically ±100 km on each side of the nadir track) that aims to achieve an accuracy as good as the SWIM nadir and an improved spatial coverage. The method is developed by using the deep neural network approach. With the advantage of both a significantly increased spatial coverage and reasonable accuracy, the wide swath SWH has the potential to enhance the positive impacts of wave assimilation. Assimilation experiments demonstrate that the wide swath SWH achieves impacts as good as the assimilation of the SWIM nadir SWH and enhances the accuracy of the wave model when assimilated together with the nadir SWH. Hence, synchronous observations from CFOSAT will facilitate relevant applications in operational wave forecasting. Key Points: An innovative method for extending significant wave height (SWH) from nadir to a wide swath is presented A deep neural network model is developed based on simultaneous observations from the nadir beam and wind scatterometer of the CFOSAT Significant positive impacts are found in the assimilation of the "wide swath" SWH compared to the assimilation of the Surface Waves Investigation and Monitoring nadir only