Error Analysis on ESA’s Envisat ASAR Wave Mode Significant Wave Height Retrievals Using Triple Collocation Model

Wang, He; Zhu, Jianrong; Yang, Jingsong

doi:10.3390/rs61212217

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…Compared to SAR data, the altimeter has some unique advantages in this topic. First, the altimeter‐measured SWH is more accurate and more precise than the swell SWH derived from SAR data [ Queffeulou et al ., ; Wang et al ., ]. Second, the along‐track sampling density of altimeter (about one measurement every 7 km) is higher than that of the SAR wave mode (about one image every 100 km), which can equate to a better statistical significance.…”

Section: Introductionmentioning

confidence: 99%

Tracking the attenuation and nonbreaking dissipation of swells using altimeters

et al. 2016

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A method for systematically tracking swells across oceanic basins is developed by taking advantage of high-quality data from space-borne altimeters and wave model output. The evolution of swells is observed over large distances based on 202 swell events with periods ranging from 12 to 18 s. An empirical attenuation rate of swell energy of about 4 3 10 27 m 21 is estimated using these observations, and the nonbreaking energy dissipation rates of swells far away from their generating areas are also estimated using a point source model. The resulting acceptance range of nonbreaking dissipation rates is 22.5 to 5.0 3 10 27 m 21 , which corresponds to a dissipation e-folding scales of at least 2000 km for steep swells, to almost infinite for small-amplitude swells. These resulting rates are consistent with previous studies using in-situ and synthetic aperture radar (SAR) observations. The frequency dispersion and angular spreading effects during swell propagation are discussed by comparing the results with other studies, demonstrating that they are the two dominant processes for swell height attenuation, especially in the near field. The resulting dissipation rates from these observations can be used as a reference for ocean engineering and wave modeling, and for related studies such as air-sea and wind-wave-turbulence interactions.

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Section: Introductionmentioning

confidence: 99%

Tracking the attenuation and nonbreaking dissipation of swells using altimeters

et al. 2016

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“…Otherwise, for cases in which ocean wave modulation is weak and the majority of a detected signature is speckle noise, the normalized variance should be small and close to 1.0, as in the example shown in Figure 1c (although some kind of signature like atmospheric waves is visible in Figure 1c, there is no apparent ocean surface wave patterns). The normalized variance _ , has thus been used as a parameter to reject non-wave SAR data in various studies e.g., [23,[37][38][39][40][41]. Figure 3 shows the possibility distribution of normalized variance from GF-3 WM imagettes during the period from January to October 2017.…”

Section: Gf-3 Sar Wm Datamentioning

confidence: 99%

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

Wang¹,

Wang

Yang

et al. 2018

Remote Sensing

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Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open ocean. An empirical retrieval algorithm of significant wave height (SWH), termed Quad-Polarized C-band WAVE algorithm for GF-3 wave mode (QPCWAVE_GF3), is developed for quad-polarized SAR measurements from GF-3 in wave mode. QPCWAVE_GF3 model is built using six SAR image and spectrum related parameters. Based on a total of 2576 WaveWatch III (WW3) and GF-3 wave mode match-ups, 12 empirical coefficients of the model are determined for 6 incidence angle modes. The validation of the QPCWAVE_GF3 model is performed through comparisons against independent WW3 modelling hindcasts, and observations from altimeters and buoys from January to October in 2017. The assessment shows a good agreement with root mean square error from 0.5 m to 0.6 m, and scatter index around 20%. In particular, applications of the QPCWAVE_GF3 model in SWH estimation for two storm cases from GF-3 data in wave mode and Quad-Polarization Strip I mode are presented respectively. Results indicate that the proposed algorithm is suitable for SWH estimation from GF-3 wave mode and is promising for other similar data.

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“…To update the values from the altimeters, we utilize double collocation. Note that triple collocation is generally used to identify nonproblematic observation points in the literature (Wang et al, 2014(Wang et al, , 2022. However, in this article, since we utilize only one wave forecast model, that is, WWIII, we only use double collocation.…”

Section: Methodsmentioning

confidence: 99%

A machine‐learning and data assimilation forecasting framework for surface waves

Pokhrel,

Abdelguerfi,

Ioup

2024

Quart J Royal Meteoro Soc

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In this paper, we combine Deep symbolic regression (DSR) and Ensemble Optimal Interpolation‐based Data Assimilation (DA) method to correct the error in the forecasts from the numerical model, WaveWatch III. In our experiments, the DA and DSR training is performed on the hindcasts and then the model is integrated forward in time with both the numerical model and the symbolic expressions generated from the DSR procedure to generate the forecasts. The DSR method is utilized in this paper to generate the symbolic equations that correct the model error in the WaveWatch III/ DA system. The proposed algorithm takes the zonal (u) and meridional (v) wind components from Global Forecast System (GFS) forecasts, wave heights from WaveWatch III, and geographical coordinates (latitude and longitude) to model physical relationships not included in the original numerical model. The DA is performed using JASON‐2 and SARAL altimeter measurements, and the independent testing uses the in situ buoys The RMSD of the proposed method is better than the numerical model with/without DA for up to 42 hours with only 12 days of assimilation spin‐up cycle. The symbolic equation generated from the proposed framework can be used to correct the predictions from WaveWatch III for weather prediction.This article is protected by copyright. All rights reserved.

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Error Analysis on ESA’s Envisat ASAR Wave Mode Significant Wave Height Retrievals Using Triple Collocation Model

Cited by 14 publications

References 32 publications

Tracking the attenuation and nonbreaking dissipation of swells using altimeters

Tracking the attenuation and nonbreaking dissipation of swells using altimeters

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

A machine‐learning and data assimilation forecasting framework for surface waves

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