Implementation of a multi-grid operational wave forecast in the South Atlantic Ocean

Campos, Ricardo Martins; D’Agostini, Andressa; França, Bruna Reis Leite; Damião, A.L.A.; Soares, C. Guedes

doi:10.1016/j.oceaneng.2021.110173

Cited by 10 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) Increasing the temporal criterion τ to values above 1800 s compromises the RMSE of the final estimates, as shown in Section 3.1. The effect of a 1 h time lag on the final error is close to the NDBC and altimeter intrinsic errors [38] and just below the accuracies of calibrated wave forecast products using WAVEWATCH III [36,69,70]. For U10, the temporal criterion τ is even more critical, anchoring the ideal temporal criterion to 1800 s.…”

Section: Discussionmentioning

confidence: 90%

“…For 1 h only, it starts with 12% for U10 and 8% for Hs. Table 2 summarizes the discussion so far, presenting the RMSD, SI, and CC for time lags ranging from 1 to 12 h. For the first hour (1 h time lag), the RMSD of Hs is already very close to the accuracy of the wave buoy, which is equal to 0.2 m according to the NDBC [38], and it is very similar to the RMSE of calibrated forecast products using WAVEWATCH III, which ranges from 0.2 to 0.5 m according to [36,69,70]. This is also valid for U10, where the 1 h time lag is even higher than the NDBC accuracy of 0.55 m/s.…”

mentioning

confidence: 86%

“…Next, two options are possible: (1) the single closest altimeter record to the fixed buoy's position is selected or (2) the average is calculated for along-track altimeter records attending a space-time criteria. Zieger et al [25] argues that the spatial average provides a more statistically stable comparison than a single-point observation-which is the approach chosen by most of the studies in the area, e.g., [4,7,13,19,20,23,25,[28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

Campos

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

This paper investigates the spatial and temporal variability of significant wave height (Hs) and wind speed (U10) using altimeter data from the Australian Ocean Data Network (AODN) and buoy data from the National Data Buoy Center (NDBC). The main goal is to evaluate spatial and temporal criteria for collocating altimeter data to fixed-point positions and to provide practical guidance on altimeter collocation in deep waters. The results show that a temporal criterion of 30 min and a spatial criterion between 25 km and 50 km produce the best results for altimeter collocation, in close agreement with buoy data. Applying a 25 km criterion leads to slightly better error metrics but at the cost of fewer matchups, whereas using 50 km augments the resulting collocated dataset while keeping the differences to buoy measurements very low. Furthermore, the study demonstrates that using the single closest altimeter record to the buoy position leads to worse results compared to the collocation method based on temporal and spatial averaging. The final validation of altimeter data against buoy observations shows an RMSD of 0.21 m, scatter index of 0.09, and correlation coefficient of 0.98 for Hs, confirming the optimal choice of temporal and spatial criteria employed and the high quality of the calibrated AODN altimeter dataset.

show abstract

Section: Discussionmentioning

confidence: 90%

mentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

Campos

2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…These combinations allow us to more closely understand the entire physical process of wave development and dissipation. Recently, a wave analysis physics package based on observations was developed [6,8], and the accuracy and wave forecast performance of numerical simulations have improved as the physical mechanism for nonlinear wave interactions has improved by comparison study between physical packages in WW3 [27,28]. The resolution of computational domains increased with the improvement of wave models, and the development of data assimilation and numerical simulation techniques contributed to gradually improving the forecasting performance [29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Forecasting Performance for ST6 Parameterization in High-Resolution Wave Model Based on WAVEWATCH III

Roh

Chang

et al. 2023

JMSE

View full text Add to dashboard Cite

A regional wave forecasting system in East Asia, including the Korean Peninsula, was built based on WAVEWATCH III using offshore wind forecast data from the Global Data Assimilation Prediction System. The numerical simulations were performed on the sensitivity of the interaction between input wind and wave development. The forecasts for each condition were compared and verified with the observational data of marine meteorological buoys from 1 August to 30 September 2020. The sensitivity conditions were configured to have a specific range of variables related to the directional distribution of input winds (SINA0) and variables indicating the development of input wind–wave (CDFAC) in the ST6. The results were presented by calculating the mean error and root mean square error for all observation points. Overall, as the CDFAC increased, the mean error tended to decrease according to the forecast time and the root mean square error increased. Although the effect of SINA0 at the same CDFAC was insignificant, when SINA0 increased in sections where the significant wave height decreased rapidly, the significant wave height tended to decrease. In addition, the main variables that affect the physical process of wind–wave interaction should be considered to improve wave model forecasting performance and accuracy.

show abstract

“…Therefore, an accurate prediction of waves is of great importance to a better understanding of the physical process in marine environments and engineering design. Numerical models, for example, SWAN (Hsu & Holland, 2007;Muraleedharan et al, 2022;Rusu & Soares, 2013) or WAVE WATCH III (Campos et al, 2022;Zheng et al, 2021) are popular models of predicting waves nowadays, in which waves are simulated with the combination of physics-based models (Hokimoto et al, 2003;Reikard & Rogers, 2011), for example, energy balance equation (Nitsure et al, 2012). Although numerical models can capture the regional pattern by nesting global ocean wave models to coastal and near-shore high-resolution wave models (Sandhya et al, 2014), it sometimes fails to forecast local wave parameters.…”

mentioning

confidence: 99%

Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds

Zhang

Yang

Zhang

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind‐decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves.

show abstract

Implementation of a multi-grid operational wave forecast in the South Atlantic Ocean

Cited by 10 publications

References 45 publications

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

Analysis of Spatial and Temporal Criteria for Altimeter Collocation of Significant Wave Height and Wind Speed Data in Deep Waters

Sensitivity Analysis of Forecasting Performance for ST6 Parameterization in High-Resolution Wave Model Based on WAVEWATCH III

Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds

Contact Info

Product

Resources

About