Indonesia is an archipelago country with great potential for marine renewable energy, particularly for wave energy. This study will provide a wave energy assessment of Indonesia over a 6.5-year ...period (2011–2017) with resolution about 5.5 km. This assessment is based on data generated with a two-way nested high-resolution wave model WAVEWATCH III with observation-based physics (ST6). Three grids have been generated, namely a ‘high resolution’ of 3 arc-minute (0.05°) grid is nested inside a 12 arc-minute (0.2°) grid which is nested within a 0.5° global grid. Validations against altimeters and buoys show good agreement with the model. Mean wave energy has been classified based on meteorological seasons and it is found that the most energetic months are June, July, August for all areas of south, southwest and west of Indonesia where it can exceed 30 kW/m. In some locations wave energy is available throughout the entire year, that is in the south of Jawa island, Bali island and West Nusa Tenggara while in the region of west Sumatera, promising wave energy is available during the time from March to November. In addition, within the Indonesian Archipelago the monthly variations are about 5 kW/m and relatively small in terms of absolute values but this region is large relative to the mean wave power energy.
•Wave energy assessment for Indonesian archipelago with spatial resolution of 0.05°.•Based on a dynamically downscaled wave model with observation-based physics (ST6).•Mean wave power in excess of 30 kW/m was found for season June to August.•10% of mean wave power exceeds 40 kW/m with the top-one percent up to 60 kW/m.
•The best 4 spectral wave parameterizations have been compared to satellites and buoys.•Higher order spectral moments and wave partitions are rigorously validated.•All models describe the low-order ...wave moments; some perform better for higher ones.•The models are sensitive to the far-field swell and have similar spatial distribution.•The directional spread within the wave spectra performs poorly and needs improvement.
Recent developments in the physical parameterizations available in spectral wave models have already been validated, but there is little information on their relative performance especially with focus on the higher order spectral moments and wave partitions. This study concentrates on documenting their strengths and limitations using satellite measurements, buoy spectra, and a comparison between the different models. It is confirmed that all models perform well in terms of significant wave heights; however higher-order moments have larger errors. The partition wave quantities perform well in terms of direction and frequency but the magnitude and directional spread typically have larger discrepancies. The high-frequency tail is examined through the mean square slope using satellites and buoys. From this analysis it is clear that some models behave better than the others, suggesting their parameterizations match the physical processes reasonably well. However none of the models are entirely satisfactory, pointing to poorly constrained parameterizations or missing physical processes. The major space-time differences between the models are related to the swell field which stresses the importance of describing its evolution. An example swell field confirms the wave heights can be notably different between model configurations while the directional distributions remain similar. It is clear that all models have difficulty describing the directional spread. Therefore, knowledge of the source term directional distributions is paramount to improve the wave model physics in the future.
•New nonlinear wind input source term.•Negative wind input for adverse winds.•New wave breaking and whitecapping dissipation source term.•New swell attenuation source term.
Measurements collected ...during the AUSWEX field campaign, at Lake George (Australia), resulted in new insights into the processes of wind wave interaction and whitecapping dissipation, and consequently new parameterizations of the input and dissipation source terms. The new nonlinear wind input term developed accounts for dependence of the growth on wave steepness, airflow separation, and for negative growth rate under adverse winds. The new dissipation terms feature the inherent breaking term, a cumulative dissipation term and a term due to production of turbulence by waves, which is particularly relevant for decaying seas and for swell. The latter is consistent with the observed decay rate of ocean swell. This paper describes these source terms implemented in WAVEWATCH III ®and evaluates the performance against existing source terms in academic duration-limited tests, against buoy measurements for windsea-dominated conditions, under conditions of extreme wind forcing (Hurricane Katrina), and against altimeter data in global hindcasts. Results show agreement by means of growth curves as well as integral and spectral parameters in the simulations and hindcast.
Twenty years (1996–2015) of satellite observations were used to study the climatology and trends of oceanic winds and waves in the Arctic Ocean in the summer season (August–September). The ...Atlantic-side seas, exposed to the open ocean, host more energetic waves than those on the Pacific side. Trend analysis shows a clear spatial (regional) and temporal (interannual) variability in wave height and wind speed. Waves in the Chukchi Sea, Beaufort Sea (near the northern Alaska), and Laptev Sea have been increasing at a rate of 0.1–0.3 m decade−1, found to be statistically significant at the 90% level. The trend of waves in the Greenland and Barents Seas, on the contrary, is weak and not statistically significant. In the Barents and Kara Seas, winds and waves initially increased between 1996 and 2006 and later decreased. Large-scale atmospheric circulations such as the Arctic Oscillation and Arctic dipole anomaly have a clear impact on the variation of winds and waves in the Atlantic sector. Comparison between altimeter observations and ERA-Interim shows that the reanalysis winds are on average 1.6 m s−1 lower in the Arctic Ocean, which translates to a low bias of significant wave height (−0.27 m) in the reanalysis wave data.
•Two wave models, containing five different source term packages of Sin+Sds+Snl are evaluated under Hurricane Ivan (2004).•Drawbacks of UMWM and ST2 are identified.•The strength of negative wind ...input is discussed within the framework of ST6.•Drag coefficient estimated by each wave model is also inter-compared.
Using the well-observed hurricane case Ivan (2004) as an example, we investigate and intercompare the performance of two wave models under hurricane conditions. One is the WAVEWATCH III model (WW3) and the other is the University of Miami Wave Model (UMWM). Within WW3, four different source term packages (ST2/3/4/6) of wind input, wave breaking dissipation and swell decay are chosen for comparison purposes. Based on the comparisons between model results and measurements from various platforms, we concluded that UMWM shows less accuracy than WW3 in specification of bulk wave parameters. This is possibly because (i) UMWM-estimated drag coefficient does not clearly show a saturation trend when wind speeds are beyond ∼ 35 m s−1 and (ii) the four-wave interaction term of UMWM disagrees evidently with the full solution of the Boltzmann integral in detail. Among the four WW3 source term packages, the older parameterization ST2 is basically the least accurate because of its systematic underestimation of high waves. The remaining three packages (ST3/4/6) are performed well under Ivan. However, we also find that they tend to overestimate energy of waves traveling in the oblique and opposing winds. It is shown that enhancing the strength of negative wind input properly can effectively improve model skills in such situations. Limited by the uncertainty in the wind forcing, we could not determine the most accurate package among ST3/4/6 unambiguously.
Abstract
The observation-based source terms available in the third-generation wave model WAVEWATCH III (i.e., the ST6 package for parameterizations of wind input, wave breaking, and swell dissipation ...terms) are recalibrated and verified against a series of academic and realistic simulations, including the fetch/duration-limited test, a Lake Michigan hindcast, and a 1-yr global hindcast. The updated ST6 not only performs well in predicting commonly used bulk wave parameters (e.g., significant wave height and wave period) but also yields a clearly improved estimation of high-frequency energy level (in terms of saturation spectrum and mean square slope). In the duration-limited test, we investigate the modeled wave spectrum in a detailed way by introducing spectral metrics for the tail and the peak of the omnidirectional wave spectrum and for the directionality of the two-dimensional frequency–direction spectrum. The omnidirectional frequency spectrum
E
(
f
) from the recalibrated ST6 shows a clear transition behavior from a power law of approximately
f
−4
to a power law of about
f
−5
, comparable to previous field studies. Different solvers for nonlinear wave interactions are applied with ST6, including the Discrete Interaction Approximation (DIA), the more expensive Generalized Multiple DIA (GMD), and the very expensive exact solutions using the Webb–Resio–Tracy method (WRT). The GMD-simulated
E
(
f
) is in excellent agreement with that from WRT. Nonetheless, we find the peak of
E
(
f
) modeled by the GMD and WRT appears too narrow. It is also shown that in the 1-yr global hindcast, the DIA-based model overestimates the low-frequency wave energy (wave period
T
> 16 s) by 90%. Such model errors are reduced significantly by the GMD to ~20%.
Wind‐wave hindcast data have many applications including climatology assessments for renewable energy projects, maritime engineering design, event‐based impact assessments, generating boundary ...conditions for further downscaling, amongst others. Here, we present a global wave hindcast with nested high‐resolution grids for the Exclusive Economic Zones of Australia and south west Pacific Island Countries, that is extended in time monthly. The model employs strategic methods to incorporate the effects of subgrid sized features such as small islands and islets. Various bulk wave parameters are available hourly from January 1979 to present, along with the full wave spectra at a set of 3,683 predetermined points distributed globally.
Wind‐wave hindcast data have many applications including for renewable energy projects, maritime engineering design, impact assessments and producing boundary conditions for coastal models to better simulate the waves approaching the shore like shown in the image. Here, we present a global wave hindcast with nested high‐resolution grids for Australia and south west Pacific Island Countries, that spans from 1,079 to present, updated monthly. The model employs strategic methods to incorporate the effects of subgrid sized features such as small islands and islets.
Global wave hindcasts are developed using the third generation spectral wave model WAVEWATCH III with the observation‐based source terms (ST6) and a hybrid rectilinear‐curvilinear, ...irregular‐regular‐irregular grid system (approximately at 0.25°×0.25°). Three distinct global hindcasts are produced: (a) a long‐term hindcast (1979–2019) forced by the ERA5 conventional winds U10 and (b) two short‐term hindcasts (2011–2019) driven by the NCEP climate forecast system (CFS)v2 U10 and the ERA5 neutral winds U10,neu, respectively. The input field for ice is sourced from the Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea‐ice concentration climate data records. These wave simulations, together with the driving wind forcing, are validated against extensive in‐situ observations and satellite altimeter records. The performance of the ST6 wave hindcasts shows promising results across multiple wave parameters, including the conventional wave characteristics (e.g., wave height Hs and wave period) and high‐order spectral moments (e.g., the surface Stokes drift and mean square slope). The ERA5‐based simulations generally present lower random errors, but the CFS‐based run represents extreme sea states (e.g., Hs>10 m) considerably better. Novel wave parameters available in our hindcasts, namely the dominant wave breaking probability, wave‐induced mixed layer depth, freak wave indexes and wave‐spreading factor, are further described and briefly discussed. Inter‐comparisons of Hs from the long‐term (41 years) wave hindcast, buoy measurements and two different calibrated altimeter data sets highlight the inconsistency in these altimeter records arising from different calibration methodology. Significant errors in the low‐frequency bins (period T>15 s) for both wave energy and directionality call for further model development.
Plain Language Summary
Ocean surface waves are fundamentally important for ocean engineering design, ship navigation, air‐sea exchange of gas, heat, momentum and energy, upper ocean dynamics, and remote sensing of the ocean. Spectral wave modeling is an indispensable tool to estimate sea state information. In this study, we present new global wave hindcasts developed using the state‐of‐the‐art model physics and numerics and the modern reanalysis winds and satellite sea ice records. It is demonstrated through validation against in‐situ observations and altimeter records that the global wave hindcasts perform well across multiple parameters. Meanwhile, intercomparisons of wave height from the long‐term hindcast, buoys, and altimeters reveal inconsistency and potential inhomogeneity in these different data sets. The wave hindcasts we developed, in combination with global wave databases published previously, will form a large ensemble of realizations of historical evolution of sea states simulated with distinct wave physics and wind forcing, which will help quantify sea states in real oceans more accurately.
Key Points
Global wave hindcasts using the observation‐based source terms are developed and validated against extensive observations
The wave hindcasts show promising performance across multiple parameters, including wave height, period, and high‐order spectral moments
Intercomparisons of wave height from the hindcast, buoys, and altimeters highlight the inconsistency and inhomogeneity in these data sets
Australia is widely recognised as having an abundant wave energy resource which could contribute to the country's future energy mix. Prior assessments have provided general broad scale information on ...the resource magnitude, but detail needed to support next level site assessments has been deficient. Aiming to support all stakeholders in Australia's emerging wave energy industry, this study presents a revised assessment of Australia's national wave energy resource. The assessment is based on a state-of-the-art global wave hindcast, with higher resolution in the Australian region. Validation of the hindcast relative to in-situ wave buoy and satellite altimeter observations show better comparison than prior assessments. The total nationally available resource is similar in magnitude to earlier studies, but regional differences are evident. The total integrated energy flux across the 200 m contour is approximately 2730 TWh/yr, with estimates of resource along the north and eastern coasts being less than previously estimated. This revised pre-competitive resource information is delivered coincidently with marine management and alternative use (constraint layers), and energy infrastructure, spatial information via the open-access Australian Wave Energy Atlas (AWavEA), served through the Australian Renewable Energy Infrastructure (AREMI). The Atlas serves to reduce barriers to emergence of an Australian wave energy industry.
•Revised estimates of Australia's wave energy resource specify altered spatial distribution.•Total integrated wave energy flux across 200 m contour is 2730 TWh/yr.•Resource information being served openly via web-based Australian Wave Energy Atlas.
Hai Yang-2 (HY-2) satellite altimeter measurements of significant wave height () are analyzed over the period from 1 October 2011 to 6 December 2014. They are calibrated and validated against in situ ...buoys and other concurrently operating altimeters: Jason-2, CryoSat-2, and Satellite with Argos and ALtiKa (SARAL). In general, the HY-2 altimeter measurements agree well with buoy measurements, with a bias of -0.22 m and a root-mean-square error (RMSE) of 0.30 m. When the reduced major axis (RMA) regression procedure was applied to the entire period, the RMSE was reduced by 33% to 0.2 m. A further comparison with other satellite altimeters, however, revealed two additional features of HY-2 estimates over this period. First, a noticeable mismatch is present between HY-2 and the other satellite altimeters for high seas ( > 6 m). Second, a jump increase in HY-2 values was detected starting in April 2013, which was associated with the switch to backup status of the HY-2 sensors and the subsequent update of its data processing software. Although reported by previous studies, these two deficiencies had not been accounted for in calibrations. Therefore, the HY-2 wave height records are now subdivided into two phases (time periods pre- and post-April 2013) and a two-branched calibration is proposed for each phase. These revised calibrations, validated throughout the range of significant wave heights of 1-9 m, are expected to improve the practical applicability of HY-2 measurements significantly.
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