The study, published in the journal Ocean Dynamics, draws on continuous wave measurements recorded between 2017 and 2019 by the coastal moored buoy CB06, operated by the National Institute of Ocean Technology under India’s Ministry of Earth Sciences. The buoy sits in shallow water at a depth of just 16 metres, close enough to the shore that its readings are directly relevant to coastal engineering, port operations, erosion management and navigation. Rather than treating the measured waves as a single undifferentiated field, the researchers applied a wave steepness algorithm to each recorded wave spectrum, a technique that exploits the fundamental physical difference between young, steep wind seas and mature, low-steepness swells. By sorting the energy in every spectrum into these two categories, the team could track the significant wave height of the swell component and the significant wave height of the wind sea component separately, and follow how each evolved through three distinct seasonal windows: the pre-monsoon months of February through May, the southwest monsoon months of June through September, and the post-monsoon northeast monsoon months of October through January.

The technical logic behind the separation is deceptively simple. Wind seas, generated by local winds, tend to be relatively steep because the waves are still growing under active forcing; swells, having left their generation region behind, lose steepness as they disperse and travel. Steepness-based partitioning therefore acts as a physical fingerprinting tool, allowing each directional wave spectrum measured by the buoy to be sliced into a swell part and a wind sea part. From these partitions the researchers derived Hm0s, the significant wave height attributable to swells, and Hm0w, the significant wave height attributable to wind seas, and then examined interannual variations across the three-year record. This decomposition matters because the two components carry different information: wind seas signal what the local atmosphere is doing right now, while swells preserve a memory of winds that blew days earlier, sometimes on the other side of the basin.

The headline finding of the analysis is that swells dominate the wave field off Chennai. Across all three years, the total significant wave height, Hm0, correlated more strongly with the swell component than with the wind sea component, confirming that the character of the sea at this location is set primarily by long-period waves arriving from distant generation areas rather than by locally born wind waves. This is consistent with a broader understanding of the North Indian Ocean, where the wave climate along the Indian east coast is shaped substantially by swells propagating from the Southern Indian Ocean and from the Bay of Bengal itself. For coastal practitioners, the implication is significant: design conditions, sediment transport estimates and coastal flood assessments off Chennai cannot be built on local wind statistics alone, because the largest and most consistent share of wave energy arrives as swell.

What elevates the study beyond a climatological description is the year that sits at its centre. The 2017 to 2019 window happened to bracket a major climate event: in 2019, a strong El Niño-Southern Oscillation episode coincided with one of the strongest positive phases of the Indian Ocean Dipole on record, a coupled ocean-atmosphere pattern in which the western Indian Ocean becomes unusually warm relative to the east. These modes are known to reorganise winds and rainfall across the Indo-Pacific, but their fingerprints on the partitioned wave climate of the Bay of Bengal had been harder to pin down from direct measurements. The Chennai buoy record caught those fingerprints clearly.

During the 2019 southwest monsoon, the wind field over the study area showed an increased occurrence of winds blowing from between 180 and 270 degrees, a southwesterly bias consistent with the large-scale circulation anomalies that a strong positive Indian Ocean Dipole tends to impose on the region. More strikingly, during the pre-monsoon period of 2019, the researchers observed unusual southeasterly winds, a departure from the patterns seen in 2017 and 2018 that coincided with the evolving El Niño conditions. The wave record responded in kind. The anomalous southwesterly winds during the 2019 monsoon were accompanied by an increased occurrence of young swells, waves that had recently left their generation area and had not yet fully matured, alongside a reduction in the annual swell percentage. In other words, the reorganised wind field did not merely strengthen local waves; it altered the age and origin structure of the swell population itself.

The pre-monsoon season told the opposite story. As El Niño conditions developed, the study recorded more swell-dominated conditions during the pre-monsoon months, with the swell share of wave energy rising relative to the preceding years. The contrast between a windier, more wind-sea-rich monsoon and a swell-rich pre-monsoon in the same year illustrates how a single climate event can push the wave climate in different directions at different times of the year, depending on how it reshapes the regional wind field and the swell pathways feeding the coast. For the Chennai coast, this means that climate teleconnections are not an abstract background factor but an active modulator of the day-to-day wave conditions that beaches, breakwaters and fishing communities actually experience.

The most quantitatively dramatic result concerns the wind sea component during the 2019 monsoon. The occurrence of wind sea significant wave heights exceeding 0.5 metres increased by 25 percent relative to 2017 and by 24 percent relative to 2018. In a shallow 16-metre water column, wind seas of that scale are far from trivial: they contribute directly to nearshore turbulence, sediment stirring and the wave-induced stresses that drive coastal erosion, a persistent problem along the Chennai shoreline. A quarter-century-scale jump in the frequency of such conditions within a single anomalous year demonstrates how quickly the shallow-water wave regime can shift under the influence of basin-scale climate variability, and how important it is for coastal models and operational forecasting systems to account for interannual climate modes rather than relying solely on a mean seasonal climatology.

The study also adds to a growing body of work showing that the Indian Ocean’s wave climate is tightly coupled to its leading climate modes, including ENSO and the Indian Ocean Dipole, which modulate wind patterns, swell generation and wave propagation pathways across the basin. Earlier research has linked these modes to wave climate variability in the eastern Arabian Sea and to high-swell events along the Indian coast, but direct, partitioned measurements from a shallow-water buoy off the east coast provide a particularly vivid confirmation. Because the data come from a long-running, quality-controlled moored buoy network maintained by the National Institute of Ocean Technology, the record offers the kind of continuous, in situ validation that satellite altimeters and numerical wave models alone cannot always provide in the complex nearshore environment.

The practical consequences reach well beyond academic interest. Chennai is a major port city with dense coastal infrastructure, an eroding shoreline and a large population exposed to marine hazards. Wave climate information that distinguishes swells from wind seas directly improves the inputs to shoreline change models, breakwater design criteria, sediment budget studies and navigational safety assessments. The finding that a strong positive Indian Ocean Dipole year can simultaneously boost wind sea occurrences during the monsoon and swell dominance before it suggests that seasonal and interannual wave forecasts tailored to climate mode outlooks could become valuable tools for coastal managers. As climate variability and change continue to reshape the Indian Ocean’s winds and waves, the humble buoy off Chennai, watching the sea separate its local storms from its far-travelled swells, is helping to write the baseline against which those future changes will be measured.

Subject of Research: Wind sea and swell partitioning in the shallow-water wave climate off Chennai and its modulation by ENSO and the Indian Ocean Dipole

Article Title: Wind sea and swell characteristics in the wave climate off Chennai

Article References: Janakiram, R., Latha, G., Balamurugan, R., & Jena, B. K. (2026). Wind sea and swell characteristics in the wave climate off Chennai. Ocean Dynamics, 76(10), Article 99. https://doi.org/10.1007/s10236-026-01856-x

Image Credits: AI Generated

DOI: 10.1007/s10236-026-01856-x

Keywords: moored buoy, wind seas, swell, wave climate, wave spectra, Bay of Bengal, ENSO, Indian Ocean Dipole, significant wave height, Chennai coast, monsoon, coastal oceanography

The work was carried out by C.W.E. de Korte, M. Verlaan, A. W. Heemink and B. Backeberg, affiliated with Delft University of Technology and the research institute Deltares. Their starting point was a persistent problem: third-generation wave models such as SWAN, the Simulating WAves Nearshore model used in the study, are highly reliable in the open ocean but continue to struggle in coastal shelf seas. Wave-current interactions, uncertain wind forcing, shallow-water effects and imperfect parametrizations of the physical source terms all introduce errors that are difficult to eliminate by calibration alone. Data assimilation offers a different route, blending real observations with the model’s own physics to nudge the simulated ocean state closer to reality without rewriting the underlying equations.

What sets this study apart from most earlier wave data assimilation efforts is the choice of state variable. Conventional operational schemes, such as Optimal Interpolation and three-dimensional variational methods, typically apply corrections only to significant wave height and then scale those corrections back onto the full wave spectrum using simplifying assumptions about how wave energy is distributed across frequencies and directions. The Dutch team instead placed the complete directional wave energy spectrum in the model state. Each ensemble member carried the spectrum across 32 frequency bands and 36 directional bins at every point of a 567-cell grid, producing a state vector of more than 650,000 elements per member. Because the ensemble evolves under the full SWAN physics, the corrections the filter produces are automatically physically consistent with the model, and integral parameters such as mean wave period adjust themselves without any ad hoc scaling.

The DEnKF itself is a deterministic variant of the classic Ensemble Kalman Filter. Rather than perturbing observations with random noise to propagate uncertainty, it updates the ensemble mean and the ensemble anomalies separately, avoiding the sampling errors that stochastic perturbations introduce. This is particularly valuable for small ensembles, and the team settled on 64 members after previous synthetic twin experiments showed the error statistics fully converged at that size. Uncertainty was injected into the system through the wind forcing, treated as the control variable, using a first-order autoregressive noise model with a spatial Gaussian correlation structure. Parameters were derived from the difference between HARMONIE wind analyses and forecasts, giving a standard deviation of two metres per second, a decorrelation timescale of fifteen hours and a spatial decorrelation length of 500 kilometres.

The observations came from seven nadir satellite altimeters: CFOSAT, Haiyang-2B, Cryosat-2, Jason-3, the two Sentinel-3 satellites, and Saral/AltiKa, all retrieved from the Copernicus Marine Environment Monitoring Service. Over the three-month window the satellites contributed 713 tracks over the North Sea, an average of about eight passes per day, with a mean interval of roughly three hours between passes but gaps stretching to nearly fifteen hours. Tracks were sub-sampled every 120 kilometres to avoid overloading individual grid cells, and a coastal mask excluded measurements within 50 kilometres of shore, where altimeter retrievals are known to be unreliable. Observation errors were assumed to be uncorrelated with a standard deviation of 0.2 metres. Hamill localisation experiments comparing the standard EnKF with the DEnKF across localisation radii of 100 to 500 kilometres showed the DEnKF with a 200-kilometre radius performed best, and that configuration became the final set-up.

Validation against the North Sea’s dense network of independent wave buoys delivered strikingly consistent results. Significant wave height errors dropped by a mean of 20.5 percent, from 0.39 metres in the free-running coarse model to 0.31 metres, a performance essentially matching the much finer SWAN-DCSM benchmark model run at roughly 3.6-kilometre resolution. Mean wave period improved at 21 of 23 stations with a ten percent reduction in root mean square error, while the peak period improved at 13 of 16 stations by about five percent. Wind speed showed modest improvements at some stations, though the researchers caution that the station anemometers, corrected to ten-metre equivalent heights assuming a neutral wind profile, carry their own uncertainties over the frequently non-neutral marine boundary layer. Not every parameter benefited: swell wave height degraded slightly on average, and the low-frequency inverse moment period and mean wave direction, each measured at only a handful of stations, also worsened marginally.

Spectral analysis explained the pattern. In unimodal sea states dominated by wind-driven waves, the assimilation corrected the entire wave spectrum in a way that closely matched buoy observations, as demonstrated during a storm peak on 20 January 2022 at the offshore station A121, where the analysis tracks the measured spectra hour by hour. But in mixed sea states where wind-sea and swell are clearly separated, the ensemble spread remained concentrated in the mid and high frequencies, because wind perturbations barely touch an independently propagating swell field and the altimeters measure only total significant wave height. Detailed examination of the largest swell errors revealed two distinct mechanisms: during short-fetch, rapidly rotating local wind conditions, the wind-based error covariances failed to represent the spatial scales at which swell actually varied between neighbouring stations, while a second error type, premature swell arrival at coastal stations, proved to be a systematic bias of the coarse-resolution model rather than a failure of the assimilation itself.

One of the most practically important findings concerns timing. The researchers binned all validation samples by the number of hours elapsed since the last satellite pass and found that prediction errors rose steadily with the length of the gap, particularly for stations in the open central North Sea. Coastal stations, whose errors are dominated by shallow-water processes rather than wind-driven corrections, were less sensitive to the satellite schedule. Because the orbital geometry of the contributing satellites fixes the timing of the gaps, these gaps recur with the tidal cycle, a phase-locking effect the authors flag as deserving further study. The message for forecasters is clear: the temporal density of observations matters, and merging additional data sources could deliver substantial gains.

The authors are candid about the limitations. The coarse 0.5-degree grid, chosen so that a 64-member ensemble of full spectra could be run at all, degrades accuracy near the coast, where resolution, missing triad interactions and a simplified setup all take their toll. Running a high-resolution model within the ensemble framework would require major advances in computing power and memory. Still, the path forward is mapped out: refining the wind noise model, assimilating additional integral wave parameters or even full spectra, incorporating continuous buoy measurements, and adding satellite SAR observations from missions such as Sentinel-1, SWOT and Sentinel-6. Beyond operational forecasting, the team points to wave reanalyses for risk and climate studies, and to the growing demand for high-quality training data for machine-learning wave models. For a shelf sea as busy and economically vital as the North Sea, better wave information from the satellites already overhead is a prize worth the computation.

Subject of Research: Ensemble-based assimilation of satellite altimeter wave measurements in a regional North Sea wave model

Article Title: Exploring the added value of assimilating satellite altimeter measurements in a North Sea wave model using the Deterministic Ensemble Kalman Filter

Article References: de Korte, C., Verlaan, M., Heemink, A. W., & Backeberg, B. (2026). Exploring the added value of assimilating satellite altimeter measurements in a North Sea wave model using the Deterministic Ensemble Kalman Filter. Ocean Dynamics, 76(10), Article 102. https://doi.org/10.1007/s10236-026-01858-9

Image Credits: AI Generated

DOI: 10.1007/s10236-026-01858-9

Keywords: wave data assimilation, Deterministic Ensemble Kalman Filter, satellite altimeter, SWAN wave model, North Sea, significant wave height, wave spectrum, ensemble forecasting, swell, coastal shelf seas, Ocean Dynamics, CMEMS