Wide-swath satellite altimetry and novel subsurface observations improve predictions in a dynamic western boundary current: Resolving complex fine-scale structures.
Abstract. Eddying oceanic regions adjacent to continental shelves typically exhibit complex flow structure and, while the broad-scale ocean surface is well observed by satellites, the ocean's fine-scale flows and complex vertical subsurface are typically undersampled. The combination of oceanographic observations and numerical models, through data assimilation, produces data-constrained, four-dimensional fields that support a range of ecological and economic needs. Data assimilation makes use of a numerical model to place observations in dynamical context, yet it is particularly challenging for highly variable oceanic flows with sharp spatial gradients and where observations are sparse in space and/or time relative to the ocean's scales of variability. Here we use observing system experiments to assess the impact of recently-available observations from the Surface Water Ocean Topography satellite (SWOT, observing the ocean's fine-scale height structure) and from the Fishing Vessel Observation Network (FVON, providing wide spread high-resolution crowd sourced temperature data below the surface) on regional ocean model estimates in an eddy-dominated western boundary current region, the East Australian Current. In our companion paper, we used temporally and/or spatially averaged statistics to describe model-observation differences and show that SWOT substantially improves sea surface height representation across scales and FVON data strongly improves subsurface temperature representation in coastal and shelf regions. While these averaged statistics are useful to demonstrate overall impact of various observation platforms, they often do not capture the detail of how data assimilation reproduces fine-scale dynamical features - precisely the features that are resolved by SWOT and FVON. Here we present select case studies to demonstrate the specific time and space structure of observation impacts. Using independent cruise, glider, drifter and FVON observations, we show that SWOT and FVON can provide significant improvement in the representation of complex fine-scale oceanic structure. The results demonstrate that wide-swath altimetry and fishing-vessel subsurface observations provide complementary constraints: SWOT data improves the surface expression and evolution of eddies, while FVON data constrains the vertical thermal structure that surface observations alone cannot resolve. The case studies also reveal that small errors in feature placement can lead to large subsurface errors, underscoring the need for sustained subsurface observing and uncertainty-aware state estimation. Our results demonstrate that integrating SWOT and FVON observations into regional prediction systems offers a pathway toward more reliable ocean state estimates (essential for navigation, fisheries, marine heatwave monitoring, ecosystem applications, and coastal management) in an energetic boundary current system.
This paper evaluated the impact of SWOT and FVON observations on the East Australian Current system by using observing system experiments with an ocean data assimilation system, ROMS-4DVar. This work is successor of Kerry et al., (2026c) which assessed statistical performance for 1-year experiments, and focuses on submesoscale and frontal variations through specific case studies using independent observation data. To evaluate specific cases is essential for data assimilation system because statistical error is the aggregation of individual errors. It can reveal the model uncertainties and be helpful to improve the assimilation system. Furthermore, this paper will support the importance of maintaining observation networks consisting of fine-resolution SSH altimetry and subsurface in-situ observations. While the manuscript presents results sufficient for publication, I found the writing style, particularly regarding the figures, to be unclear and there are several issues and concerns which should be improved. The detail is described below.
General comment
(1) Within a one-year data assimilation cycle, demonstrating the impact of observational data on specific cases is crucial for evaluating the performance of a data assimilation system. However, as the data assimilation cycle is repeated, it becomes unclear whether the results of a specific assimilation cycle stem from the influence of the observational data at that time or are carried over from the results of the previous cycle. By clarifying this point, the impact of observational data can be better emphasized. To achieve this, it is desirable to also present the analysis increment together with the results. In particular, it is considered useful to evaluate the analysis increment in the cases discussed in Sections 3.1 and 3.3, which focus on a single cycle.
For example, TRAD+SWOT+FishSOOP results in Section 3.1 showed the degradation of water temperature score due to the eastward extent of warm water from the WCE. Looking at the SWOT SSH map in Figure 2a, it appears to be less observation around there between paths. FishSOOP observations are also not seen, except in coastal areas (Figure 2a). Why did the degradation occur? Is it attributed to the increment or the model dynamics? In other word, is the warm water stream related with the previous assimilation cycle?
(2) The results in Section 3.4 are interesting: the experiment assimilating SWOT data has improved the reproduction of eddy, and the results show consistency with the drifter buoyâs trajectory. However, during the cycle from June 14 to June 20, the buoy moved toward the center of the cold-water eddy located around 34°S. If the buoy were being moved by the geostrophic current, it would be expected to move along the edge of the eddy, as seen in the results from other cycles. Is the buoy movement toward the eddy center caused by drag associated with atmospheric disturbances? Please describe the possible factors. Additionally, do the modelâs velocity field match with buoy motion? Through quantitative evaluation of the velocity field, it should be possible to strengthen the impact of the observational data. Please clarify this point in the manuscript.
(3) SST data is grided into about 15-km resolution in assimilation. On the other hand, SWOT SSH data with a resolution of 2 km is used. Why did thinning process not applied for SWOT data? The resolution of numerical ocean model is about 2.5 to 6 km as described in L146. In general, effective resolution to represent phenomena is lower than the original resolution. While I understand the idea to utilize finer-resolution SSH data, the 2-km SWOT data could lead to a lower accuracy due to representative errors. Please clarify this point in the manuscript. This may be future work, but it will be helpful and informative for readers who want to assimilate SWOT data.
(4) Based on Figures 4 and 5, the strength of the modelâs stratification appears to be weaker than that observed. Furthermore, as noted in L441, there are cases where the vertical step-like structures are not adequately represented. To begin with, is the vertical resolution designed to adequately represent these structures? If not, strongly constraining the model to observations may disrupt the dynamical balance. Also, assuming such a situation, would ensemble methods be still effective? Of course, as the authors mentioned in L321, ensemble methods have the potential to improve model accuracy. However, applying ensemble mean without considering the modelâs representational limitations is highly likely to smooth out fine horizontal and vertical structures such as fronts and submesoscales. Therefore, although this would extend beyond the scope of this paper, I would like the authors to specifically address the advantages of ensemble methods for fine-scale structures in the manuscript, if possible.
(5)Â Overall, the description of the figures is insufficient. These points are listed below.
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Specific comments
Section 2.4
L224 âmiminisedâ is âminimizedâ?
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Section 2.5
There are many typos for experiment names as mentioned above and below. How about use simple names for the experiments?
Fo example, Exp_T for TRADshortTS, Exp_TS for TRAD+SWOTsh, Exp_TF for TRAD+FishSOOP, and Exp_TSF for TRAD+SWOT+FishSOOP. It can be help to distinguish the experiment names and the observation names (SWOT, FishSOOP).
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Section 3.1
L281 âOct 2024â is âOct 2023â. Same in L286.
L281: In this paper, assimilation window used is 6 days, but firstly the description â18-24th Octâ looks like 7 days. I imagine that the ideas of the authors is the period from 18 Oct 00:00 to 24 Oct 00:00 (UTC). It is better to use â18-23 Octâ for 6-day window.
L286 (13 6-day windows) would be (13 cycles of the 6-day window).
P15Â In Figure 4, profiles observed by Argo floats are depicted in the left column. However, there seems to be no description in the manuscript. If the figures are necessary for this paper, please describe the results in the manuscript. In addition, the number of the data points extracted from the model appears to be larger than that of observation. Is this correct? If so, why does this occur?
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Section 3.2
L331 (15 6-day windows) would be (15 cycles of the 6-day window).
L336 âFISHSOOPâ should be âFishSOOPâ
L339 âTRAD+SWOTshortâ should be âTRAD+SWOTshâ
L339 and Figure 6. â1 Aug â 30 Oct2023â in L339, but â1 Aug 2023 up to Oct 30th 2023â in Figure 6. It might be better to use the same writing formats if you do not have any specific reasons. This point is also observed in other sentences.
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Section 3.3
L374 As shown in Figure 7f and 7d, temperature biases reduced from 0.89 to 0.1. Is it true â11% improvement in biasâ?
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Section 3.4
L406 (5 6-day assimilation cycles) would be (5 cycles of the 6-day window)
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References
L585-802 The same references seem to be listed (L717-720). Some references have no journal name (L652-643), and writing style of journal name is inconsistent (L711 and L736, L722 and L759, etc.) It might be better to reorder the papers by Kerry et al in L661-688. Please recheck the submission guideline, especially âReferencesâ