the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 May 2026
High-resolution dataset of 2024 typhoons in the northern South China Sea captured by a collaborative network of underwater gliders and autonomous underwater vehicles
Abstract. Typhoon-induced ocean responses are not only a key mechanism for regulating global heat transport and maintaining the energy balance of the climate system, but also the core physical processes underlying the intense exchange of matter and energy at the air-sea interface under extreme dynamic forcing. However, traditional passive sampling methods are limited by their discontinuous and sparse spatiotemporal coverage, making it difficult to capture the complete three-dimensional structure and rapid evolution of upper-ocean responses during the critical window of typhoon passage. Autonomous Underwater Vehicles (AUVs) and Underwater Gliders (UGs), with their active tracking and sampling capabilities, can effectively resolve the spatiotemporal evolution of these highly dynamic processes. This paper presents a high-resolution temperature-salinity dataset covering the passage of seven typhoons in the South China Sea during 2024. Constructed from collaborative observations by 62 UGs and 2 AUVs, the dataset achieves an average spatial resolution of 2.4–3.8 km and an average temporal resolution of 3.5–4.3 h (99.7% of the samples had resolutions within 8.4 km and 6.7 h, respectively ). The dataset successfully captures the complex upper-ocean temperature and salinity responses under typhoon forcing, including cooling and salinity increase due to pumping, cooling and salinity decrease triggered by freshwater caps formed by precipitation, cooling and salinity decrease caused by background warm eddies, and significant near inertial oscillations in temperature and salinity. This dataset holds significant potential for in-depth investigation of typhoon-ocean coupling mechanisms and for improving the accuracy of numerical model forecasts.
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Status: open (until 02 Aug 2026)
- RC1: 'Comment on essd-2026-300', Anonymous Referee #1, 01 Jul 2026 reply
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High-resolution dataset of 2024 typhoons in the northern South China Sea Jiawei Qi et al. https://doi.org/10.5281/zenodo.19656867
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- 1
This manuscript presents a valuable temperature–salinity dataset collected by collaborative observations of underwater gliders and AUVs during seven typhoon events in the northern South China Sea. Such observations under extreme weather conditions are potentially valuable for studies of typhoon–ocean interactions, and the released dataset represents a useful contribution to the oceanographic community. However, the current manuscript does not yet document the dataset with sufficient clarity. As a data paper, greater emphasis should be placed on describing the observational strategy, dataset characteristics, quality-control procedure, and limitations of the released product. Several key descriptions are ambiguous or insufficiently supported, making it difficult for future users to correctly interpret, evaluate, and apply the dataset.
Major comments:
(1) The observational design of the field campaign should be described more comprehensively. One of the major strengths of this dataset is the collaborative observation campaign involving 62 underwater gliders (UGs) and 2 autonomous underwater vehicles (AUVs) during seven typhoon events. However, the manuscript provides only limited information on the overall observational design of the field campaign. Important information, including the deployment layout, mission objectives, observation strategy, navigation and profiling strategies, sampling configuration, and the role of different platforms during each typhoon event, is largely absent. Without these descriptions, it is difficult for readers to understand how the observation network was organized, how the observations were acquired, and what characteristics distinguish different observation missions. As a data paper, the manuscript should provide a more complete description of the observational design and deployment strategy, together with sufficient metadata describing each observation mission, either in the main text or in the Supplementary Material.
(2) The terminology and statistical description of the dataset should be clarified and better aligned with the characteristics of the released data product. Section 2.2 introduces several statistical quantities, including valid paired samples, matching records, navigation distance, navigation duration, operational cycle, average spatial resolution, and average temporal resolution. However, many of these terms are undefined or insufficiently explained, making it difficult for readers to understand the basic observational unit of the dataset or how these statistics were derived. Furthermore, several of the reported metrics appear to describe platform operational behavior rather than the sampling characteristics of the released dataset. As a result, their relevance to the documentation and use of the dataset is unclear and may even be misleading to users. For example, the reported spatial and temporal resolutions are difficult to interpret, and it is unclear whether they represent profile spacing, platform displacement, or other quantities. The authors should critically reassess the necessity of these statistics, retain only those that directly characterize the released dataset, and replace or supplement the others with more informative and standardized metrics. This would enable future users to better understand the sampling characteristics, limitations, and appropriate use of the released dataset.
(3) Several statements regarding the observational capability of the dataset require additional justification. Some conclusions presented in Section 2 appear stronger than the supporting evidence provided. For example, the manuscript states that the observation network has sufficient spatiotemporal resolution to resolve high-frequency oceanic processes, including near-inertial oscillations, based primarily on the operational statistics of the platforms. However, these conclusions are not quantitatively justified. Likewise, the expression "characteristic timescale > 2 × 4 h" is insufficiently explained and lacks a clear physical basis. The authors are encouraged either to provide quantitative justification and supporting references for these statements or to adopt a more cautious interpretation of the observational capability of the released dataset.
(4) The quality-control procedure should be documented in greater detail. The manuscript presents a comprehensive quality-control framework consisting of thirteen QC tests following the IOOS standard, which is appropriate for glider and AUV observations. However, the current description mainly lists the names and purposes of each QC test, while the implementation details remain insufficient. Important information such as the threshold values, decision criteria, parameter sources, treatment of abnormal observations (e.g., whether they were flagged or removed), and the storage of QC flags in the released dataset is not adequately documented. Since reproducibility and transparency are fundamental requirements for a data paper, these details should be provided either in the main text or in an Appendix or Supplementary Material.
(5) The implementation of the thermal-lag correction should be described more rigorously. The thermal-lag correction algorithm represents one of the key processing steps of the released dataset. Although Equations (1)–(3) present the general formulation, several variables, parameters, and implementation details remain unclear. For example, the notation used for the flow velocity is inconsistent (i.e., V versus Vf), and the definition should be clarified. In addition, the value or determination of the Nyquist frequency (fn), the parameter fitting procedure, the estimation of the empirical coefficients (e.g., α0, αs, τ0, and τs), and the optimization process used to obtain these parameters are not sufficiently described. Providing these details would improve the transparency and reproducibility of the data processing methodology and facilitate the reuse of the proposed workflow by future users.
(6) The effectiveness of the quality-control procedure should be demonstrated more directly. The current manuscript validates the final dataset through comparisons with shipboard CTD observations, demonstrating the overall consistency of the processed data. However, this comparison alone does not directly demonstrate the effectiveness of the individual quality-control procedures, particularly the thermal-lag correction. Since thermal-lag correction represents one of the key processing steps in the data workflow, its effectiveness should be evaluated by comparing both the pre-corrected and post-corrected glider observations against the corresponding shipboard CTD measurements. In addition, representative examples, such as comparisons of temperature–salinity (T–S) diagrams, salinity profiles before and after thermal-lag correction, or examples illustrating spike removal and density inversion correction, would further improve the transparency and credibility of the data processing workflow.
(7) A quantitative summary of the quality-control results should be included. The manuscript would benefit from a concise summary describing the outcome of the quality-control procedure. For example, the numbers of raw profiles, successfully quality-controlled profiles, flagged profiles, rejected profiles, and unrecoverable profiles could be summarized in a simple table. Such information would help users assess the completeness, reliability, and applicability of the released dataset and is commonly expected in high-quality data publications.
(8) The interpretation of the case studies in Section 4 should be presented more cautiously. The case studies presented in Fig. 5 are useful for demonstrating the potential applications of the released dataset. However, the observation trajectories associated with these examples extend over relatively large spatial scales, indicating that the presented temperature and salinity variations likely reflect both temporal evolution and spatial variability. Therefore, it is difficult to attribute the observed changes solely to the local temporal response of the upper ocean to typhoon forcing. The manuscript should more clearly describe the observational context of these case studies, discuss the potential influence of background spatial variability, and avoid overinterpreting along-track observations as purely temporal evolution unless sufficient evidence is provided to support such interpretation.
Minor comments:
(1) Figure 1 is difficult to read because of the large number of overlapping trajectories and labels. Consider simplifying the visualization or enlarging individual panels.
(2) Several figures and tables are placed far from their first citation in the text. Please improve the organization following journal style.
(3) The caption of Fig. 5 contains repeated panel references ("A(b) and A(c)") for three different physical processes and should be corrected.
(4) The discussion of "outliers" in Section 2.2 is not rigorous. Points outside the boxplot whiskers are statistical outliers according to the IQR criterion, but this does not imply they are physically meaningful observations. The current interpretation may confuse statistical outliers with scientifically significant events and should be revised.