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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RC2: 'Comment on essd-2026-300', Anonymous Referee #2, 16 Jul 2026
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Review of preprint “High-resolution dataset of 2024 typhoons in the northern South China Sea captured by acollaborative network of underwater gliders and autonomous underwater vehicles”, Qi, et al., https://doi.org/10.5194/essd-2026-300
In general, the overall quality of the article is good. The novelty of the data set is the extremely high number of observations in the vicinity of tropical cyclones. This is historically unequalled and does represent a commendable commitment to deployment of a TC observing array, making the data set potentially quite valuable to researchers.
The deployments themselves and their relationships to TCs occurring during the deployment period are well documented.
The data are available and complete as described in the manuscript.
The authors rightfully call attention to the fact that “ Oceanographic parameters are collected at 6-secondintervals via a non-pumped CTD sensor” . This forces them to do some complicated processing with assumptions to remove salinity spikes due to C-T response time differences.
Salinity spikes are still evident in the profiles (attached, all MALIKSI glider salinity data, along with a zoomed view) as well as some possibly spurious high frequency vertical variability caused by processing.
They may have done the best they could under the circumstances, but I would suggest putting the original Conductivity data in the data files to allow users to try to do better if they desire.
Additionally, first data point in downcasts is usually at 10-15 m, with reduced vertical resolution in the upper 30-40 m. This is possibly an artifact of either the sampling rate or filtering, but should be mentioned in the text.
In spite of the higher resolution issues noted above, the data seem to be appropriate for the example analyses in the article (bulk mixed layer changes, near-inertial oscillation response). The comparison with high-resolution CTD profiles provides valid error estimates and supports the use of the data for this type of analysis.
The metadata in the original data files are extremely sparse (20 parameter global attributes, compared to typical US IOOS DAC 60+) and there are NO metadata on instruments, which normally include type, serial number, calibration information, etc.
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RC3: 'Comment on essd-2026-300', Anonymous Referee #3, 22 Jul 2026
reply
Review of “High-resolution dataset of 2024 typhoons in the northern South China Sea captured by a collaborative network of underwater gliders and autonomous underwater vehicles”
General comments
In this manuscript, the authors present observation data collected in the South China Sea by underwater gliders and Autonomous Underwater Vehicles (AUVs) during the 2024 typhoon season. After providing statistics about the platform deployments and the encounters with Tropical Cyclones, the authors present some study cases to illustrate the ability of the array to describe a variety of complex ocean processes associated with the passage of several storms in the region. In particular, they show different ocean responses due to precipitation or to the presence of ocean eddies.
The manuscript is generally well written and concise, and it presents a very interesting dataset, which supports the study of air-sea interactions under Typhoons. I think the manuscript can be improved in several ways. In particular, some figures are too small and the reader cannot see or read the necessary information. Moreover, certain aspects of the data analysis need to be better presented. As a result, I recommend Major Revision in order for the authors to clarify those aspects, before the manuscript can be published. Please see the main comments and suggestions below for more details.
Specific comments
First, some important figures are too small and prevent the reader from seeing the information. In particular, Figure 1 is way too small. It is impossible to see anything on this figure. Figures 5, A1 and A2 are also too small. The letters on these figures are too small; it is not possible to read the text.
In the presentation of the dataset, some of the information is not clear. What is a “valid pair sample”, also referred to as a “matching record”, and how is it measured, if not in number of profiles? What exactly do the Equip. Active Days represent? In Table 1 it is written “(2 days pre- and post-TY passage)” but some of the values seem large (for example MAN-YI, 15 days). Does the “TY passage” mean the total period over which a storm was over the South China Sea? This needs to be clarified.
I think the analysis section (section 4) needs to be better supported or explained:
- In the case of Typhoon Maliksi, 136 mm of precipitation in 24 hours is presented like an extreme level that leads to enhanced stratification, but such level of precipitation is relatively common under Tropical Cyclones. Can the pre-existing stratification or the moderate wind intensity at that stage (the storm was still a Tropical Depression) explain the absence of mixing in this case?
- In the case of Typhoon Soulik, I do not see an upwelling. To the contrary, the isotherms and isohalines seem to extend deeper after the passage of the storm than before. Also, I do not understand how the presence of a warm eddy would lead to an initial decrease in salinity in the near-surface salinity, followed by an increase. The authors need to better explain their reasoning.
One aspect that is not mentioned by the authors is that the platforms (glider or AUV) keep moving during the passage of a Typhoon, rather than staying stationary. It is thus possible that some of the signals that is captured might be due to the platforms moving into an ocean feature (eddy, front) at the same time that a storm is passing. I think this aspect needs to be discussed in the manuscript.
Suggestions
- l. 34: Mixing should also be mentioned here.
- l. 19 and 56: If the authors talk about global aspects, they should use the term Tropical Cyclones rather than Typhoons, which is the term for Western Pacific Tropical Cyclones.
- l. 91: Thanks to, rather than Attributed to.
- l. 91-123: In that paragraph, the authors should also mention the community effort in the Caribbean and Atlantic region to deploy gliders for hurricane applications that has been taking place for years every hurricane season.
- l. 133, l. 137: What is the difference between “temperature and salinity profile data points” and ““temperature and salinity profiles”?
- l. 135-136: What is a “valid pair sample” and how is it measured, if not in number of profiles? (Please see my main comments.)
- l. 152: This line mentions 8 typhoon events, whereas l. 133 and Table 1 both mention 7. It looks like 1 typhoon, Toraji, was not sampled, but the presentation is confusing.
- l. 162-163: The threshold values of (3 h, 100 km) and (3 h, 150 km) appear quite arbitrary. The authors should mention the justification for these values.
- l. 164-167: What exactly is the maximum navigation distance? What do the authors mean by “forward deviation”?
- l. 167: What exactly do the Equip. Active Days represent? (Please see my main comments.)
- l. 151-169: Overall, that paragraph is not very clear and needs to be improved.
- l. 207-208: Communication disruptions can also lead to estimated long distances between detected surfacing locations.
- Table 1: Equip. Active Days needs to be better defined, as well as the Number of matches.
- l. 248: Table 1, not Table 2
- l. 257: I suggest “thorough” rather than “complete”.
- l. 267, 268: I suggest UGs (plural) rather than UG (singular).
- l. 487, 489: Shouldn’t it be B and C?
- Figure A3: The letters in the figure and in the captions are not consistent.
Citation: https://doi.org/10.5194/essd-2026-300-RC3
Data sets
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.