Lagrangian modelling of a person lost at sea during the Adriatic scirocco storm of 29 October 2018

Ličer, Matjaž; Estival, Solène; Reyes-Suarez, Catalina; Deponte, Davide; Fettich, Anja

doi:https://doi.org/10.5194/nhess-20-2335-2020

Articles | Volume 20, issue 8

https://doi.org/10.5194/nhess-20-2335-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-20-2335-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20, issue 8

Research article

|

26 Aug 2020

Research article |

| 26 Aug 2020

Lagrangian modelling of a person lost at sea during the Adriatic scirocco storm of 29 October 2018

Matjaž Ličer, Solène Estival, Catalina Reyes-Suarez, Davide Deponte, and Anja Fettich

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Final revised paper (published on 26 Aug 2020)
Supplement to the final revised paper
Preprint (discussion started on 06 Jan 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review of: Lagrangian trajectory modelling for a person lost at sea…. By Licer et al', Anonymous Referee #1, 30 Jan 2020
- AC1: 'Authors' response to Review #1', Matjaz Licer, 02 Mar 2020
  - AC3: 'Authors' response and track-changes version of the revised manuscript', Matjaz Licer, 02 Mar 2020
RC2: 'Review of Licer et al.', Anonymous Referee #2, 08 Feb 2020
- AC2: 'Authors' response to Review #2', Matjaz Licer, 02 Mar 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by editor and referees) (30 Mar 2020) by Piero Lionello

AR by Matjaz Licer on behalf of the Authors (12 Apr 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (14 Apr 2020) by Piero Lionello

RR by Anonymous Referee #2 (25 Apr 2020)

Suggestions for revision or reasons for rejection

I thank the authors their effort in improving the previous version of the manuscript. Now the storyline looks clearer to me. Regarding the conclusions, the newest thing that I see is that the authors demonstrate that ocean currents and the wind drift effect are required together to better reproduce the trajectory of a windsurfer in the area of study. As expected, Person-Powered-Vessel-2 coefficients work better in Open Drift to reproduce the drifting of a windsurfer. The regional numerical configuration of the ocean model (NEMO) in the current state does not seem yet optimum for search and rescue tasks. It is likely more accurate to design a system in which HF-radar data (where possible) and model data are combined. Despite the above shortcomings, I consider that the story of the windsurfer is interestingly introduced and results are a useful intermediate step in the development of the operational system in the region. However, I still have some questions for the authors which I expect they will answer satisfactorily.

- Stokes drift. Authors say in Ln. 170-173: “Note however that since coefficients are determined from observations, they already contain the Stokes drift contribution in the observed leeway. If one attemps to model object’s leeway using downwind and crosswind leeway coefficients based on empirical data from (Allen and Plourde, 1999), Stokes drift term must be omitted from the initial value problem (1)-(2).”

I disagree with this argument since:

1) Coefficients do not include the effect of remote (swell) waves.
2) Impact of local waves is not necessarily well incorporated in the coefficients since the fetch can be very different.

As I already mentioned in the previous review. I think that the best way to justify the irrelevance of the Stokes drift is to compute it through nearby wave buoy data or with a wave model for the day of the simulation, and show that its value is of second/third order with respect to ocean currents and wind drift.

Please, can the authors comment on this?

- Particles density. This new approach is more satisfactory than the use of trajectories. And it is closer to the probabilistic standpoint that I suggested. However, I have some comments about this approach:

1) As I understand the units are the number of particles per square box of 150 m x 150 m. However, the colorbar range selected by the authors saturates at 40 particles. This point is critical in Fig. 4, where particles show a bi-modal distribution. The saturation of the colorbar prevents me to judge how well the backtracking performed with HF-radar data approaches to the tentative path suggested by the windsurfer in comparison to the right cloud of particles. One option is to change the colorbar range in a way that the maximum value in the colorbar is closer to the maximum number of particles per cell.

2) I still think that the approach I suggested in the first review is more optimal for search and rescue tasks since it prioritize more objectively research areas based on accumulated probability and it is really easy to implement.

3) An interesting exercise to quantify how good (or bad) is the model would be to compare the backtracking distribution of points using both HF-radar and model data. Indeed from this comparison authors can get an indirect measurement of the delay of model data in terms of spatial distance.

Please, can the authors comment on this?

- Other comments.

Ln 28. Check format of reference.
Ln 28-29. Sentence too complicated.
Ln 38. Check the format of units.
Ln 55. Below.
Ln 91. 1 min intervals
Ln 167. Check format of reference.
Ln 173. Check format of reference.
Ln. 184. Check format of reference.
Ln 187. Check format of reference.
Ln 219-220. “During the night, when survivor reported feeling maximum distress, the
back-propagation estimate of trajectory is located a mile or two to the east of his reconstruction.”
It depends on which cloud of particles you look at and how significant they are. Refer to my comment on particle density.
Ln 229. Check dot.
Ln 243. Check format of reference.
Ln 265. Check format of units.
Ln 301. To me the distribution of particles is sparse. Difficult to see if the number of closer particles is residual or significant. Suggest to revise colorbar range.
Fig. 1. Different sizes of circles in Fig. 1 representing uncertainties in the windsurfer location still look arbitrary to me.
Fig. 10. Why the colors are merged sometimes? Merged colors do not appear in the legend. This should be avoided.

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RR by Anonymous Referee #1 (02 May 2020)

Suggestions for revision or reasons for rejection

I found the paper significantly improved, and I now recommend its publication after some minor revisions, listed below.
I do not need to see the revision again:

- Please clarify from the beginning (e.g. in the Introduction, lines 90-92) and then consistently in the text (Section 4) the reason why HF radar data are used only for backtrackinng and not for forward calculation, i.e.because the initial point is outside from the radar coverage and the following points are not known

- In Section 2.2 please introduce the concept of GDOP for HF radar data, as the reason for the persistent total velocity data gap in the Gulf shown in Fig.3 and 6.

- Section 4.1, I would change the title to something softer, such as “Comparison between survivor drift trajectory and backtracking estimates from HF radar”

- In Section 4.1, please specify how are the 50.000 random particles initialized

- In Section 4.2 (now mistakingly indicated as 4.1, see below) lines 325-330, the comparison between NEMO and HF radar should be better quantified. What does the bias range mean? It would be better to show pdfs of values averaged over a certain interval (hours?).

- Lines 393-395, please be careful that the backtracking with HF radar is performed as a passive particle (correct?)

- Fig.10 the caption and its reference in the text are not very clear. Please introduce A1, A2,,, in the caption and in the text

- In Figs 8-9, why is the survivor trajectory not shown in the lower left panels (10 UTC)?

- In section 5 (Lines 491-497) please avoid referring to “drifters”. It is very confusing because you did not use the word drifter until then. Please refer to something like “drift parameterization cases”.

- Regarding the omparison of NEMO outputs and HF radar. I think NEMO not only tends to underestimate the currents, but also the direction and circulation patterns are quite different. This should be clearer in the Abstract (line 18-19) and in the text.

- Please check for typos or misprints. Some examples are:
“below” is misspelled (line 69),
Numbering of Section 4 is wrong (4.1 is repeated twice),
in Section 4 there are several phrases left from the previous version referring to 2 particle tracking models (e.g. Line 309, 344..?)

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ED: Publish subject to minor revisions (review by editor) (15 May 2020) by Piero Lionello

AR by Matjaz Licer on behalf of the Authors (21 May 2020) Author's response Manuscript

ED: Publish subject to minor revisions (review by editor) (20 Jun 2020) by Piero Lionello

Dear Authors.
Thanks for having addressed the comments of the reviewers in your reply.

I am asking you a final minor revision, considering the two following points:

a) Considering the Stokes’ drif (comment 1 of reviewer #2)

Line 178 I suggest adding “of the local wind sea” so that the sentence become “They already contain the Stokes drift contribution of the local wind sea in the observed leeway”

Lines 178-180 I suggest to replace
“If one attempts to model object’s leeway using downwind and crosswind leeway coefﬁcients based on empirical data from Allen and Plourde (1999), Stokes drift term must be omitted from the initial value problem (1)-(2), as explained in Breivik and Allen (2008).”
With
“In our attempt to model object’s leeway using downwind and crosswind leeway coefﬁcients based on empirical data from Allen and Plourde (1999), we have omitted the Stokes drift term from the initial value problem (1)-(2), as explained in Breivik and Allen (2008).”

b) Considering the accuracy of the NEMO fields and the comparison with HF observations (omment 3) of reviewer #2)

I appreciate your answer to comment 3) of reviewer # 2. I agree on your decision to keep the paper focused. Anyway, I think that you should expand a bit the discussion of the role of NEMO model errors and to which extent these errors might confuse your analysis of the tracking model. In my understanding, the two new experiments that you have performed show that errors on NEMO were large in the last 6 hours, but not previously. Please confirm this. I suggest that you add some text in section 4.1, mentioning these two experiments, the results of the back and forward tracking simulations and the conclusion that you get from their analysis. Consider adding a pdf with the maps as supplementary material (that can be cited in the text).

Line 13 I suggest replacing “during the period of the drift” with “during the last 6 hours of the drift”. This is my understanding of your answer to reviewer #2 comment 3

Line 14-15 the importance for what? My suggestion “This indicates the importance of topographically constrained ocean currents in semi-enclosed basins even in seemingly wind-dominated situations for determining the trajectory of a person lost at sea.”

Looking forward to receiving the final version of your manuscript
Best regards
Piero Lionello

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AR by Matjaz Licer on behalf of the Authors (13 Jul 2020) Author's response Manuscript

ED: Publish as is (19 Jul 2020) by Piero Lionello

AR by Matjaz Licer on behalf of the Authors (20 Jul 2020) Manuscript

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Short summary

In 2018 windsurfer’s mast broke about 1 km offshore during a scirocco storm in the northern Adriatic. He was drifting in severe conditions until he eventually beached alive and well in Sistiana (Italy) 24 h later. We conducted an interview with the survivor to reconstruct his trajectory. We simulate his trajectory in several ways and estimate the optimal search-and-rescue area for a civil rescue response. Properly calibrated virtual drifter properties are key to reliable rescue area forecasting.