the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Oct 2024
Connecting Ocean Observations with Prediction
Abstract. Ocean prediction relies on the integration between models, satellite and in-situ observations through data assimilation techniques. Satellites offer nowadays high-resolution observations of essential ocean variables at the surface, widely adopted in combination with precise but sparse in-situ measurements that, from the surface to the deep ocean, can constrain large scale variability in models. Moreover, observations are a valuable source of information for validating and assessing model products, for improving them and for developing the next generation of machine learning algorithms aimed at enhancing the accuracy and scope of ocean forecasts. The authors discuss the role of observations in operational ocean forecasting systems, describing the state-of-the-art of satellite and in-situ observing networks and defining the paths for addressing multi-scale monitoring and forecasting.
- Preprint
(774 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
CC1: 'Comment on sp-2024-36', Peter Oke, 18 Oct 2024
Review of “Connecting Ocean Observations with Prediction”, by Le Traon et al.
This paper provides a concise description of the different observing platforms that are used for ocean forecasting. It is very light on any detail – in some places, it’s little more than a list of platforms – but perhaps that’s the intention. There is nothing new in this paper. But, for a reader that is not part of the ocean forecasting community, this paper could be an informative introduction. Some detailed comments are offered below that need to be addressed. Aside from these details, this paper is suitable for publication.
Detail comments
L28: “These dependencies depend on ocean dynamics and the scales of motion” is circular and should be revisited.
Figure 1 is not good quality. I like what the authors tried to do. But it’s also obviously a screen shot, with spelling mistakes (based on a dictionary that isn’t english) underlined.
L104: “More recent impact studies …”. The authors then cite a paper from 2016, which is not very recent.
L105: OneArgo … consider citing Roemmich et al. (2019; Frontiers)
L122: I don’t think “Benkiran et al., 2022” is an appropriate reference to credit “the development of operational swath altimetry”. Consider citing Morrow et al. (2019; Frontiers)
Citation: https://doi.org/10.5194/sp-2024-36-CC1 -
AC1: 'Reply on CC1', Pierre-Yves Le Traon, 25 Oct 2024
Thanks for the review. We agree with all comments from the reviewer. The paper is indeed "light" as this is the format specification.
L28. "These dependencies depend on ocean dynamics and the scales of motion" replaced by "These dependencies vary according to ocean dynamics"
Figure 1 : updated figure included
L104: More recent removed.
L105. Reference Roemmich et al. added
L122. Reference Morrow et al. added. We kept Benkiran et al as the study dealt with a simulation study on operational swath altimetry
Citation: https://doi.org/10.5194/sp-2024-36-AC1 -
RC2: 'Comment on SP-2024-36', Peter Oke, 03 Dec 2024
Review of “Connecting Ocean Observations with Prediction”, by Le Traon et al.
This paper provides a concise description of the different observing platforms that are used for ocean forecasting. It is very light on any detail – in some places, it’s little more than a list of platforms – but perhaps that’s the intention. There is nothing new in this paper. But, for a reader that is not part of the ocean forecasting community, this paper could be an informative introduction. Some detailed comments are offered below that need to be addressed. Aside from these details, this paper is suitable for publication.
Detail comments
L28: “These dependencies depend on ocean dynamics and the scales of motion” is circular and should be revisited.
Figure 1 is not good quality. I like what the authors tried to do. But it’s also obviously a screen shot, with spelling mistakes (based on a dictionary that isn’t english) underlined.
L104: “More recent impact studies …”. The authors then cite a paper from 2016, which is not very recent.
L105: OneArgo … consider citing Roemmich et al. (2019; Frontiers)
L122: I don’t think “Benkiran et al., 2022” is an appropriate reference to credit “the development of operational swath altimetry”. Consider citing Morrow et al. (2019; Frontiers)
Citation: https://doi.org/10.5194/sp-2024-36-RC2 -
AC4: 'Reply on RC2', Pierre-Yves Le Traon, 03 Dec 2024
I assume this was sent by error as this is the same comment as previously received
Citation: https://doi.org/10.5194/sp-2024-36-AC4
-
AC4: 'Reply on RC2', Pierre-Yves Le Traon, 03 Dec 2024
-
AC1: 'Reply on CC1', Pierre-Yves Le Traon, 25 Oct 2024
-
RC1: 'Comment on sp-2024-36', Anonymous Referee #1, 23 Nov 2024
This manuscript presents the current capacities and limitations of ocean observation platforms integrated into the ocean forecasting pipeline. It reads like a generalized short review demonstrating the importance and need for high-quality ocean data for modeling assimilation and validation purposes and machine learning forecasting. My comments are the following:
- I agree with the first reviewer, this work distills current knowledge without entering in depth in each topic discussed.
- In Section 2 on satellite observations, please add in brackets a few examples of satellites with infrared, ocean color, and microwave sensors widely used in oceanographic research. Also, some geostationary and polar-orbiting satellites are extensively used in oceanography, and their data have been assimilated or used for validation in forecasting modeling studies.
- Line 96: A statement on data quality control and QA/QC procedures before data assimilation in models should be added here.
- The role of low-cost sensors in in-situ ocean observation systems, especially at coastal seas, is not discussed.
- Novel sensors, like microplastic, oil spills, dissolved gases, etc., are not presented.
- The role of Citizen Science in marine data collection is also not discussed.
- In future challenges the need for data standardization for interoperability purposes should also be discussed.
Citation: https://doi.org/10.5194/sp-2024-36-RC1 -
AC3: 'Reply on RC1', Pierre-Yves Le Traon, 25 Nov 2024
dear reviewer,
thanks for your comments. We agree with most of them and have updated the ms accordingly. See below:
- I agree with the first reviewer, this work distills current knowledge without entering in depth in each topic discussed => see answer to reviewer 1. We followed the framework asked for this very concise paper
- In Section 2 on satellite observations, please add in brackets a few examples of satellites with infrared, ocean color, and microwave sensors widely used in oceanographic research. Also, some geostationary and polar-orbiting satellites are extensively used in oceanography, and their data have been assimilated or used for validation in forecasting modeling studies => we added references to instrument and satellite missions (GEOS, MTG, VIIRS, S3/SLSTR).
- Line 96: A statement on data quality control and QA/QC procedures before data assimilation in models should be added here => the point on data quality control was already mentioned but this is better emphasized now.
- The role of low-cost sensors in in-situ ocean observation systems, especially at coastal seas, is not discussed => a sentence was added incl. the role of citizen science
- Novel sensors, like microplastic, oil spills, dissolved gases, etc., are not presented. => the ms is focused on ocean prediction core systems and not downstream applications.
- The role of Citizen Science in marine data collection is also not discussed. => see remark above
- In future challenges the need for data standardization for interoperability purposes should also be discussed => done
Citation: https://doi.org/10.5194/sp-2024-36-AC3
-
AC2: 'Reply on RC1', Pierre-Yves Le Traon, 25 Nov 2024
dear reviewer,
thanks for your comments. We agree with most of them and have updated the ms accordingly. See below:
- I agree with the first reviewer, this work distills current knowledge without entering in depth in each topic discussed => see answer to reviewer 1. We followed the framework asked for this very concise paper
- In Section 2 on satellite observations, please add in brackets a few examples of satellites with infrared, ocean color, and microwave sensors widely used in oceanographic research. Also, some geostationary and polar-orbiting satellites are extensively used in oceanography, and their data have been assimilated or used for validation in forecasting modeling studies => we added references to instrument and satellite missions (GEOS, MTG, VIIRS, S3/SLSTR).
- Line 96: A statement on data quality control and QA/QC procedures before data assimilation in models should be added here => the point on data quality control was already mentioned but this is better emphasized now.
- The role of low-cost sensors in in-situ ocean observation systems, especially at coastal seas, is not discussed => a sentence was added incl. the role of citizen science
- Novel sensors, like microplastic, oil spills, dissolved gases, etc., are not presented. => the ms is focused on ocean prediction core systems and not downstream applications.
- The role of Citizen Science in marine data collection is also not discussed. => see remark above
- In future challenges the need for data standardization for interoperability purposes should also be discussed => done
Citation: https://doi.org/10.5194/sp-2024-36-AC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 131 | 66 | 14 | 211 | 1 | 2 |
- HTML: 131
- PDF: 66
- XML: 14
- Total: 211
- BibTeX: 1
- EndNote: 2
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1