the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Mediterranean drifters dataset: 1998–2022
Antonio Bussani
Milena Menna
Andrea Satta
Roberto Sorgente
Andrea Cucco
Riccardo Gerin
Abstract. Over a hundred of experiments were realised between 1998 and 2022 in the Mediterranean Sea using surface Lagrangian drifters, at coastal and offshore level. Raw data were initially unified and pre-processed manually by eliminating spikes and wrong positions or date/time information. The integrity of the received data packages was checked, and incomplete ones were discarded. Deployment information was retrieved from an initial excel database and campaign notes for each drifter and integrated into the PostgreSQL database, realised and maintained by the National Institute of Oceanography and Applied Geophysics (OGS) in Trieste (IT). This database also collects a variety of metadata about the drifter model, project, owner and operator. Subsequently data were processed using standard procedures of editing and quality control developed for the OGS Mediterranean drifter dataset to remove spikes generated by malfunctioning of the sensors and obtain files with common characteristics. Drifter data and plots of each track were also visually checked to remove any point not identified by the automatic procedure and clearly erroneous. Drifters’ trajectories were split into two or more segments that have been considered as different deployments, in case of specific drifters' behaviours. Data were interpolated at defined time intervals to temporarily unify tracks. From the original 138 experiments, a dataset of 204 tracks was obtained, available from the public open-access repository in SEANOE (SEA scieNtific Open data Edition) at https://doi.org/10.17882/90537 (Ribotti et al., 2022).
Alberto Ribotti et al.
Status: open (extended)
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RC1: 'Comment on essd-2022-344', Anonymous Referee #1, 16 Dec 2022
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This article is not fully comprehensible because of the English grammar, and as such, I deem it inappropriate for publication in its present stage. I would recommend a thorough editing and rewriting in order to improve the English.
I do not find the description of the processing methods to be extensive enough to understand the dataset. As an example, a kriging method is stated to be used to produce the estimated positions but nothing is said of the underlying structure functions that need to be first estimated to apply this method. Another example is the quality control procedure of despiking: once again, no detail is given for this method (type of filter, threshold etc.).
The dataset website (https://doi.org/10.17882/90537) indicates that 366 trajectories (tracks) are available yet the article mentions 204? After downloading all the files, the number of track appears to be indeed 204, one per file. These files do not follow a traditional data format: every single variable in these files (u,v, Lat, Lon, etc.) has its own dimension with the name of the variable. In other words, the variable "u" has dimension "u", which is odd. This does not suggest that these variables are contemporaneous or constitute time series along a common dimension ("obs" as an example). Moreover, because some variables exhibit missing values, a common software like Panoply is unable to plot time series for which missing values are present (because the dimension for that variable has missing values!). My suggestion is to reformat and recreate these files so that the variables have a common dimension (such as "obs"). There are template available for trajectory files, see as an example the one from NOAA NCEI (https://www.ncei.noaa.gov/netcdf-templates).
Moreover, some files have only two data points for each variables, and in the particular example of aarib_LCA113.nc, no valid value at all. What is the point of this set of data? This shows inadequate curation or automatic processing and editing of the data.Citation: https://doi.org/10.5194/essd-2022-344-RC1 -
CC1: 'Comment on essd-2022-344', Adam Gauci, 10 Jan 2023
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This work presents various drifter experiments that were carried out between 1998 and 2022. In Section 2 of the paper, the authors put forward the characteristics and specifications of the four types of drifters used. Very good and relevant comparisons between the instrument dimensions, battery duration, data transmission type, and data formats, are made. Clearly, the authors have extensive knowledge and experience in using and deploying such equipment. The points mentioned are useful to anyone that plans to carry out similar experiments in the same region or elsewhere. In Section 3, details about the data quality control procedures are provided. This has also been carried out meticulously and follows the current state-of-the-art methods. Details of how the data is saved and what information is stored are also mentioned. The 138 trajectories that are discussed are also made freely available. This dataset is useful to several users working with data assimilation or HF radar validation. Well done to the authors. Excellent work!
Citation: https://doi.org/10.5194/essd-2022-344-CC1
Alberto Ribotti et al.
Data sets
Mediterranean Lagrangian drifters data from 1998 to 2022 Ribotti Alberto, Bussani Antonio, Menna Milena, Satta Andrea, Sorgente Roberto, Cucco Andrea, Gerin Riccardo https://doi.org/10.17882/90537
Alberto Ribotti et al.
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