dataesr/scientific-paragraphs-categorization huggingface dataset:
The main changes with this new system are that the eddy detection is made on filtered absolute dynamic topography (ADT) maps with geometrical detection method, and several files are provided: the main atlas of eddy trajectories lasting more than 10 d, but also trajectories shorter than 10 d and lone eddies that are identified only for 1 d. The source code for detection and tracking is written in Python and has been made freely available under a GPL V3 license (https://doi. org/10. 5281/zenodo. 6333989; Delepoulle et al. , 2022) , inherited from the py-eddy-tracker (PET) of  Mason et al. (2014)  and improved after a fruitful collaboration between E. Mason (IMEDEA)  and CLS. This new atlas is available in two versions using the ADT maps for detection: with the two-satellites version, the T/P-Jason and ERS-Envisat-Saral dual sampling gives the most consistent spatial coverage over the full time series; the all-satellites version uses all available missions for the best possible sampling coverage, albeit one which varies over time.
This paper compares this new product to the current AVISO+ operational version META2. 0. Such a comparison is complex due to the size of the datasets and the diversity of variables describing the mesoscale eddies within similarly organized atlases. The originality of the methodology we developed is its capacity to match eddies from different atlases as well as comparing the global distributions of parameters. The similarity coefficient (SC) employed here is based on the ratio between the common area of two eddies and their total area. The algorithm is able to rapidly process the large global atlases over the whole altimetry period. After pairing the eddies between the two datasets, we separate (1) the common and similar eddies which have the highest SCs, (2) the common but different eddies with moderate SCs, (3) the eddies only present in one dataset and thus presenting a novelty with regards to the other dataset, and (4) the eddies with multiple matches.
Many geographically correlated eddies are formed near coasts or bathymetric changes, result from wind-orographic effects or current retroflections, and have an imprint on the mean dynamic topography (MDT). Using the ADT rather than SLA field provides more consistent eddy detection and presence in such oceanic regions. Finally, the preprocessing step of filtering, inherent to the OSU and PET methodologies, was modified to better separate the eddies from the background large-scale ocean circulation. We produced intermediate datasets to independently assess how each change impacts the atlas of eddy trajectories. This paper is organized as follows. In Sect. 2, we present first the altimetry gridded maps used to detect eddies, the differences and similarities between the OSU and PET detection and tracking algorithms, and the methodology developed to efficiently compare the different atlases.
Altimetric fields for eddy detection In continuity with the CH11 dataset, the META2. 0 product is based on the SLA maps produced by the Copernicus Climate Change Service (C3S) and distributed in the C3S Climate Data Store (https://cds. climate. copernicus. eu/, last access: 8 March 2022). These maps are built using at most two altimetric missions, with the Topex-Poseidon and Jason satellites on the same long-term ground tracks, and a second satellite mission, mainly on the ERS-Envisat-Saral-Sentinel-3A ground tracks. As the sampling and the represented scales are stable throughout time, this dataset is considered to be homogeneous in time in terms of climate signals and mesoscale content. The META3. 1exp has also been computed from the C3S dataset to ensure continuity for users, but a second production, based on the Copernicus Marine Environment Monitoring Service (CMEMS, https://marine.
For the all-satellites product, SLA is obtained as the difference between the SSH and the mean profiles (MP), the most precise MSS, available in the long-term repeat tracks (for details, see  Pujol et al. , 2016). This different strategy to remove an MSS will also have a slight impact on the eddy field. The META3. 1exp product is based on the ADT. The ADT is the sum of the SLA and the MDT, the later corresponding to the mean oceanic circulation derived from multiple satellite and in situ data. The accuracy of the MDT has greatly improved in recent years, providing robustness in the ADT fields  (Rio et al. , 2014). The input fields (SLA or ADT, C3S or CMEMS) are all global daily products, with a 1/4 • grid resolution, using the DT2018 reprocessing version  (Taburet et al. , 2019). The along-track data are filtered with a low pass Lanczos filter depending on the latitude (250 km near the Equator, down to 55 km at high latitudes) and subsampled at 14 km  (Pujol et al.
Note that the SLA fields are filtered before the eddy detection in order to remove the largescale anomaly patterns, such as the chevron-like patterns near the Equator or the El Niño-induced displacement of warm water from west to east in the Equatorial Pacific, following the procedures of  Chelton et al. (2011). The filtering step is made in OSU with a 2D Lanczos filter, with a 1000 km halfpower cutoff wavelength in latitude and longitude to take into account the latitudinal variation of the dimension of a grid pixel. For the META3. 1exp dataset, the detection algorithm is based on PET algorithm of  Mason et al. (2014) , loosely based on  Chelton et al. (2011) ,  Kurian et al. (2011), and Penven et al. (2005). Working either on SLA or ADT fields, the SSH contours are interpolated instead of using the SSH pixels.
In  Mason et al. (2014)  and  Kurian et al. (2011) , the shape error was limited to 55 %. We increase this value to 70 % to ensure that elongated eddies are detected, a case often visible in highly dynamic regions and when eddies are interacting. The META3. 1exp dataset includes both the effective contour (outermost closed contour) and the speed contour (contour with the maximum averaged speed around it). The effective radius is deduced from the best-fit circle applied to the effective contour; similarly, the speed radius is derived from the best-fit circle applied to the speed contour. We chose to decrease the amplitude threshold from 1 to 0. 4 cm, since with a minimum of five consecutive pixels within a contour, we ensure a minimum geographic imprint instead of limiting the amplitude parameter.
Tracking The META2. 0 dataset is composed of trajectories lasting more than 28 d, following the 4-week minimum eddy duration of  Chelton et al. (2011). This limitation was linked to the weekly availability of the maps, and is close to the mean temporal resolution of 34 d of the altimetry maps  (Ballarotta et al. , 2019). The tracking procedure consists of searching for an eddy at the time step t + dt (dt = 1 d) in a restricted area around the center of the eddy considered at t. After analyzing the mean displacement of eddies in the previous META1. 0exp, the restricted area was set to evolve with latitude. From high latitudes towards 25 • , the radius of the restricted area is set to 50 km; then a progressive increase is made to reach a radius of 100 km at 10 • latitude, since the eddies are larger and travel faster in the equatorial band.
2b ). This implies that the analyses made on the remaining trajectories is representative of only half of the trajectories but concerns 70 %-80 % of the individual eddies. Regardless of the tracking method, trajectories lasting more than 6 months represent only a few percent of the dataset, but the overlap method detects twice as many very long trajectories than the restricted area method. Note that the occurrence of four consecutive virtual eddies in the case of the overlap tracking is stable over different eddy lifetimes, so longer trajectories do not particularly rely on the presence of more virtual eddies. We can thus conclude that the overlap tracking is able to efficiently associate detected eddies into long trajectories, with no overuse of virtual observations for the longest trajectories.
Principle The SC compares the eddies detected in different databases having the same formalism: each eddy is saved with a rotating sense, position, amplitude, radius, time, trajectory, and contours. As for the tracking procedure, when we want to associate similar eddies within a trajectory, here we search for one eddy at a time t in one atlas (the reference) and check if there is a corresponding eddy in another atlas (the study) at the same time, using the overlap of the effective contours of those eddies. The SC is defined in Eq. (  1 ) as the ratio between the intersection of the eddies' effective areas in the reference and the studied atlases and the union of their effective areas, expressed in percent: Similaritycoefficient (%) = 100 × Area Eddy ref ∩ Area Eddy study Area Eddy ref ∪ Area Eddy study.
63; thus they are very similar eddies in location but also in physical characteristics. Eddies with no SC (0 %) are the novelty, as they were not present in any atlas. Eddies with low SCs are representative of the differences between the two atlases, as they are present in both but quite different in their location and characteristics. Note that with the SC definition used here, an eddy included in another eddy and shifted eddies are treated similarly (Fig. 3 ). Since the META2. 0 product does not provide eddy contours, in all comparisons made with this dataset we replaced contours with the circles built from the center and the radius values. After the visualization of the SCs between different atlases, we noticed that some complex oceanic regions (high latitudes where sea ice can be present, semi-enclosed seas with complex dynamics and topography) have lower SCs and more unmatched eddies than in the open ocean; thus we decided to restrict the SC applications to the major open oceanic basins.
Validation of the similarity coefficient capacity: study case of the all-satellites versus two-satellites detection To evaluate the ability of the SC to accurately identify changes, we present here the results obtained from the fol-lowing datasets. The reference atlas is comprised of the detections made with PET using the two-satellites ADT maps, and the study atlas is made with PET on the all-satellites ADT maps. Both atlases use the high-pass filter with a 700 km cutoff wavelength. The only difference between the atlases is the number of satellites used in the production of the SSH maps and the way the along-track SLA is built (see Sect. 2. 1). The SC captures well the influence of the temporal variation of the satellite constellation in the representation of mesoscale eddies (Fig. 4 ). From 1993 to 2000, the period where only two altimetric missions were providing data, 94.
3 % of the detected eddies of the all-satellites-based atlas have SCs higher than 40 % (i. e. , very similar eddies). The new eddies (SC = 0 %, brown) represent 3. 6 % of the dataset. Eddies with the lowest SCs (5 %-40 %) represent only 1. 9 % of the dataset, and the 0. 2 % multiple matched eddies are anecdotal. The small differences between eddies detected in the all-and two-satellites products are directly linked to the different processing procedures used to build the maps, with the absence of mean profiles in the two-satellite product. The SCs are also very homogeneous in time, except from January 1994 to March 1995, where only Topex/Poseidon was delivering data. After 2000, not only was the processing of the maps different, but the number of satellites changed, with up to six satellites in the constellation.
The introduction of more than two satellites is well captured by the SC method, as the rupture in the homogeneous spatiotemporal two-satellites sampling coincides with a reduction of the observations with the highest similarities between the two-satellites and all-satellites detections (only 73. 7 % of observations with SCs ≥ 40 %). Multiple matched eddies are few after 2000 (2. 2 %) but more than before. After 2000, the proportion of the lowest SCs (< 40 %) is 11. 7 %, and the new eddies represent 12. 4 % of the all-satellites dataset. The repartitioning of the SCs in time is not homogeneous but reacts to the changes in the constellation (Fig. 4 ), highlighting the sensitivity of the eddy detection to the mapping procedure. The geographic distribution of the SCs after 2000 (not shown) indicate strong similarities under the reference mission tracks, which is expected as the reference missions are shared by the all-and two-satellites products, and lower similarities and new eddies in the diamondshaped observation gaps between the reference tracks, where the additional satellites enhance the mesoscale representation.
Change in the detection algorithm: from OSU to PET The major change between the META2. 0 and META3. 1exp datasets is the change in the detection algorithm. To evaluate the impact of a detection made with the PET algorithm instead of the historical OSU one, we produced an intermediate dataset similar to META2. 0 except for the detection algorithm. We applied the PET detection algorithm to SLA maps filtered with a 1000 km half-power cutoff wavelength, and tracked the detected eddies with the restricted area method. We lowered the minimum lifetime threshold to 10 d and provided statistics only for the open ocean. The first striking result is that even with minimum lifetimes of at least 10 d, the PET-detected eddies outnumbered the OSU-detected eddies by a factor of 1. 7, with, respectively, 5936 daily eddies for PET (59.
4 million over the total period) and 3445 daily eddies for OSU (34. 9 million over the total period). Table  2  presents a repartition of the detected eddies after the pairing between the datasets. Despite the large number of new eddies detected with PET (1994 daily new eddies), there is also a higher quantity of eddies with high SCs (2318 daily eddies), meaning there are a larger number of similar eddies detected between META2. 0 and the intermediate atlas. In comparison, the low and intermediate SCs represent only 575 daily eddies. Contrary to the other analyses in this paper, PET eddies often have multiple matches (17 % of the datasets) and a large number of unmatched eddies (33 %), which needs to be investigated. To qualitatively compare the eddies detected with the two algorithms, we analyzed the repartition of the SCs' categories as a function of the effective radius (Fig.
8 ) and amplitude (not shown). To provide an idea of the distribution of the effective radius for each dataset, we represent for each SC category its percentage relative to a constant number of eddies. The vertical lines of Fig. 8  delimits the bounds of each effective radius class containing 5 % of the total eddy population (∼ 1. 7 million eddies in a class for OSU, ∼ 2. 9 million eddies for PET). Lines close to each other highlight a concentration of eddies for the corresponding effective radii. In general, the PET-detected eddy population has smaller effective radii than the OSU-detected eddy population, with a median radius of 55 km for PET and 90 km for OSU. Nevertheless, the population of OSU-detected eddies matching the PET-detected eddies with high SCs is present for all the effective radius classes (Fig.
8a , blue), except for the smallest PET-detected eddies with effective radii below 30 km. 62 % of the OSU-detected eddies are thus detected similarly with the PET algorithm, ensuring the continuity between the two detections. The new eddies, represented by the unmatched PETdetected eddies, have a quantity comparable with similarly detected eddies (33 % of the PET dataset for the unmatched eddies, 39 % for the high SCs eddies, Table  2 ). The new eddies are over-represented in the smallest effective radius classes (Fig. 8b , brown). The eddies that were not detected with OSU may arise from three OSU thresholds in the detection scheme: the absence of detection near the Equator (±2. 5 • ), the 1 cm amplitude limitation, and the absence of gaps between pixels in latitude or longitude. The PET algorithm detects eddies near the Equator, accepts amplitudes above 0.
Regardless of their size or amplitude, we are interested in the behavior of these new detected eddies from a dynamical point of view, since being part of a long trajectory is synonymous with persistence. We note that these new, unmatched PET-detected eddies are present homogeneously from 15 % to 85 % of the normalized lifetimes, with a slightly higher amount present during the early phase (0 %-15 % of the normalized lifetime) and at the very end (85 %-100 % of the normalized lifetime; not shown). Smaller eddies are expected to be particularly involved in the growth and the decay of the trajectories. This result reinforces the robustness of the new eddies, for the smallest as well as for the larger ones. Eddies with multiple matches are particularly numerous when comparing the OSU and PET detections (∼ 17 % of the datasets).
The multiple matches category is divided into three subgroups: the parents of twins, where one eddy matches with two eddies; the twins, associated with a unique parent eddy; and complex multiple matches, where more than two parents or children associations coexist. This last subgroup represents only ∼ 2 % of the datasets. It is more frequent to associate one larger eddy detected with OSU with two smaller eddies detected with PET than the reverse: 12 % of the OSU-detected eddies are parents, and 3 % are twins, whereas 1 % of PET-detected eddies are parents and 15 % are twins. This is directly linked to the possibility in the OSU detection to have more than one extremum within an eddy. This specificity was initially developed to treat the SSH irregularities or to detect eddies in close proximity, and avoid too large changes in the eddy's characteristics from one weekly map to another  (Chelton et al.
1exp, since the presence of those multiple extrema eddies are a source of discrepancies in the construction of coherent trajectories, and are poorly linked to the chlorophyll data (9). Finally, following the analyses of  Chelton et al. (2011) , Fig. 11  shows the different geographical distributions of the long-lived trajectories (more than 365 d) for META2. 0 and META3. 1exp. The number of trajectories in META3. 1exp is twice that of META2. 0, and the higher latitudes exhibit more trajectories. The main geographical differences are linked to the increased detection from using ADT maps and not SLA. In particular, the more energetic regions are highly variable in SLA, allowing for the detection of anticyclones and cyclones over mean patterns, with very different patterns in the ADT detection.
As presented in Sect. 3. 1. 1, the ADT-built trajectories are organized following the MDT patterns. The META3. 1exp has more dynamical trajectories than META2. 0 when tracking the anticyclonic Agulhas Rings and the cyclonic center of the Zapiola Eddy in the South Atlantic, the anticyclones and cyclones generated in the lee of Cabo Verde or the Hawaiian islands, the cyclones generated by the Leeuwin Current west of Australia and anticyclones generated south of Australia, the southern part of the Kuroshio and Gulf Stream veins, the Haida anticyclones in the Alaskan Gyre, and all the recurrent anticyclones of the Mediterranean Sea. Nevertheless, some very static trajectories at high latitudes are suspected to be linked to the mapping methodology and thus should be taken with caution, and warrant further validation through independent data colocation.
Code and datasets availability The detection and tracking algorithms, as well as the implemented similarity coefficient, have been freely released under a GPL V3 license (https://doi. org/10. 5281/zenodo. 6333989; Delepoulle et al. , 2022)  in the Python language. A large gallery of illustrated and documented routines to help with the data manipulation and visualization is available, and is updated when new methods are developed (https:// py-eddy-tracker. readthedocs. io/en/v3. 3. 1/; py-eddy-tracker, 2022). We produced two versions of META3. 1exp in Delayed Time. The META3. 1exp_twosat (https://doi. org/10. 24400/527896/a01-2021. 002,  Pegliasco et al. , 2021b)  presented here is based on the two-satellites maps (C3S), ensuring a stability of the represented scales in space and time. This product is recommended for long-term analyses, including climate studies.
The global attributes of each file inform the users of the algorithm version used to produce them, with their specific detection and tracking parameters. Be aware that the files are compressed, but loading them to use them will decompress the files. Since loading the contours is memory consuming, we recommend loading the files without these variables and make an extraction of the time period and geographic area of interest with the EddySubSetter application. Note that each threshold and parameter (input maps, cutoff wavelength, amplitude, shape error, number of pixels, trajectory lifetime) used to produce the META3. 1exp datasets can be adapted by the users if they found the datasets not suitable for their study. The flexibility of the detection and tracking schemes is a strong advantage of the py-eddy-tracker package.
Summary We present the new global Mesoscale Eddy Trajectory Atlas (META3. 1exp). Numerous evolutions have been made from the META2. 0 product, mainly to provide useful characteristics for users, in particular the eddy contours, which are mandatory for efficient colocation of eddies with other data. Moreover, some details of the detection and tracking algorithm were developed for weekly altimetric maps, with less accuracy than the daily products available at present, and needed to be adapted. The code has been freely released under a GPL V3 license (https://doi. org/10. 5281/zenodo. 6333989, Delepoulle et al. , 2022)  in the Python language, and the routines for data manipulation and visualization have been documented. The changes between the META2. 0 and META3. 1exp concern the input sea level field, its filtering, the detection algorithm, and the tracking scheme.
We developed a methodology to compare intermediate datasets where only one change is made at a time, and to compute an SC between matching eddies, from the ratio between their overlap area and their union. Using these diagnostics, we have quantified eddies which are similar in the intermediate datasets (SC over 40 %), those which differ (SC between 5 % and 40 %), those with multiples matches, and the new eddies present in only one dataset. The analysis was made on restricted datasets including only eddies in the open ocean involved in trajectories lasting at least 10 d, although all geographical regions and shorter eddy trajectories are included in the full dataset. Performing the detection of mesoscale eddies on the ADT instead of SLA maps allows us to better represent ocean dynamics, since the SLA detected over strong MDT features represents their variability (weakening, reinforcement, displacement) but not their absolute polarity (anticyclones or cyclones).
This eddy-detection change isolated from the others slightly reduced the number of detected eddies. Nevertheless, a large majority of eddies (77 %) are very similar between the ADT and the SLA detection in the open ocean, with no marked influence on the radius and amplitude distributions. The major impact induced by the use of ADT maps is the geographical reorganization of the trajectories, with preferential occurrence of anticyclones (cyclones) over anticyclonic (cyclonic) MDT patterns in the META3. 1exp dataset. To take into account the sharp gradients introduced in the ADT by the MDT while maintaining the mesoscale patterns in the filtered maps, the half-power cutoff wavelength was reduced from 1000 to 700 km. This change slightly increased the number of detected eddies, and the similar eddies (87 % of the dataset) had their radius and amplitude increased, but not significantly.