Articles | Volume 28, issue 2
https://doi.org/10.5194/npg-28-181-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-28-181-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2021
Research article |
| 20 Apr 2021
| 20 Apr 2021
Extracting statistically significant eddy signals from large Lagrangian datasets using wavelet ridge analysis, with application to the Gulf of Mexico
Jonathan M. Lilly
CORRESPONDING AUTHOR
Theiss Research, La Jolla, California, USA
Paula Pérez-Brunius
Departamento de Oceanografía, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Mexico
Related authors
Jonathan M. Lilly and Paula Pérez-Brunius
Earth Syst. Sci. Data, 13, 645–669, https://doi.org/10.5194/essd-13-645-2021, https://doi.org/10.5194/essd-13-645-2021, 2021
Short summary
Short summary
A large set of historical surface drifter data from the Gulf of Mexico are processed and assimilated into a spatially and temporally gridded dataset called GulfFlow, forming a significant resource for studying the circulation and variability in this important region. The uniformly processed historical drifter data interpolated to hourly resolution from all publicly available sources are also distributed in a separate product. A greatly improved map of the mean circulation is presented.
Jonathan M. Lilly and Paula Pérez-Brunius
Earth Syst. Sci. Data, 13, 645–669, https://doi.org/10.5194/essd-13-645-2021, https://doi.org/10.5194/essd-13-645-2021, 2021
Short summary
Short summary
A large set of historical surface drifter data from the Gulf of Mexico are processed and assimilated into a spatially and temporally gridded dataset called GulfFlow, forming a significant resource for studying the circulation and variability in this important region. The uniformly processed historical drifter data interpolated to hourly resolution from all publicly available sources are also distributed in a separate product. A greatly improved map of the mean circulation is presented.
Orens Pasqueron de Fommervault, Paula Perez-Brunius, Pierre Damien, Victor F. Camacho-Ibar, and Julio Sheinbaum
Biogeosciences, 14, 5647–5662, https://doi.org/10.5194/bg-14-5647-2017, https://doi.org/10.5194/bg-14-5647-2017, 2017
Short summary
Short summary
The Gulf of Mexico is known to be characterized by a low abundance in phytoplankton. However, observations across the basin are scarce and prevent an accurate description of the mechanism controlling its distribution and dynamics. The recent deployment of autonomous profiling floats equipped with bio-optical sensors was a great opportunity to explore the phytoplankton in the Gulf Of Mexico. This study presents the analysis of this novel dataset.
Related subject area
Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Big data and artificial intelligence
Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
Characterisation of Dansgaard-Oeschger events in palaeoclimate time series using the Matrix Profile
The sampling method for optimal precursors of El Niño–Southern Oscillation events
A comparison of two causal methods in the context of climate analyses
A two-fold deep-learning strategy to correct and downscale winds over mountains
Downscaling of surface wind forecasts using convolutional neural networks
Representation learning with unconditional denoising diffusion models for dynamical systems
Data-driven methods to estimate the committor function in conceptual ocean models
Exploring meteorological droughts' spatial patterns across Europe through complex network theory
Integrated hydrodynamic and machine learning models for compound flooding prediction in a data-scarce estuarine delta
Predicting sea surface temperatures with coupled reservoir computers
Using neural networks to improve simulations in the gray zone
The blessing of dimensionality for the analysis of climate data
Producing realistic climate data with generative adversarial networks
Identification of droughts and heatwaves in Germany with regional climate networks
Ensemble-based statistical interpolation with Gaussian anamorphosis for the spatial analysis of precipitation
Applications of matrix factorization methods to climate data
Detecting dynamical anomalies in time series from different palaeoclimate proxy archives using windowed recurrence network analysis
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
Nonlin. Processes Geophys., 31, 247–257, https://doi.org/10.5194/npg-31-247-2024, https://doi.org/10.5194/npg-31-247-2024, 2024
Short summary
Short summary
During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.
Susana Barbosa, Maria Eduarda Silva, and Denis-Didier Rousseau
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2024-13, https://doi.org/10.5194/npg-2024-13, 2024
Revised manuscript accepted for NPG
Short summary
Short summary
The characterisation of abrupt transitions in palaeoclimate records allows the understanding of millennial climate variability and of potential tipping points in the context of current climate change. In our study an algorithmic method, the matrix profile, is employed to characterise abrupt warmings designated as Dansgaard-Oeschger (DO) events and to identify the most similar transitions in the palaeoclimate time series.
Bin Shi and Junjie Ma
Nonlin. Processes Geophys., 31, 165–174, https://doi.org/10.5194/npg-31-165-2024, https://doi.org/10.5194/npg-31-165-2024, 2024
Short summary
Short summary
Different from traditional deterministic optimization algorithms, we implement the sampling method to compute the conditional nonlinear optimal perturbations (CNOPs) in the realistic and predictive coupled ocean–atmosphere model, which reduces the first-order information to the zeroth-order one, avoiding the high-cost computation of the gradient. The numerical performance highlights the importance of stochastic optimization algorithms to compute CNOPs and capture initial optimal precursors.
David Docquier, Giorgia Di Capua, Reik V. Donner, Carlos A. L. Pires, Amélie Simon, and Stéphane Vannitsem
Nonlin. Processes Geophys., 31, 115–136, https://doi.org/10.5194/npg-31-115-2024, https://doi.org/10.5194/npg-31-115-2024, 2024
Short summary
Short summary
Identifying causes of specific processes is crucial in order to better understand our climate system. Traditionally, correlation analyses have been used to identify cause–effect relationships in climate studies. However, correlation does not imply causation, which justifies the need to use causal methods. We compare two independent causal methods and show that these are superior to classical correlation analyses. We also find some interesting differences between the two methods.
Louis Le Toumelin, Isabelle Gouttevin, Clovis Galiez, and Nora Helbig
Nonlin. Processes Geophys., 31, 75–97, https://doi.org/10.5194/npg-31-75-2024, https://doi.org/10.5194/npg-31-75-2024, 2024
Short summary
Short summary
Forecasting wind fields over mountains is of high importance for several applications and particularly for understanding how wind erodes and disperses snow. Forecasters rely on operational wind forecasts over mountains, which are currently only available on kilometric scales. These forecasts can also be affected by errors of diverse origins. Here we introduce a new strategy based on artificial intelligence to correct large-scale wind forecasts in mountains and increase their spatial resolution.
Florian Dupuy, Pierre Durand, and Thierry Hedde
Nonlin. Processes Geophys., 30, 553–570, https://doi.org/10.5194/npg-30-553-2023, https://doi.org/10.5194/npg-30-553-2023, 2023
Short summary
Short summary
Forecasting near-surface winds over complex terrain requires high-resolution numerical weather prediction models, which drastically increase the duration of simulations and hinder them in running on a routine basis. A faster alternative is statistical downscaling. We explore different ways of calculating near-surface wind speed and direction using artificial intelligence algorithms based on various convolutional neural networks in order to find the best approach for wind downscaling.
Tobias Sebastian Finn, Lucas Disson, Alban Farchi, Marc Bocquet, and Charlotte Durand
EGUsphere, https://doi.org/10.5194/egusphere-2023-2261, https://doi.org/10.5194/egusphere-2023-2261, 2023
Short summary
Short summary
We train neural networks as denoising diffusion models for state generation in the Lorenz 1963 system and demonstrate that they learn an internal representation of the system. We make use of this learned representation and the pre-trained model in two downstream tasks: surrogate modelling and ensemble generation. For both tasks, the diffusion model can outperform other more common approaches. Thus, we see a potential of representation learning with diffusion models for dynamical systems.
Valérian Jacques-Dumas, René M. van Westen, Freddy Bouchet, and Henk A. Dijkstra
Nonlin. Processes Geophys., 30, 195–216, https://doi.org/10.5194/npg-30-195-2023, https://doi.org/10.5194/npg-30-195-2023, 2023
Short summary
Short summary
Computing the probability of occurrence of rare events is relevant because of their high impact but also difficult due to the lack of data. Rare event algorithms are designed for that task, but their efficiency relies on a score function that is hard to compute. We compare four methods that compute this function from data and measure their performance to assess which one would be best suited to be applied to a climate model. We find neural networks to be most robust and flexible for this task.
Domenico Giaquinto, Warner Marzocchi, and Jürgen Kurths
Nonlin. Processes Geophys., 30, 167–181, https://doi.org/10.5194/npg-30-167-2023, https://doi.org/10.5194/npg-30-167-2023, 2023
Short summary
Short summary
Despite being among the most severe climate extremes, it is still challenging to assess droughts’ features for specific regions. In this paper we study meteorological droughts in Europe using concepts derived from climate network theory. By exploring the synchronization in droughts occurrences across the continent we unveil regional clusters which are individually examined to identify droughts’ geographical propagation and source–sink systems, which could potentially support droughts’ forecast.
Joko Sampurno, Valentin Vallaeys, Randy Ardianto, and Emmanuel Hanert
Nonlin. Processes Geophys., 29, 301–315, https://doi.org/10.5194/npg-29-301-2022, https://doi.org/10.5194/npg-29-301-2022, 2022
Short summary
Short summary
In this study, we successfully built and evaluated machine learning models for predicting water level dynamics as a proxy for compound flooding hazards in a data-scarce delta. The issues that we tackled here are data scarcity and low computational resources for building flood forecasting models. The proposed approach is suitable for use by local water management agencies in developing countries that encounter these issues.
Benjamin Walleshauser and Erik Bollt
Nonlin. Processes Geophys., 29, 255–264, https://doi.org/10.5194/npg-29-255-2022, https://doi.org/10.5194/npg-29-255-2022, 2022
Short summary
Short summary
As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.
Raphael Kriegmair, Yvonne Ruckstuhl, Stephan Rasp, and George Craig
Nonlin. Processes Geophys., 29, 171–181, https://doi.org/10.5194/npg-29-171-2022, https://doi.org/10.5194/npg-29-171-2022, 2022
Short summary
Short summary
Our regional numerical weather prediction models run at kilometer-scale resolutions. Processes that occur at smaller scales not yet resolved contribute significantly to the atmospheric flow. We use a neural network (NN) to represent the unresolved part of physical process such as cumulus clouds. We test this approach on a simplified, yet representative, 1D model and find that the NN corrections vastly improve the model forecast up to a couple of days.
Bo Christiansen
Nonlin. Processes Geophys., 28, 409–422, https://doi.org/10.5194/npg-28-409-2021, https://doi.org/10.5194/npg-28-409-2021, 2021
Short summary
Short summary
In geophysics we often need to analyse large samples of high-dimensional fields. Fortunately but counterintuitively, such high dimensionality can be a blessing, and we demonstrate how this allows simple analytical results to be derived. These results include estimates of correlations between sample members and how the sample mean depends on the sample size. We show that the properties of high dimensionality with success can be applied to climate fields, such as those from ensemble modelling.
Camille Besombes, Olivier Pannekoucke, Corentin Lapeyre, Benjamin Sanderson, and Olivier Thual
Nonlin. Processes Geophys., 28, 347–370, https://doi.org/10.5194/npg-28-347-2021, https://doi.org/10.5194/npg-28-347-2021, 2021
Short summary
Short summary
This paper investigates the potential of a type of deep generative neural network to produce realistic weather situations when trained from the climate of a general circulation model. The generator represents the climate in a compact latent space. It is able to reproduce many aspects of the targeted multivariate distribution. Some properties of our method open new perspectives such as the exploration of the extremes close to a given state or how to connect two realistic weather states.
Gerd Schädler and Marcus Breil
Nonlin. Processes Geophys., 28, 231–245, https://doi.org/10.5194/npg-28-231-2021, https://doi.org/10.5194/npg-28-231-2021, 2021
Short summary
Short summary
We used regional climate networks (RCNs) to identify past heatwaves and droughts in Germany. RCNs provide information for whole areas and can provide many details of extreme events. The RCNs were constructed on the grid of the E-OBS data set. Time series correlation was used to construct the networks. Network metrics were compared to standard extreme indices and differed considerably between normal and extreme years. The results show that RCNs can identify severe and moderate extremes.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
Short summary
Short summary
An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Dylan Harries and Terence J. O'Kane
Nonlin. Processes Geophys., 27, 453–471, https://doi.org/10.5194/npg-27-453-2020, https://doi.org/10.5194/npg-27-453-2020, 2020
Short summary
Short summary
Different dimension reduction methods may produce profoundly different low-dimensional representations of multiscale systems. We perform a set of case studies to investigate these differences. When a clear scale separation is present, similar bases are obtained using all methods, but when this is not the case some methods may produce representations that are poorly suited for describing features of interest, highlighting the importance of a careful choice of method when designing analyses.
Jaqueline Lekscha and Reik V. Donner
Nonlin. Processes Geophys., 27, 261–275, https://doi.org/10.5194/npg-27-261-2020, https://doi.org/10.5194/npg-27-261-2020, 2020
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34, https://doi.org/10.5194/npg-27-23-2020, https://doi.org/10.5194/npg-27-23-2020, 2020
Short summary
Short summary
Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
Cited articles
Alpers, W., Brandt, P., Lazar, A., Dagorne, D., Sow, B., Faye, S., Hansen,
M. W., Rubino, A., Poulain, P.-M., and Brehmer, P.: A small-scale oceanic
eddy off the coast of West Africa studied by multi-sensor satellite and
surface drifter data, Remote Sens. Environ., 129, 132–143,
https://doi.org/10.1016/j.rse.2012.10.032, 2013. a
Arai, M. and Yamagata, T.: Asymmetric evolution of eddies in rotating shallow
water, Chaos, 4, 163–175, https://doi.org/10.1063/1.166001, 1994. a
Armi, L., Hebert, D., Oakey, N., Price, J. F., Richardson, P., and Rossby, H.: Two years in the life of a Mediterranean salt lens, J. Phys. Oceanogr., 19, 354–370, https://doi.org/10.1175/1520-0485(1989)019<0354:TYITLO>2.0.CO;2, 1989. a
Bedrosian, E.: A product theorem for Hilbert transforms, Proc. IRE, 51, 868–869, https://doi.org/10.1109/PROC.1963.2308, 1963. a
Benjamini, Y. and Hochberg, Y.: Controlling the false discovery rate: a
practical and powerful approach to multiple
testing, J. Roy. Stat. Soc. B Met., 57, 289–300, https://doi.org/10.1111/j.2517-6161.1995.tb02031.x, 1995. a
Bosse, A., Fer, I., Lilly, J. M., and Søiland, H.: Dynamical controls on
the longevity of a non-linear vortex: the case of the Lofoten Basin Eddy,
Sci. Rep.-UK, 9, 13448, https://doi.org/10.1038/s41598-019-49599-8, 13448, 2019. a
Bower, A. S., Hendry, R. M., Amrhein, D. E., and Lilly, J. M.: Direct
observations of formation and propagation of subpolar eddies into the
subtropical North Atlantic, Deep-Sea Res., 39, 15–41,
https://doi.org/10.1016/j.dsr2.2012.07.029, 2013. a
Cetina-Heredia, P., Roughan, M., van Sebille, E., Keating, S., and Brassington, G. B.: Retention and leakage of water by mesoscale eddies in the East Australian Current system, J. Geophys. Res.-Oceans, 124, 2485–2500, https://doi.org/10.1029/2018JC014482, 2019. a
Cho, J. Y.-K. and Polvani, L. M.: The emergence of jets and vortices in
freely-evolving, shallow-water turbulence on a sphere, Phys. Fluids, 8,
1531–1552, https://doi.org/10.1063/1.868929, 1996. a
Cushman-Roisin, B. and Tang, B.: Geostrophic turbulence and the emergence of
eddies beyond the radius of deformation, J. Phys. Oceanogr., 20, 97–113,
https://doi.org/10.1175/1520-0485(1990)020<0097:GTAEOE>2.0.CO;2, 1990. a
Cushman-Roisin, B., Heil, W., and Nof, D.: Oscillations and rotations of
elliptical warm-core rings, J. Geophys. Res.-Oceans, 20, 11756–11764,
https://doi.org/10.1029/JC090iC06p11756, 1985. a
D'Asaro, E. A., Walker, S., and Baker, E.: Structure of two hydrothermal
megaplumes, J. Geophys. Res.-Oceans, 99, 20361–20373, https://doi.org/10.1029/94JC01846, 1994. a
Daubechies, I. and Paul, T.: Time-frequency localisation operators: a geometric phase space approach II – The use of dilations and translations, Inverse Probl., 4, 661–680, https://doi.org/10.1088/0266-5611/4/3/009, 1988. a
de Jong, M. F., Søiland, H., Bower, A. S., and Furey, H. H.: The
subsurface circulation of the Iceland Sea observed with RAFOS floats,
Deep-Sea Res. Pt. I, 141, 1–10, https://doi.org/10.1016/j.dsr.2018.07.008, 2018. a
Delprat, N., Escudié, B., Guillemain, P., Kronland-Martinet, R.,
Tchamitchian, P., and Torrésani, B.: Asymptotic wavelet and Gabor
analysis: Extraction of instantaneous frequencies, IEEE T. Inform. Theory,
38, 644–665, https://doi.org/10.1109/18.119728, 1992. a, b, c
Dong, C., Liu, Y., Lumpkin, R., Lankhorst, M., Chen, D., McWilliams, J. C., and Guan, Y.: A scheme to identify loops from trajectories of oceanic surface
drifters: an application in the Kuroshio extension region, J. Atmos. Ocean. Tech., 28, 1167–1176, https://doi.org/10.1175/JTECH-D-10-05028.1, 2011. a, b
Eldevik, T. and Dysthe, K. B.: Spiral eddies, J. Phys. Oceanogr., 32, 851–869, https://doi.org/10.1175/1520-0485(2002)032<0851:SE>2.0.CO;2, 2002. a
Elliott, B. A.: Anticyclonic rings in the Gulf of Mexico, J. Phys. Oceanogr., 12, 1292–1309, https://doi.org/10.1175/1520-0485(1982)012<1292:ARITGO>2.0.CO;2, 1982. a, b, c
Flament, P., Lumpkin, R., Tournadre, J., and Armi, L.: Vortex pairing in an
unstable anticyclonic shear flow: discrete subharmonics of one pendulum day,
J. Fluid Mech., 440, 401–409, https://doi.org/10.1017/S0022112001004955, 2001. a, b
Furey, H., Bower, A., Pérez-Brunius, P., Hamilton, P., and Leben, R.: Deep
eddies in the Gulf of Mexico observed with floats, J. Phys. Oceanogr., 48,
2703–2719, https://doi.org/10.1175/JPO-D-17-0245.1, 2018. a
Gabor, D.: Theory of communication, Proc. IRE, 93, 429–457, https://doi.org/10.1049/ji-1.1947.0015, 1946. a, b, c
Garreau, P., Garnier, V., and Schaeffer, A.: Eddy resolving modelling of the
Gulf of Lions and Catalan Sea, Ocean Dynam., 61, 991–1003,
https://doi.org/10.1007/s10236-011-0399-2, 2011. a
Gonella, J.: A local study of inertial oscillations in the upper layer of the
ocean, Deep-Sea Res., 18, 775–788, https://doi.org/10.1016/0011-7471(71)90045-3, 1971. a
Gonella, J.: A rotary-component method for analyzing meteorological and
oceanographic vector time series, Deep-Sea Res., 19, 833–846,
https://doi.org/10.1016/0011-7471(72)90002-2, 1972. a
Griffa, A., Lumpkin, R., and Veneziani, M.: Cyclonic and anticyclonic motion in the upper ocean, Geophys. Res. Lett., 35, L01608,
https://doi.org/10.1029/2007GL032100, 2008. a, b
Hall, C. A. and Leben, R. R.: Observational evidence of seasonality in the
timing of Loop Current eddy separation, Dynam. Atmos. Oceans, 76, 240–267,
https://doi.org/10.1016/j.dynatmoce.2016.06.002, 2016. a, b, c
Haller, G.: An objective definition of a vortex, J. Fluid Mech., 525, 1–26,
https://doi.org/10.1017/S0022112004002526, 2005. a
Haller, G. and Beron-Vera, F. J.: Geodesic theory of transport barriers in
two-dimensional flows, Physica D, 241, 1680–1702,
https://doi.org/10.1016/j.physd.2012.06.012, 2012. a
Hamilton, P., Berger, T. J., and Johnson, W.: On the structure and motions of cyclones in the northern Gulf of Mexico, J. Geophys. Res.-Oceans, 107, 3208, https://doi.org/10.1029/1999JC000270, 2002. a
Harvey, A. C.: Forecasting, structural time series models and the Kalman
filter, Cambridge University Press, Cambridge, UK,
https://doi.org/10.1017/CBO9781107049994, 1989. a
Huang, J. and Yang, L.: Vakman's analysis in L2(ℝ),
Int. J. Comput. Math., 88, 545–554, https://doi.org/10.1080/00207161003631869, 2011. a
Inoue, R., Faure, V., and Kouketsu, S.: Float observations of an anticyclonic
eddy off Hokkaido, J. Geophys. Res.-Oceans, 121, 6103–6120,
https://doi.org/10.1002/2016JC011698, 2016. a
Kirwan Jr., A. D., Merrell Jr., W. J., Lewis, J. K., Whitaker, R. E., and
Legeckis, R.: A model for the analysis of drifter data with an application to
a warm core ring in the Gulf of Mexico, J. Geophys. Res.-Oceans, 89, 3425–3438, https://doi.org/10.1029/JC089iC03p03425, 1984. a, b
Kirwan Jr., A. D., Lewis, J. K., Indest, A. W., Reinersman, P., and Quintero,
I.: Observed and simulated kinematic properties of Loop Current rings, J.
Geophys. Res.-Oceans, 93, 1189–1198, https://doi.org/10.1029/JC093iC02p01189, 1988. a
Kloosterziel, R. C.: Viscous symmetric stability of circular flows, J. Fluid
Mech., 652, 171–193, https://doi.org/10.1017/S0022112009994149, 2010. a
Kourafalou, V., Androulidakis, Y., Le Hénaff, M., and Kang, H.: The dynamics of Cuba Anticyclones (CubANs) and interaction with the Loop
Current/Florida Current system, J. Geophys. Res.-Oceans, 122, 7897–7923,
https://doi.org/10.1002/2017JC012928, 2017. a
Kunze, E.: Near-inertial wave propagation in geostrophic shear, J. Phys. Oceanogr., 15, 544–565,
https://doi.org/10.1175/1520-0485(1985)015<0544:NIWPIG>2.0.CO;2, 1985. a
Lankhorst, M.: A self-contained identification scheme for eddies in drifter and float trajectories, J. Atmos. Ocean. Tech., 23, 1583–1592,
https://doi.org/10.1175/JTECH1931.1, 2006. a
Le Hénaff, M., Kourafalou, V. H., Dussurget, R., and Lumpkin, R.: Cyclonic
activity in the eastern Gulf of Mexico: Characterization from along-track
altimetry and in situ drifter trajectories, Prog. Oceanogr., 120,
120–138, https://doi.org/10.1016/j.pocean.2013.08.002, 2014. a, b, c
Le Hénaff, M., Kourafalou, V. H., Androulidakis, Y., Smith, R. H., Kang, H., Hu, C., and Lamkin, J. T.: In situ measurements of circulation features
influencing cross-shelf transport around northwest Cuba, J. Geophys. Res.-Oceans, 125, e2019JC015780, https://doi.org/10.1029/2019JC015780, 2020. a
Lilly, J. M.: Element analysis: a wavelet-based method for analyzing
time-localized events in noisy time series, P. Roy. Soc. Lond. A Mat., 473,
20160776, https://doi.org/10.1098/rspa.2016.0776, 2017. a
Lilly, J. M.: jLab: A data analysis package for Matlab v1.7.0, Zenodo, https://doi.org/10.5281/zenodo.4547006, 2021. a
Lilly, J. M. and Olhede, S. C.: Wavelet ridge estimation of jointly modulated
multivariate oscillations, in: 2009 Conference Record of the Forty-Third
Asilomar Conference on Signals, Systems, and Computers, 1–4 November 2009, Pacific Grove, California, USA, 452–456,
https://doi.org/10.1109/ACSSC.2009.5469858, 2009a. a, b, c
Lilly, J. M. and Olhede, S. C.: On the analytic wavelet transform,
IEEE T. Inform. Theory, 56, 4135–4156, https://doi.org/10.1109/TIT.2010.2050935, 2010a. a, b, c
Lilly, J. M. and Olhede, S. C.: Generalized Morse wavelets as a superfamily
of analytic wavelets, IEEE T. Signal Proces., 60, 6036–6041,
https://doi.org/10.1109/TSP.2012.2210890, 2012b. a, b, c
Lilly, J. M. and Pérez-Brunius, P.: GulfDrifters: a consolidated surface
drifter dataset for the Gulf of Mexico (Version 1.1.0), Zenodo,
https://doi.org/10.5281/zenodo.3985916, 2021b. a
Lilly, J. M. and Pérez-Brunius, P.: The Gulf of Mexico Eddy Dataset (GOMED), a census of statistically significant eddy-like events from all available surface drifter data (Version 1.1.0), Zenodo, https://doi.org/10.5281/zenodo.3978803, 2021c. a
Lilly, J. M., Scott, R. K., and Olhede, S. C.: Extracting waves and vortices
from Lagrangian trajectories, Geophys. Res. Lett., 38, L23605,
https://doi.org/10.1029/2011GL049727, 2011. a, b, c
Lilly, J. M., Sykulski, A. M., Early, J. J., and Olhede, S. C.: Fractional Brownian motion, the Matérn process, and stochastic modeling of turbulent dispersion, Nonlin. Processes Geophys., 24, 481–514, https://doi.org/10.5194/npg-24-481-2017, 2017. a, b
Lipphardt Jr., B. L., Poje, A. C., and Kirwan, A.: Death of three Loop
Current rings, J. Mar. Res., 66, 25–60, https://doi.org/10.1357/002224008784815748,
2008. a, b, c
Lumpkin, R.: Global characteristics of coherent vortices from surface drifter
trajectories, J. Geophys. Res.-Oceans, 121, 1306–1321,
https://doi.org/10.1146/annurev-marine-010816-060641, 2016. a, b
Lumpkin, R. and Elipot, S.: Surface drifter pair spreading in the North
Atlantic, J. Geophys. Res.-Oceans, 115, C12017,
https://doi.org/10.1029/2010JC006338, 2010. a
Lumpkin, R. and Pazos, M.: Measuring surface currents with Surface Velocity
Program drifters: the instrument, its data, and some recent results, in: Lagrangian Analysis and Prediction in Coastal and
Ocean Processes, edited by: Griffa, A., Kirwan Jr., A. D., Mariano, A., Özgökmen, T., and Rossby, H., Cambridge University Press, Cambridge, UK, 39–67, https://doi.org/10.1017/CBO9780511535901.003, 2007. a
Mallat, S.: A wavelet tour of signal processing, edn. 2, Academic Press,
New York, USA, https://doi.org/10.1016/B978-0-12-374370-1.X0001-8, 1999. a, b, c
Matsuura, T. and Yamagata, T.: On the evolution of nonlinear plantary eddies
larger than the radius of deformation, J. Phys. Oceanogr., 12, 440–456,
https://doi.org/10.1175/1520-0485(1982)012<0440:OTEONP>2.0.CO;2, 1982. a
McKiver, W. J. and Dritschel, D. G.: The stability of a quasi-geostrophic
ellipsoidal vortex in a background shear flow, J. Fluid Mech., 560, 1–17,
https://doi.org/10.1017/S0022112006000462, 2006. a
Meacham, S. P. and Flierl, G. R.: Vortices in shear, Dynam. Atmos. Oceans, 14, 333–386, https://doi.org/10.1016/0377-0265(89)90067-5, 1990. a
Merrell Jr., W. J. and Morrison, J. M.: On the circulation of the western Gulf of Mexico with observations from April 1978, J. Geophys. Res.-Oceans, 86, 4181–4185, https://doi.org/10.1029/JC086iC05p04181, 1981. a
Munk, W. H., Armi, L., Fischer, K., and Zachariasen, F.: Spirals on the sea, P. Roy. Soc. Lond. A Mat., 456, 1217–1280, https://doi.org/10.1098/rspa.2000.0560, 2000. a
Nerlove, M.: Distributed lags and unobserved components in economic time
series, in: Ten Economic Studies in the Tradition of Irving Fisher, John Wiley & Sons Inc., New York, USA, 127–169, 1967. a
Olhede, S. C. and Walden, A. T.: Generalized Morse wavelets, IEEE T. Signal
Proces., 50, 2661–2670, https://doi.org/10.1109/TSP.2002.804066, 2002. a
Padilla-Pilotze, A. R.: Evidence of a cyclonic eddy in the Bay of
Campeche, Cienc. Mar., 16, 1–14, https://doi.org/10.7773/cm.v16i3.703, 1990. a, b
Papoulis, A.: The Fourier integral and its applications, McGraw-Hill Book
Company Inc., New York, USA, 1962. a
Park, J., Lindberg, C. R., and Vernon III, F. L.: Multitaper spectral
analysis of high-frequency seismograms, J. Geophys. Res.-Oceans, 92,
12675–12684, https://doi.org/10.1029/JB092iB12p12675, 1987. a
Percival, D. B. and Walden, A. T.: Spectral Analysis for Physical Applications, Cambridge University Press, New York, USA, https://doi.org/10.1017/CBO9780511622762, 1993. a
Pérez-Brunius, P., García-Carrillo, P., Dubranna, J., Sheinbaum, J., and
Candela, J.: Direct observations of the upper layer circulation in the
southern Gulf of Mexico, Deep-Sea Res., 85, 182–194,
https://doi.org/10.1016/j.dsr2.2012.07.020, 2013. a, b
Picinbono, B.: On instantaneous amplitude and phase of signals, IEEE T. Signal Proces., 45, 552–560, https://doi.org/10.1109/78.558469, 1997. a, b, c
Ripa, P.: On the stability of elliptical vortex solutions of the shallow-water equations, J. Fluid Mech., 183, 343–363, https://doi.org/10.1017/S0022112087002660, 1987. a
Ruddick, B. R.: Anticyclonic lenses in large-scale strain and shear, J. Phys.
Oceanogr., 17, 741–749,
https://doi.org/10.1175/1520-0485(1987)017<0741:ALILSS>2.0.CO;2, 1987. a
Søiland, H. and Rossby, T.: On the structure of the Lofoten Basin Eddy, J. Geophys. Res.-Oceans, 118, 4201–4212, https://doi.org/10.1002/jgrc.20301, 2013. a
Testor, P. and Gascard, J.-C.: Large-scale spreading of deep waters in the
western Mediterranean Sea by submesoscale coherent eddies, J. Phys.
Oceanogr., 33, 75–87, https://doi.org/10.1175/1520-0485(2003)033<0075:LSSODW>2.0.CO;2,
2003.
a
Thomson, D. J.: Spectrum estimation and harmonic analysis, P. IEEE, 70,
1055–1096, https://doi.org/10.1109/PROC.1982.12433, 1982. a
Trodahl, M., Isachsen, P. E., Lilly, J. M., Nilsson, J., and Kristensen, N. M.: The regeneration of the Lofoten Vortex through vertical alignment, J. Phys. Oceanogr., 50, 2689–2711, https://doi.org/10.1175/JPO-D-20-0029.1, 2020. a
Vakman, D.: On the analytic signal, the Teager-Kaiser energy algorithm, and other methods for defining amplitude and frequency, IEEE T. Signal Proces., 44, 791–797, https://doi.org/10.1109/78.492532, 1996. a, b, c
Vázquez De La Cerda, A. M., Reid, R. O., DiMarco, S. F., and Jochens, A. E.: Bay of Campeche circulation: an update, in: Circulation in the Gulf of Mexico: Observations and Models, edited by: Sturges, W. and
Lugo-Fernández, A., no. 161 in Geophysical Monograph Series,
American Geophysical Union, 279–293, https://doi.org/10.1029/GM161, 2005. a, b
Veneziani, M., Griffa, A., Garraffo, Z., and Chassignet, E.: Lagrangian spin
parameter and coherent structures from trajectories released in a
high-resolution ocean model, J. Mar. Res., 63, 753–788,
https://doi.org/10.1357/0022240054663187, 2005a. a
Veneziani, M., Griffa, A., Reynolds, A. M., Garraffo, Z. D., and Chassignet,
E. P.: Parameterizations of Lagrangian spin statistics and particle
dispersion in the presence of coherent vortices, J. Mar. Res., 63,
1057–1083, https://doi.org/10.1357/002224005775247571, 2005b. a
Young, W. R.: Elliptical vortices in shallow water, J. Fluid Mech., 171,
101–119, https://doi.org/10.1017/S0022112086001386, 1986. a
Short summary
Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying eddies in the Gulf of Mexico is created through the analysis of trajectories of drifting instruments. The method involves the identification of quasi-periodic signals, characteristic of particles trapped in eddies, from the displacement records, followed by the creation of a measure of statistical significance. It is expected that this dataset will be of use to other authors studying this region.
Long-lived eddies are an important part of the ocean circulation. Here a dataset for studying...
