journal geophysical research

Journal of Geophysical Research: Oceans

Subject Area and Category

• Earth and Planetary Sciences (miscellaneous)
• Geochemistry and Petrology
• Oceanography
• Space and Planetary Science

Wiley-Blackwell

Publication type

21699291, 21699275

1986-1992, 1994-2022

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How to publish in this journal

[email protected]

The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

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AGU Launches Journal of Geophysical Research: Machine Learning and Computation

Today, we are excited to announce the launch of a new open access journal, the Journal of Geophysical Research: Machine Learning and Computation .

JGR: Machine Learning and Computation is dedicated to research that explores data-driven and computational methodologies based on statistical analysis, machine learning, artificial intelligence and mathematical models, with the aim of advancing knowledge in the Earth and space sciences. In particular, this journal will accept papers proposing novel results in the broad fields of solar and space physics, geophysical fluids and planetary environments, Earth surface and interiors, and biogeosciences.

“In the last five years alone, we’ve seen research utilizing machine learning and artificial intelligence grow rapidly across AGU journals and meetings, and for good reason,” said Lisa J. Graumlich, president of AGU. “Earth and space scientists have access to more data than ever. These cutting-edge techniques are invaluable in helping scientists use this data to uncover new insights about our planet and improve crucial scientific predictions, including alerting communities to natural hazards and climate-related risks.”

The decision to launch JGR: Machine Learning and Computation follows a growing body of research in the field, evidenced by dedicated special collections and meetings. In 2022, more than 500 manuscripts submitted across AGU journals and more than 1700 abstracts submitted to AGU22 contained the index terms “machine learning” or “artificial intelligence.” The journal was proposed by members of AGU’s Nonlinear Geophysics Section and received vocal approval in discussions with the AGU Council and editors of AGU journals.

“There came a point when special collections were not enough,” said Enrico Camporeale, founding editor-in-chief of JGR: Machine Learning and Computation and research scientist at the University of Colorado Boulder. “ JGR: Machine Learning and Computation fills a crucial gap for researchers utilizing machine learning or artificial intelligence in the Earth and space sciences. With this journal, we now have a dedicated platform for rigorous peer review of our research.”

JGR: Machine Learning and Computation  is the 12th of 24 AGU journals to be fully open access. Open access journals remove the paywall that would require readers to have a paid subscription, thereby increasing equitable access to the latest advances among researchers and the public alike. AGU also offers various funding support for authors publishing in its fully open access journals, including full waivers. All accepted papers will be published regardless of the author’s ability to pay publication fees. AGU is a proud leader in open science , advancing the effort through publications, meetings, data leadership, community science, policy engagement and career development.

“The growth of machine learning goes hand-in-hand with the push to take more journals open access,” said Matthew Giampoala, AGU’s vice president of publications. “Both of these practices accelerate the speed of scientific discovery and inspire researchers to collaborate more broadly with their peers.”

AGU is currently seeking dynamic, well-organized scientists with high editorial standards to join the JGR: Machine Learning and Computation editorial board as editors and associate editors . Individuals interested in being considered should send a curriculum vitae with a letter of interest outlining qualifications for the position and interest in the journal to [email protected] .

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It is timely to launch such a journal. In space weather, data science, prediction method and ven my own field of numerical space weather modeling welcomes sych a journal devoted to ML. Recently, our research topic applies DL techniques combined with numerical space weather models to show the potentiality of ML.

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Analyzing 50 years of the lacq induced seismicity (southwestern, france) highlights the role of fluid injection.

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L Jacquemond, J Letort, F Cotton, M Causse, J R Grasso, G Senechal, J B Ammirati, B Derode, F Grimaud, H Pauchet, S Benhamed, M Sylvander, Analyzing 50 years of the Lacq induced seismicity (SouthWestern, France) highlights the role of fluid injection, Geophysical Journal International , 2024;, ggae119, https://doi.org/10.1093/gji/ggae119

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The Lacq area in southwest France has been associated with continuous moderate induced seismic activity since 1969. However, the mechanisms driving this induced seismicity are not fully understood: reservoir depletion has been proposed as the main factor, and more recently wastewater injection has been suggested to play a more important role (Grasso et al., 2021). The interpretation of these mechanisms relies heavily on the quality of earthquake locations, which we prove to be weak due to a lack of local instrumentation for several years. In order to provide the most complete and reliable induced event catalog for the studies of the Lacq induced seismicity mechanisms & seismic hazard, we made an exhaustive compilation, analysis and improvement of all available catalogs. We also provided new earthquake detections & relocations in a 3D velocity model from past and present temporary deployments never used for studying the Lacq area. Important remaining location uncertainties lead us to also carefully sort the events according to their location confidence, defining 3 classes of events (unconstrained location, location constrained within 2-3 km and 1-2 km respectively). This new harmonized catalog and the identification of well-constrained events, covering 50 years of induced seismicity, allow us to propose that wastewater injection is almost certainly the main mechanism driving the seismicity, with (i) most of the constrained events located within the reservoir boundaries and (ii) the released seismic energy variations following variations in injection operations at different scales. In particular, we have also highlighted a change in the injection-seismicity relationship around 2010–2013. From 2013, despite lower injection volumes, seismicity remained persistent and some clusters of earthquakes were detected predominantly in spring, summer, and early autumn, except in winter periods. From 2016, we observed a strong temporal relationship between days with higher rate/volume injections (approximately above 400m3/day) and both clustered events and higher magnitude earthquakes (greater than 2.4).

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General properties of geophysical hydrodynamics models

• Published: August 2007
• Volume 62 , pages 255–259, ( 2007 )

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• K. V. Pokazeev ,
• V. G. Baidulov &
• M. P. Vasil’ev  

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The influence of simplifying assumptions used in geophysical hydrodynamics models on the general properties of space and time, as well as on the fundamental physical principles that underlie the mechanical systems, is studied. Analysis of the general properties of such models is supplemented by investigation of the peculiarities introduced by stratification into the solutions of nonlinear stationary models. The possibilities for representing the solutions as finite and infinite Fourier series in cylindrical coordinates are explored.

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Hydrodynamic Models

On the Geometry Arising in Some Meteorological Models in Two and Three Dimensions

Geodynamo Models

M. Yu. Reshetnyak

V. M. Kamenkovich, Fundamental of Ocean Dynamics (Leningrad, 1973) [in Russian].

Ocean Physics, Vol. 2: Ocean Hydrodynamics , Ed. by A. S. Monin (Institute of Oceanology, Moscow, 1978).

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Yu. Z. Miropolskii, Dynamics of Internal Gravity Waves in Ocean (Leningrad, 1981) [in Russian].

V. G. Baidulov and Yu. D. Chashechkin, Dokl. Akad. Nauk 387 (6), 760 (2002).

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G. K. Batchelor, An Introduction to Fluid Dynamics (Cambridge University Press, Cambridge, 1970; Mir, Moscow, 1973).

G. G. Chernyi, Izv. Ross. Akad. Nauk, Ser. Mekhanika zhidkosti i gaza, No. 4, 39 (1997).

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Original Russian Text © K.V. Pokazeev, V.G. Baidulov, M.P. Vasil’ev, 2007, published in Vestnik Moskovskogo Universiteta. Fizika, 2007, No. 4, pp. 64–68.

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Pokazeev, K.V., Baidulov, V.G. & Vasil’ev, M.P. General properties of geophysical hydrodynamics models. Moscow Univ. Phys. 62 , 255–259 (2007). https://doi.org/10.3103/S0027134907040121

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Received : 13 September 2006

Issue Date : August 2007

DOI : https://doi.org/10.3103/S0027134907040121

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The fast winds of Venus are getting faster

The most detailed record of cloud motion in the atmosphere of Venus chronicled by ESA’s Venus Express has revealed that the planet’s winds have steadily been getting faster over the last six years.

Venus is well known for its curious super-rotating atmosphere, which whips around the planet once every four Earth days. This is in stark contrast to the rotation of the planet itself – the length of the day – which takes a comparatively laborious 243 Earth days.

By tracking the movements of distinct cloud features in the cloud tops some 70 km above the planet’s surface over a period of 10 venusian years (6 Earth years), scientists have been able to monitor patterns in the long-term global wind speeds.

When Venus Express arrived at the planet in 2006, average cloud-top wind speeds between latitudes 50º on either side of the equator were clocked at roughly 300 km/h. The results of two separate studies have revealed that these already remarkably rapid winds are becoming even faster, increasing to 400 km/h over the course of the mission.

“This is an enormous increase in the already high wind speeds known in the atmosphere. Such a large variation has never before been observed on Venus, and we do not yet understand why this occurred,” says Igor Khatuntsev from the Space Research Institute in Moscow and lead author of the Russian-led paper to be published in the journal Icarus.

Dr Khatuntsev’s team determined the wind speeds by measuring how cloud features in images moved between frames: over 45 000 features were painstakingly tracked by hand and more than 350 000 further features were tracked automatically using a computer programme.

In a complementary study, a Japanese-led team used their own automated cloud tracking method to derive the cloud motions: their results are to be published in the Journal of Geophysical Research.

On top of this long-term increase in the average wind speed, however, both studies have also revealed regular variations linked to the local time of day and the altitude of the Sun above the horizon, and to the rotation period of Venus.

One regular oscillation occurs roughly every 4.8 days near the equator and is thought to be connected to atmospheric waves at lower altitudes.

But the research also unveiled some harder-to-explain curiosities.

“Our analysis of cloud motions at low latitudes in the southern hemisphere showed that over the six years of study the velocity of the winds changed by up 70 km/h over a time scale of 255 Earth days – slightly longer than a year on Venus,” says Toru Kouyama from the Information Technology Research Institute in Ibaraki, Japan.

The two teams also saw dramatic variations in the average wind speeds between consecutive orbits of Venus Express around the planet.

In some cases, wind speeds at low latitudes varied such that clouds completed one journey around the planet in 3.9 days, while on other occasions they took 5.3 days.

Scientists currently have no explanation for either of these variations, or for the long-term overall increase in wind speeds.

“Although there is clear evidence that the average global wind speeds have increased, further investigations are needed in order to explain what drives the atmospheric circulation patterns that are responsible, and to explain the changes seen in localised areas on shorter timescales,” says Håkan Svedhem, ESA’s Venus Express Project Scientist.

“The atmospheric super-rotation of Venus is one of the great unexplained mysteries of the Solar System. These results add more mystery to it, as Venus Express continues to surprise us with its ongoing observations of this dynamic, changing planet.”

Notes for Editors

Related papers:

“Cloud level winds from the Venus Express Monitoring Camera imaging,” by I. Khatuntsev et al., is accepted for publication in the journal Icarus. doi:10.1016/j.icarus.2013.05.018 .

“Long-term variation in the cloud-tracked zonal velocities at the cloud top of Venus deduced from Venus Express VMC images,” by T. Kouyama et al. is accepted for publication in the Journal of Geophysical Research – Planets,118, 37–46, 2013 doi:10.1029/2011JE004013 .

The observations were carried out using the Venus Monitoring Camera (VMC; principal investigator W. Markiewicz, MPI-Ae, Katlenburg-Lindau, Germany). The VMC is a wide-angle multichannel camera that takes images in the near-infrared, ultraviolet and visible wavelengths.

For further information, please contact:

Igor Khatuntsev Space Research Institute of the Russian Academy of Sciences, Moscow, Russia Email: [email protected]

Toru Kouyama Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan Email: [email protected]

Håkan Svedhem Venus Express Project Scientist Tel: +31 71 565 3370 Email: [email protected]

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