matplotlib/matplotlib: REL: v3.2.1

Caswell, Thomas A; Droettboom, Michael; Lee, Antony; Hunter, John; Firing, Eric; Stansby, David; Klymak, Jody M.; Hoffmann, Tim; Andrade, Elliott Sales de; Varoquaux, Nelle; Nielsen, Jens Hedegaard; Root, Benjamin; Elson, Phil; May, Ryan; Dale, Darren; Lee, Jae‐Joon; Seppänen, Janne; McDougall, Damon; Straw, Andrew; Hobson, Paul; Gohlke, Christoph; Yu, Tony S; Ma, Eric; Vincent, Adrien F.; Silvester, Steven; Moad, Charlie; Kniazev, Nikita; Ivanov, Paul; Ernest, Elan; Katins, Jan

doi:10.5281/zenodo.3714460

Cited by 31 publications

(8 citation statements)

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“…For example, the normalized inputs with and without an ionospheric storm are plotted in Figure 2. Figure 2 and the following figures in this paper were made with Matplotlib version 3.2.1 (Caswell et al, 2020;Hunter, 2007), available under the Matplotlib license at https://matplotlib.org/. There are some differences between these two data series that can be captured by the deep learning method to produce a classification.…”

Section: Inputs and Outputmentioning

confidence: 99%

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

Ban

Guo²,

Zhao³

et al. 2023

Space Weather

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An ionospheric storm forecasting method was proposed using a deep learning algorithm, LSTM (long short‐term memory). We used the perturbation index to denote the level of an ionospheric storm, deduced from foF2 data, and helped to remove most of the local time and seasonal variations in the ionosphere. In constructing the model, a number of correlated factors were used as inputs, including the properties of coronal mass ejections, solar flare bursts, interplanetary conditions, and geomagnetic and ionospheric states, and the output was whether an ionospheric storm occurred locally in the next 24 hr. Data sets from 2007 to 2014 were used to train the model, and those from 2015 to 2016 were used for validation. The results showed that the model behaved well in most events. The mean precision rate, recall rate, accuracy, and F1 score of the model were 71.7%, 59.7%, 92.7%, and 65.0% in northern China and 78.9%, 56.3%, 96.3% and 65.0% in southern China, respectively. The LSTM forecasting model performed better than other models such as persistence, multiple‐layer perceptron and support vector machine models. Case studies also showed good performance during geomagnetic storms of different strengths. We believe that this model can be beneficial for functional ionospheric storm operation.

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Section: Inputs and Outputmentioning

confidence: 99%

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

Ban

Guo²,

Zhao³

et al. 2023

Space Weather

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“…Some figures were made using Generic Mapping Tools (GMT) 6 (Wessel et al, 2019) licensed under LGPL. Other figures were made with Matplotlib version 3.2.1 (Caswell et al, 2020;Hunter, 2007).…”

Section: Data Availability Statementmentioning

confidence: 99%

Surface Wave Isotropic and Azimuthally Anisotropic Dispersion Across Alaska and the Alaska‐Aleutian Subduction Zone

et al. 2022

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The North American plate in Alaska is an amalgamation of many tectonic terranes (e.g., Colpron et al., 2007) (Figure 1a). The present terranes result from multiple long-term episodes of accretion, accumulation, and deformation due to the dynamic and complex plate boundary interactions of the Pacific, Siberian, and North American Plates (e.g., Shephard et al., 2013). Northern Alaska is a passive margin and was accreted in the late Jurassic (e.g., Moore & Box, 2016;Till, 2016). The Alaskan Interior experienced complex collisional and extensional deformation in the Permian and Cretaceous periods (e.g., Johnston, 2001;Plafker & Berg, 1994). In southern Alaska, accreted terranes docked in the late Mesozoic to Cenozoic, and strike-slip zones developed due to the subduction of the Pacific Plate and Yakutat microplate (e.g., Shephard et al., 2013). Offshore southern Alaska, the Alaska-Aleutian subduction zone is one of the most tectonically and seismically active convergent margins in the world. Historically, a series of great earthquakes have occurred along the megathrust boundary (Figure 1a).With the recent deployment of the USArray Transportable Array (TA) in continental Alaska (Busby & Aderhold, 2020) and the Alaska Amphibious Community Seismic Experiment (AACSE) in the Alaska Peninsula and offshore (Barcheck et al., 2020), as well as other regional networks, new seismic data have become available to investigate the tectonic architecture and history of deformation of Alaska and the subduction zone both onshore and offshore (Figure 1b). Previous seismic studies in Alaska have focused both on the isotropic and anisotropic structure of the crust and mantle. Isotropic seismic models have been presented based on seismic reflection and

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“…The CMIP5 datasets used in this study (listed in Caswell et al, 2020;Hunter, 2007). The codes used to produce figures and tables in this paper are available from Zenodo (Kanno, 2022).…”

Section: Data Availability Statementmentioning

confidence: 99%

Future Changes of Atmospheric Energy Cycle in CMIP5 Climate Models

Kanno

Iwasaki

2022

JGR Atmospheres

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Future changes in the atmospheric energy cycle were estimated by using 12 climate models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and mass‐weighted isentropic zonal‐mean framework. In this framework, the zonal‐mean available potential energy (AZ) is converted to the zonal‐mean kinetic energy (KZ) through mean‐meridional direct circulations, and KZ is converted to the wave energy (W), the sum of eddy available potential energy and eddy kinetic energy, through wave‐mean flow interactions. The comparison between the late 21st century in a high emission scenario and the late 20th century in the historical scenario indicates a significant increase in AZ and KZ in winter in the Northern Hemisphere (NH) and the Southern Hemisphere (SH). The wave energy significantly decreases in the NH winter but slightly increases in the SH winter. In the NH winter, the stationary wave energy significantly decreases, and the transient wave energy shifts poleward. Global warming reduces the baroclinic instability wave activity, which suppresses the upward Eliassen‐Palm flux and wave‐induced extratropical direct circulation. It decreases the dynamic energy conversion rates C(AZ, KZ) and C(KZ, W). On the other hand, the diabatic wave energy generation rate (QE) is projected to increase, particularly in the SH. The future W change is consistent with the change in the sum of C(KZ, W) and QE. Changes in both the dynamical energy conversion associated with wave‐mean flow interactions and the generation of eddy available potential energy associated with diabatic heating processes are necessary to explain the wave energy change.

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matplotlib/matplotlib: REL: v3.2.1

Cited by 31 publications

References 0 publications

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM

Surface Wave Isotropic and Azimuthally Anisotropic Dispersion Across Alaska and the Alaska‐Aleutian Subduction Zone

Future Changes of Atmospheric Energy Cycle in CMIP5 Climate Models

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