scholarly journals Reanalysis of relativistic radiation belt electron fluxes using CRRES satellite data, a radial diffusion model, and a Kalman filter

2007 ◽  
Vol 112 (A12) ◽  
pp. n/a-n/a ◽  
Author(s):  
Yuri Shprits ◽  
Dmitri Kondrashov ◽  
Yue Chen ◽  
Richard Thorne ◽  
Michael Ghil ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Diffusion Model ◽  
Satellite Data ◽  
Radiation Belt ◽  
Radial Diffusion

Modeling Radiation Belt Electron Dynamics with the DREAM3D Diffusion Model

2014 ◽  
Author(s):  
Weichao Tu ◽  
Gregory S. Cunningham ◽  
Yue Chen ◽  
Michael G. Henderson ◽  
Steven K. Morley ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Radiation Belt ◽  
Electron Dynamics

Radial diffusion coefficients database in the frame of SafeSpace project

2021 ◽  
Author(s):  
Christos Katsavrias ◽  
Ioannis A. Daglis ◽  
Afroditi Nasi ◽  
Constantinos Papadimitriou ◽  
Marina Georgiou
Keyword(s):  
Diffusion Coefficients ◽  
Radiation Belt ◽  
Field Measurements ◽  
Radial Diffusion ◽  
Relativistic Electrons ◽  
Outer Radiation Belt ◽  
Horizon 2020 ◽  
Research And Innovation ◽  
The Difference ◽  
Cycle 24

<p>Radial diffusion has been established as one of the most important mechanisms contributing the acceleration and loss of relativistic electrons in the outer radiation belt. Over the past few years efforts have been devoted to provide empirical relationships of radial diffusion coefficients (D<sub>LL</sub>) for radiation belt simulations yet several studies have suggested that the difference between the various models can be orders of magnitude different at high levels of geomagnetic activity as the observed D<sub>LL</sub> have been shown to be highly event-specific. In the frame of SafeSpace project we have used 12 years (2009 – 2020) of multi-point magnetic and electric field measurements from THEMIS A, D and E satellites to create a database of calculated D<sub>LL</sub>. In this work we present the first statistics on the evolution of D<sub>LL </sub>during the various phases of Solar cycle 24 with respect to the various solar wind parameters and geomagnetic indices.</p><p>This work has received funding from the European Union's Horizon 2020 research and innovation programme “SafeSpace” under grant agreement No 870437.</p>

ULF Wave Power During Geomagnetic Storms and Implications for Radial Diffusion Processes

2021 ◽  
Author(s):  
Jasmine Sandhu ◽  
Jonathan Rae ◽  
John Wygant ◽  
Aaron Breneman ◽  
Sheng Tian ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Diffusion Coefficients ◽  
Radiation Belt ◽  
Geomagnetic Storms ◽  
Radial Diffusion ◽  
Ulf Waves ◽  
Wave Power ◽  
Outer Radiation Belt ◽  
Large Variability ◽  
Ulf Wave

<p>Ultra Low Frequency (ULF) waves drive radial diffusion of radiation belt electrons, where this process contributes to and, at times, dominates energisation, loss, and large scale transport of the outer radiation belt. In this study we quantify the changes and variability in ULF wave power during geomagnetic storms, through a statistical analysis of Van Allen Probes data for the time period spanning 2012 – 2019. The results show that global wave power enhancements occur during the main phase, and continue into the recovery phase of storms. Local time asymmetries show sources of ULF wave power are both external solar wind driving as well as internal sources from coupling with ring current ions and substorms.</p><p>The statistical analysis demonstrates that storm time ULF waves are able to access lower L values compared to pre-storm conditions, with enhancements observed within L = 4. We assess how magnetospheric compressions and cold plasma distributions shape how ULF wave power propagates through the magnetosphere. Results show that the Earthward displacement of the magnetopause is a key factor in the low L enhancements. Furthermore, the presence of plasmaspheric plumes during geomagnetic storms plays a crucial role in trapping ULF wave power, and contributes significantly to large storm time enhancements in ULF wave power.</p><p>The results have clear implications for enhanced radial diffusion of the outer radiation belt during geomagnetic storms. Estimates of storm time radial diffusion coefficients are derived from the ULF wave power observations, and compared to existing empirical models of radial diffusion coefficients. We show that current Kp-parameterised models, such as the Ozeke et al. [2014] model, do not fully capture the large variability in storm time radial diffusion coefficients or the extent of enhancements in the magnetic field diffusion coefficients.</p>

scholarly journals Application of remote sensing to the study of the pelagic spiny lobster larval transport in the Tropical Atlantic

2009 ◽  
Vol 57 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Camila Aguirre Góes Rudorff ◽  
João Antônio Lorenzzetti ◽  
Douglas F. M. Gherardi ◽  
Jorge Eduardo Lins-Oliveira
Keyword(s):  
Diffusion Model ◽  
Satellite Data ◽  
El Niño ◽  
El Nino ◽  
Spiny Lobster ◽  
Ocean Dynamics ◽  
Tropical Atlantic ◽  
Geostrophic Current ◽  
Advection Diffusion ◽  
Larval Dispersion

The connectivity of marine populations via larval dispersal is crucial for the maintenance of fisheries production and biodiversity. Because larval dispersion takes place on different spatial scales, global operational satellite data can be successfully used to investigate the connectivity of marine populations on different spatial and temporal scales. In fact, satellite data have long been used for the study of the large and mesoscale biological processes associated with ocean dynamics. This paper presents simulations of spiny lobster larvae transport in the Tropical Atlantic using the geostrophic currents, generated by altimetry that feeds an advection/diffusion model. Simulations were conducted over the Tropical Atlantic (20ºN to 15ºS), considering four larvae release areas: the Cape Verde Archipelago, the Ivory Coast, Ascension Island and Fernando de Noronha Archipelago. We used mean geostrophic current (MGC) calculated from 2001 to 2005 to represent the mean circulation of the Tropical Atlantic. We also ran the model for the El Niño geostrophic current regime (ENGC) using part of the MGC data, representing the El Niño 2002/2003 event. Results suggest that the intensification of the mesoscale ocean processes associated with El Niño events promotes the connectivity between populations, increasing the chances of a genetic flux among different stocks. We concluded that the altimetry geostrophic current data together with a relatively simple advection/diffusion model can provide useful information about the physical dynamics necessary to conduct studies on larval dispersion.

scholarly journals Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization

2017 ◽  
Vol 145 (11) ◽  
pp. 4575-4592 ◽  
Author(s):  
Craig H. Bishop ◽  
Jeffrey S. Whitaker ◽  
Lili Lei
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Satellite Data ◽  
Localization Length ◽  
Model Space ◽  
Ensemble Size ◽  
Length Scales ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Expanded Ensemble

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

scholarly journals Radial diffusion modeling with empirical lifetimes: comparison with CRRES observations

2005 ◽  
Vol 23 (4) ◽  
pp. 1467-1471 ◽  
Author(s):  
Y. Y. Shprits ◽  
R. M. Thorne ◽  
G. D. Reeves ◽  
R. Friedel
Keyword(s):  
Diffusion Model ◽  
Radiation Effects ◽  
Outer Zone ◽  
Recovery Phase ◽  
Decay Rates ◽  
Radial Diffusion ◽  
Main Phase ◽  
Relativistic Electrons ◽  
Storm Main Phase ◽  
Order Of Magnitude

Abstract. A time dependent radial diffusion model is used to quantify the competing effects of inward radial diffusion and losses on the distribution of the outer zone relativistic electrons. The rate of radial diffusion is parameterized by Kp with the loss time as an adjustable parameter. Comparison with HEEF data taken over 500 Combined Release and Radiation Effects Satellite (CRRES) orbits indicates that 1-MeV electron lifetimes near the peak of the outer zone are less than a day during the storm main phase and few days under less disturbed conditions. These values are comparable to independent estimates of the storm time loss rate due to scattering by EMIC waves and chorus emission, and also provide an acceptable representation of electron decay rates following the storm time injection. Although our radial diffusion model, with data derived lifetimes, is able to simulate many features of the variability of outer zone fluxes and predicts fluxes within one order of magnitude accuracy for most of the storms and L values, it fails to reproduce the magnitude of flux changes and the gradual build up of fluxes observed during the recovery phase of many storms. To address these differences future modeling should include an additional local acceleration source and also attempt to simulate the pronounced loss of electrons during the main phase of certain storms.

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