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

Shprits, Y. Y.; Kondrashov, Dmitri; Chen, Yue; Thorne, R. M.; Ghil, Michael; Friedel, R. H. W.; Reeves, G. D.

doi:10.1029/2007ja012579

Cited by 78 publications

(117 citation statements)

References 36 publications

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“…This is corroborated by the high correlation between the occurrence of ULF waves, flux increases and the speed of solar wind (Mathie and Mann 2000;Horne et al 2005). Recent observations show the peak particle density near L = 4.5 and not in the outer region as required by inward radial transport (Iles et al 2006;Chan et al 2007;Shprits et al 2007). This suggests the presence of a local acceleration mechanism (Horne 2007;Shprits et al 2007).…”

Section: Earth's Radiation Beltsmentioning

confidence: 99%

“…Recent observations show the peak particle density near L = 4.5 and not in the outer region as required by inward radial transport (Iles et al 2006;Chan et al 2007;Shprits et al 2007). This suggests the presence of a local acceleration mechanism (Horne 2007;Shprits et al 2007). …”

Section: Earth's Radiation Beltsmentioning

confidence: 99%

“…During the resonant wave-particle interaction, the chorus waves grow at the cost of low energy (*10 keV) and small pitch angle electrons. At the same time waves also interact with much higher energy electrons at large pitch angles and accelerate them (Horne 2007;Shprits et al 2006Shprits et al , 2007. Thus there is a transfer of energy from a large number of low energy electrons at small pitch angles to accelerate a fraction of the population at large pitch angle to higher energy.…”

Section: Earth's Radiation Beltsmentioning

confidence: 99%

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Space Weather: Physics, Effects and Predictability

2010

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The time varying conditions in the near-Earth space environment that may affect space-borne or ground-based technological systems and may endanger human health or life are referred to as space weather. Space weather effects arise from the dynamic and highly variable conditions in the geospace environment starting from explosive events on the Sun (solar flares), Coronal Mass Ejections near the Sun in the interplanetary medium, and various energetic effects in the magnetosphere-ionosphere-atmosphere system. As the utilization of space has become part of our everyday lives, and as our lives have become increasingly dependent on technological systems vulnerable to the space weather influences, the understanding and prediction of hazards posed by these active solar events have grown in importance. In this paper, we review the processes of the Sun-Earth interactions, the dynamic conditions within the magnetosphere, and the predictability of space weather effects on radio waves, satellites and ground-based technological systems today.

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Section: Earth's Radiation Beltsmentioning

confidence: 99%

Section: Earth's Radiation Beltsmentioning

confidence: 99%

Section: Earth's Radiation Beltsmentioning

confidence: 99%

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Space Weather: Physics, Effects and Predictability

2010

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“…They have already been proven as a valuable tool able to guide "the best" estimate of the state of a complex system in the fields of oceanography (Evensen, 1994), ecological systems (Eknes and Evensen, 2001), meteorology (Houtekamer, 1998), ionosphere (Schunk et al, 2004) and radiation belts . Naehr and Toffoletto (2005) as well as Shprits et al (2007) illustrated using synthetic data how a Kalman filter may be applied successfully to radiation belt forecasts. More recently, the applicability of data assimilation techniques to the radiation belts has been proven despite the uncertainties that are difficult to assess.…”

Section: S a Bourdarie And V F Maget: Electron Radiation Belt Datmentioning

confidence: 99%

“…according to L * , where only radial diffusion is considered (Naehr and Toffoletto, 2005;Koller et al, 2007;Kondrashov et al, 2007). In Koller et al (2007) and Shprits et al (2007), data assimilation with a 1-D physical model has been used to study radial profiles of phase space density. In the present paper, we describe a data assimilation tool based on the 3-D Salammbô code (Varotsou et al, 2005(Varotsou et al, , 2008 and an ensemble Kalman filter (Evensen, 1994).…”

Section: S a Bourdarie And V F Maget: Electron Radiation Belt Datmentioning

confidence: 99%

Electron radiation belt data assimilation with an ensemble Kalman filter relying on the Salammbô code

Bourdarie

Maget

2012

Ann. Geophys.

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Abstract. In this study we implement a data assimilation tool using a 3-D radiation belt model and an ensemble Kalman filter approach. High time and space reanalysis of the electron radiation belt fluxes is obtained over the time period 5 October to 25 October 1990 by combining sparse observations with the Salammbô 3-D model in an optimal way. The convergence of the ensemble Kalman filter is analyzed carefully. The risk of using a biased physical model is discussed and relative consequences are highlighted. Finally, a validation against CRRES data and major improvements compared to pure physics based model are presented.

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Noise statistics identification for Kalman filtering of the electron radiation belt observations I: Model errors

et al. 2014

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In this study we present a first attempt to identify errors of the 1-D radial diffusion model for relativistic electron phase space density (PSD). In practice, the model error and characteristics of satellite observations are poorly known, which may cause failure of a Kalman filter algorithm. Correct specification of model errors statistics is necessary for the development of the next generation of radiation belt specification models providing the effective PSD reconstruction and hence the prediction and mitigation of space weather effects in the hazardous space environment. The proposed approach to the identification of errors statistics is based on estimating the unknown bias and the covariance matrix of model errors from the sparse CRRES observations over a period of 441 days, from 28 July 1990 to 11 October 1991. With our technique we demonstrate that model errors are biased. Neglecting the bias when applying a data assimilation algorithm to radiation belt electrons can cause significant errors of the PSD estimate during data gaps. Both the identified bias and the covariance matrix of model errors increase with increase of L shell. Sensitivity of the PSD reconstruction to model errors statistics and advances of the improved physical-based model based on the model errors identification are illustrated by a number of representative examples of the PSD reanalysis. Identification of satellite observations characteristics, and filtration and smoothing algorithms are discussed in the companion paper.

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

Cited by 78 publications

References 36 publications

Space Weather: Physics, Effects and Predictability

Space Weather: Physics, Effects and Predictability

Electron radiation belt data assimilation with an ensemble Kalman filter relying on the Salammbô code

Noise statistics identification for Kalman filtering of the electron radiation belt observations I: Model errors

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