Relativistic electron flux forecast at geostationary orbit using Kalman filter based on multivariate autoregressive model

Sakaguchi, Kaori; Miyoshi, Yoshizumi; Saito, Shinji; Nagatsuma, Tsutomu; Seki, K.; Murata, Ken T.

doi:10.1002/swe.20020

Cited by 31 publications

(51 citation statements)

References 42 publications

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“…The multivariate model is suitable for explaining the interaction among multiple time series, and thus, variations of radiation belt electron flux can be predicted on the basis of its correlation with changes in other conditions. The details of the model are described in a previous paper [ Sakaguchi et al ., ]. The formula for the representation is briefly introduced here.…”

Section: Multivariate Autoregressive (Ar) Modelmentioning

confidence: 99%

“…The Japanese space weather information center at the National Institute of Information and Communications Technology (NICT) [ Nagatsuma , ] has been providing relativistic electron flux prediction near GEO satellites since 2012 (http://seg-web.nict.go.jp/radi/en). Their prediction is based on a multivariate autoregressive (AR) model that calculates future log‐flux variations by accounting for a few days lagging response of the electron flux to several solar wind parameter changes [ Sakaguchi et al ., ].…”

Section: Introductionmentioning

confidence: 99%

“…This long, continuous time series of data represents a new opportunity for developing improved empirical prediction models throughout the equatorial radiation belt at L = 3–6 in MEO. For example, the multivariate AR method used for GEO [ Sakaguchi et al ., ] can now be applied to predict flux variations at each L value region in MEO.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

et al. 2015

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The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. To provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraft were used as the predictors. Model explanatory parameters were selected from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L ≧ 4.8 and L ≧ 5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≦ L ≦ 6, while for the GEO flux prediction, the K P index is better than Dst. A test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.

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Section: Multivariate Autoregressive (Ar) Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

et al. 2015

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“…Those energy range of electron flux is enhanced due to variations of radiation belt. So we have been developed prediction model of geomagnetic disturbances including substorm activities, and radiation belt electron flux [12].…”

Section: Model and User-oriented Informationmentioning

confidence: 99%

Geospace monitoring for space weather research and operation

Nagatsuma

2017

E3S Web Conf.

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Abstract. Geospace, a space surrounding the Earth, is one of the key area for space weather. Because geospace environment dynamically varies depending on the solar wind conditions. Many kinds of space assets are operating in geospace for practical purposes. Anomalies of space assets are sometimes happened because of space weather disturbances in geospace. Therefore, monitoring and forecasting of geospace environment is very important tasks for NICT's space weather research and development. To monitor and to improve forecasting model, fluxgate magnetometers and HF radars are operated by our laboratory, and its data are used for our research work, too. We also operate real-time data acquisition system for satellite data, such as DSCOVR, STEREO, and routinely received high energy particle data from Himawari-8. Based on these data, we are monitoring current condition of geomagnetic disturbances, and that of radiation belt. Using these data, we have developed empirical models for relativistic electron flux at GEO and inner magnetosphere. To provide userfriendly information , we are trying to develop individual spacecraft anomaly risk estimation tool based on combining models of space weather and those of spacecraft charging, Current status of geospace monitoring, forecasting, and research activities are introduced.

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“…It is well documented that changes in solar wind speed precede changes in electron flux by 1 to 3 days [ Baker et al , ; Li et al , ; Rigler et al , ; Reeves et al , ]. Historically, electron flux forecasting models have leveraged this relationship [ Baker et al , ; Li et al , ; Ukhorskiy et al , ; Sakaguchi et al , ; Boynton et al , ]. A simple model describing the mean relationship between log electron flux and 1 day lagged solar wind speed by a straight line is

F_{t} = β_{0} + β_{1} V_{t - 1} + ϵ_{t} .

…”

Section: Parameter Estimate Sensitivity To Training Datamentioning

confidence: 99%

Dynamic linear models for forecasting of radiation belt electrons and limitations on physical interpretation of predictive models

et al. 2014

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Key Points:• Static models are ill equipped to model dynamics in radiation belt • Models with many inputs are often nonuniquely interpretable • DLMs are attractive models for forecasting radiation belt dynamics Abstract Relationships exist between radiation belt electron flux intensities and solar drivers such as solar wind speed, ion density, and magnetic fields. The particulars of these relationships, however, are not well understood. Many forecasting models have been developed in the last 25 years, attempting to make sense of these relationships and produce accurate forecasts for electron flux intensities. We discuss some of the inherent limitations that many forecasting models (e.g., static models) possess when trying to untangle the intricate and dynamic relationships between electron flux levels and solar wind drivers. Dynamics related to the solar cycle limit physical interpretations for static forecasting models to customized and narrow time windows. Furthermore, the interrelatedness of solar drivers severely limit the ability to uniquely partition and describe the relationship between any one solar driver with electron flux levels. We suggest an alternate approach using dynamic linear models (DLMs). DLMs avoid some of the inherent limitations of physical understanding static models possess. We compare the 1 day ahead forecast accuracy of a relatively simple DLM to the current NOAA relativistic electron forecast model (REFM). The REFM does produce a more favorable prediction efficiency averaged across years when compared to the relatively simple DLM (0.749 to 0.721). However, the competitiveness of the DLM suggests that further development may lead to more accurate and interpretable models in the future.

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Relativistic electron flux forecast at geostationary orbit using Kalman filter based on multivariate autoregressive model

Cited by 31 publications

References 42 publications

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

Geospace monitoring for space weather research and operation

Dynamic linear models for forecasting of radiation belt electrons and limitations on physical interpretation of predictive models

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