Comparing geosynchronous relativistic electron prediction models

Perry, K. L.; Ginet, G. P.; Ling, A. G.; Hilmer, R. V.

doi:10.1029/2010sw000581

Cited by 20 publications

(25 citation statements)

References 22 publications

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“…This model assumes no particular acceleration method but only recognizes that the solar wind is the ultimate driver of relativistic electron flux variations. Finally, Perry et al [2010] conducted a comparative study of three different outer belt forecasts: NOAA's REFM, Li [2004], and the Air Force's FluxPred model [ Ling et al , 2010]. FluxPred is a neural network model using 17 inputs consisting of the past 10 days of GEO flux data and 7 days of Kp.…”

Section: Introductionmentioning

confidence: 99%

An improved forecast system for relativistic electrons at geosynchronous orbit

Turner

Roziers³

et al. 2011

Space Weather

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Here we provide a review of existing forecast models for Earth's outer radiation belt electrons, discuss some recent improvements to two of these models, and present a new and improved forecast system for relativistic electrons at GEO. For the first time, we can forecast at a local hour resolution around GEO using a statistical tool included in the system. This forecast system also includes several real‐time forecast models, two previously existing and one that is a new development. This new model incorporates an internal electron source, simulating local acceleration by wave‐particle interactions, and it proves to be the most accurate of the models in the system. For 2007–2008, it achieves +1 and +2 day prediction efficiencies of 0.90 and 0.63. We conclude this work with a discussion of how these models are currently operational and providing results to the community online in real time, and we also speculate on future possibilities to allow for forecasts with extended lead times and ranges throughout the rest of the outer radiation belt.

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Section: Introductionmentioning

confidence: 99%

An improved forecast system for relativistic electrons at geosynchronous orbit

Turner

Roziers³

et al. 2011

Space Weather

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“…By fitting Model separately for each year, we are able to detect that parameter estimates change over time. The time‐varying functional relationship between log electron flux and solar wind speed has been noted in the literature [e.g., Reeves et al , ; Perry et al , ; Turner et al , ; Kellerman et al , ]. Reeves et al [] graphically examined the long‐term variations between solar wind speed and log electron flux and concluded that the functional relationship between these two variables is not constant in time.…”

Section: Parameter Estimate Sensitivity To Training Datamentioning

confidence: 99%

“…Reeves et al [] graphically examined the long‐term variations between solar wind speed and log electron flux and concluded that the functional relationship between these two variables is not constant in time. Perry et al [] compared the accuracy of models forecasting log electron flux between 1996 and 2008. They note that the worst forecasts occurred during solar maximum (second half of 1999 through first half of 2002), better forecasts during the declining years of solar minimum (second half of 2002 through 2008), and their best forecasts during the inclining years of solar minimum (1996 through first half of 1999).…”

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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“…As a check of how well the model performs in relation to other models, the performance of the FLUXPRED model is compared with that of the REFM model, a fluence forecasting model which is based on a linear prediction filter algorithm developed by Baker et al [1990] and refined by the Space Weather Prediction Center of the National Oceanic and Atmospheric Administration (NOAA/SWPC) (http://www.swpc.noaa.gov/refm/REFMDoc.html). A more comprehensive comparison with the REFM model and other models will be discussed in a companion paper [ Perry et al , 2010]. The REFM model was run with input consisting of 30 days of 5 minute > 2 MeV electron flux data and 30 days of 1 h solar wind data.…”

Section: Model Performancementioning

confidence: 99%

A neural network-based geosynchronous relativistic electron flux forecasting model

et al. 2010

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A multilayer feed‐forward neural network model has been developed to forecast >2 MeV electron flux at geosynchronous orbit. The model uses as input 10 consecutive days of historical electron flux values and 7 consecutive days of daily summed values of the planetary Kp index with two neurons in a single hidden layer. Development of the model is discussed in which the size of the training set interval and the retraining period are investigated. Problems associated with neuron saturation which limit the ability of the network to generalize are shown to be circumvented through a daily retraining regimen. The model performance is evaluated for the period 1998–2008 and compared with the results produced by the REFM model. The neural network model is demonstrated to perform quite well relative to the REFM model for this time period, producing mean prediction efficiencies for 6 month test intervals of 0.71, 0.49, and 0.31 for 1 day, 2 day, and 3 day forecasts, respectively.

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Comparing geosynchronous relativistic electron prediction models

Cited by 20 publications

References 22 publications

An improved forecast system for relativistic electrons at geosynchronous orbit

An improved forecast system for relativistic electrons at geosynchronous orbit

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

A neural network-based geosynchronous relativistic electron flux forecasting model

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