{
"USGS Publications Warehouse": {
"@context": "https://schema.org",
"@type": "Article",
"additionalType": "Journal Article",
"name": "Operational nowcasting of electron flux levels in the outer zone of Earth's radiation belt",
"identifier": [
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse IndexID",
"value": "70202358",
"url": "https://pubs.usgs.gov/publication/70202358"
},
{
"@type": "PropertyValue",
"propertyID": "USGS Publications Warehouse Internal ID",
"value": 70202358
},
{
"@type": "PropertyValue",
"propertyID": "DOI",
"value": "10.1029/2017SW001788",
"url": "https://doi.org/10.1029/2017SW001788"
}
],
"journal": {
"@type": "Periodical",
"name": "Space Weather",
"volumeNumber": "16",
"issueNumber": "5"
},
"inLanguage": "en",
"isPartOf": [
{
"@type": "CreativeWorkSeries",
"name": "Space Weather"
}
],
"datePublished": "2018",
"dateModified": "2019-02-25",
"abstract": "We describe a lightweight, accurate nowcasting model for electron flux levels measured by the Van Allen probes. Largely motivated by Rigler et al. (2004,\u00a0https://doi.org/10.1029/2003SW000036), we turn to a time\u2010varying linear filter of previous flux levels and\u00a0Kp. We train and test this model on data gathered from the 2.10 MeV channel of the Relativistic Electron\u2010Proton Telescope sensor onboard the Van Allen probes. Dynamic linear models are a specific case of state space models and can be made flexible enough to emulate the nonlinear behavior of particle fluxes within the radiation belts. Real\u2010time estimation of the parameters of the model is done using a Kalman filter, where the state of the model is exactly the parameters. Nowcast performance is assessed against several baseline interpolation schemes. Our model demonstrates significant improvements in performance over persistence nowcasting. In particular, during times of high geomagnetic activity, our model is able to attain performance substantially better than a persistence model. In addition, residual analysis is conducted in order to assess model fit and to suggest future improvements to the model.",
"description": "18 p.",
"publisher": {
"@type": "Organization",
"name": "AGU"
},
"author": [
{
"@type": "Person",
"name": "Coleman, Tim",
"givenName": "Tim",
"familyName": "Coleman"
},
{
"@type": "Person",
"name": "McCollough, James P.",
"givenName": "James P.",
"familyName": "McCollough"
},
{
"@type": "Person",
"name": "Young, Shawn L.",
"givenName": "Shawn L.",
"familyName": "Young"
},
{
"@type": "Person",
"name": "Rigler, E. Joshua erigler@usgs.gov",
"givenName": "E. Joshua",
"familyName": "Rigler",
"email": "erigler@usgs.gov",
"identifier": {
"@type": "PropertyValue",
"propertyID": "ORCID",
"value": "0000-0003-4850-3953",
"url": "https://orcid.org/0000-0003-4850-3953"
},
"affiliation": [
{
"@type": "Organization",
"name": "Geologic Hazards Science Center",
"url": "https://www.usgs.gov/centers/geologic-hazards-science-center"
}
]
}
],
"funder": [
{
"@type": "Organization",
"name": "Geologic Hazards Science Center",
"url": "https://www.usgs.gov/centers/geologic-hazards-science-center"
}
]
}
}