Comparative Accuracies of Models for Drag Prediction During Geomagnetically Disturbed Periods: A First Principles Model versus Empirical Models Article Swipe
R. L. Walterscheid
,
Margaret W. Chen
,
Chia-Chun Chao
,
Sierra Gegenheimer
,
Jehosafat Cabrera-Guzman
,
John P. McVey
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7186729
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7186729
This dataset contains observational and TIEGCM simulation data used for a manuscript that is being submitted to the Space Weather journal. The abstract for the study follows: We examine the accuracy of density prediction by the first principals model Thermosphere Ionsosphere Electrodynamics General Circulation Model (TIEGCM) developed by the National Center for Atmospheric Research and compare it to the accuracy of three empirical models: Jacchia 71, the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Extended 2000 (NRLMSIS), Jacchia 1971 and Jacchia-Bowman 2008. Comparisons are made for three large storms: the October 2003 storm, the March 2013 storm, and the March 2015 storm. To evaluate the accuracy of these models we use tracking data for nine space objects in low earth orbit (three for each storm). Additionally, and evaluate the accuracy of the TIEGCM and NRLMSIS with data from high precision accelerometers on the Challenging Minisatellite Payload (CHAMP) and Gravity field and Circulation Explorer (GOCE) satellites. The goal is to assess the use of a first principles model as a potential tool for forecasting satellite drag during large magnetic storms. We find that the TIEGCM accuracy is substantially better than for the Jacchia 71 and NRLMSIS models. The accuracies of the TIEGCM and JB2008 models are similar, but overall the TIEGCM is more accurate. We found smaller mean percentage differences for TIEGCM versus CHAMP than for NRLMIS for the Halloween Storm and smaller differences than results published for JB2008 and the assimilative model HASDM. The empirical models are at present more practical for operational purposes, but the first principles TIEGCM was developed as a research model and with a greater focus on operational use offers the potential for improved utility during stressing conditions.
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Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://zenodo.org/record/7186729
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393530222
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393530222Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.7186729Digital Object Identifier
- Title
-
Comparative Accuracies of Models for Drag Prediction During Geomagnetically Disturbed Periods: A First Principles Model versus Empirical ModelsWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-25Full publication date if available
- Authors
-
R. L. Walterscheid, Margaret W. Chen, Chia-Chun Chao, Sierra Gegenheimer, Jehosafat Cabrera-Guzman, John P. McVeyList of authors in order
- Landing page
-
https://zenodo.org/record/7186729Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://zenodo.org/record/7186729Direct OA link when available
- Concepts
-
Drag, Empirical modelling, Meteorology, Statistical physics, Climatology, Econometrics, Environmental science, Geology, Mathematics, Physics, Computer science, Mechanics, SimulationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 31, 60, 106, 130, 162, 198 |
| abstract_inverted_index.on | 141, 270 |
| abstract_inverted_index.to | 16, 57, 158 |
| abstract_inverted_index.we | 109 |
| abstract_inverted_index.71, | 65 |
| abstract_inverted_index.The | 21, 155, 196, 243 |
| abstract_inverted_index.and | 4, 54, 79, 97, 126, 133, 147, 150, 193, 201, 230, 238, 265 |
| abstract_inverted_index.are | 83, 204, 246 |
| abstract_inverted_index.but | 206, 254 |
| abstract_inverted_index.for | 9, 23, 51, 85, 113, 122, 171, 189, 219, 224, 226, 236, 251, 276 |
| abstract_inverted_index.low | 118 |
| abstract_inverted_index.the | 17, 24, 29, 35, 48, 58, 66, 89, 93, 98, 104, 128, 131, 142, 160, 182, 190, 199, 208, 227, 239, 255, 274 |
| abstract_inverted_index.use | 110, 161, 272 |
| abstract_inverted_index.was | 259 |
| abstract_inverted_index.1971 | 78 |
| abstract_inverted_index.2000 | 75 |
| abstract_inverted_index.2003 | 91 |
| abstract_inverted_index.2013 | 95 |
| abstract_inverted_index.2015 | 100 |
| abstract_inverted_index.Mass | 70 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.data | 7, 112, 136 |
| abstract_inverted_index.drag | 174 |
| abstract_inverted_index.each | 123 |
| abstract_inverted_index.find | 180 |
| abstract_inverted_index.from | 137 |
| abstract_inverted_index.goal | 156 |
| abstract_inverted_index.high | 138 |
| abstract_inverted_index.made | 84 |
| abstract_inverted_index.mean | 216 |
| abstract_inverted_index.more | 211, 249 |
| abstract_inverted_index.nine | 114 |
| abstract_inverted_index.than | 188, 223, 233 |
| abstract_inverted_index.that | 12, 181 |
| abstract_inverted_index.tool | 170 |
| abstract_inverted_index.used | 8 |
| abstract_inverted_index.with | 135, 266 |
| abstract_inverted_index.2008. | 81 |
| abstract_inverted_index.CHAMP | 222 |
| abstract_inverted_index.March | 94, 99 |
| abstract_inverted_index.Model | 44 |
| abstract_inverted_index.Naval | 67 |
| abstract_inverted_index.Storm | 229 |
| abstract_inverted_index.being | 14 |
| abstract_inverted_index.earth | 119 |
| abstract_inverted_index.field | 149 |
| abstract_inverted_index.first | 36, 164, 256 |
| abstract_inverted_index.focus | 269 |
| abstract_inverted_index.found | 214 |
| abstract_inverted_index.large | 87, 176 |
| abstract_inverted_index.model | 38, 166, 241, 264 |
| abstract_inverted_index.orbit | 120 |
| abstract_inverted_index.space | 115 |
| abstract_inverted_index.study | 25 |
| abstract_inverted_index.these | 107 |
| abstract_inverted_index.three | 61, 86 |
| abstract_inverted_index.(GOCE) | 153 |
| abstract_inverted_index.(three | 121 |
| abstract_inverted_index.Center | 50 |
| abstract_inverted_index.HASDM. | 242 |
| abstract_inverted_index.JB2008 | 202, 237 |
| abstract_inverted_index.NRLMIS | 225 |
| abstract_inverted_index.TIEGCM | 5, 132, 183, 200, 209, 220, 258 |
| abstract_inverted_index.assess | 159 |
| abstract_inverted_index.better | 187 |
| abstract_inverted_index.during | 175, 279 |
| abstract_inverted_index.models | 108, 203, 245 |
| abstract_inverted_index.offers | 273 |
| abstract_inverted_index.storm, | 92, 96 |
| abstract_inverted_index.storm. | 101 |
| abstract_inverted_index.versus | 221 |
| abstract_inverted_index.(CHAMP) | 146 |
| abstract_inverted_index.General | 42 |
| abstract_inverted_index.Gravity | 148 |
| abstract_inverted_index.Jacchia | 64, 77, 191 |
| abstract_inverted_index.NRLMSIS | 134, 194 |
| abstract_inverted_index.October | 90 |
| abstract_inverted_index.Payload | 145 |
| abstract_inverted_index.Scatter | 73 |
| abstract_inverted_index.compare | 55 |
| abstract_inverted_index.dataset | 1 |
| abstract_inverted_index.density | 32 |
| abstract_inverted_index.examine | 28 |
| abstract_inverted_index.greater | 268 |
| abstract_inverted_index.models. | 195 |
| abstract_inverted_index.models: | 63 |
| abstract_inverted_index.objects | 116 |
| abstract_inverted_index.overall | 207 |
| abstract_inverted_index.present | 248 |
| abstract_inverted_index.results | 234 |
| abstract_inverted_index.smaller | 215, 231 |
| abstract_inverted_index.storm). | 124 |
| abstract_inverted_index.storms. | 178 |
| abstract_inverted_index.storms: | 88 |
| abstract_inverted_index.utility | 278 |
| abstract_inverted_index.(TIEGCM) | 45 |
| abstract_inverted_index.Explorer | 152 |
| abstract_inverted_index.Extended | 74 |
| abstract_inverted_index.National | 49 |
| abstract_inverted_index.Research | 53, 68 |
| abstract_inverted_index.abstract | 22 |
| abstract_inverted_index.accuracy | 30, 59, 105, 129, 184 |
| abstract_inverted_index.contains | 2 |
| abstract_inverted_index.evaluate | 103, 127 |
| abstract_inverted_index.follows: | 26 |
| abstract_inverted_index.improved | 277 |
| abstract_inverted_index.journal. | 20 |
| abstract_inverted_index.magnetic | 177 |
| abstract_inverted_index.research | 263 |
| abstract_inverted_index.similar, | 205 |
| abstract_inverted_index.tracking | 111 |
| abstract_inverted_index.<em>Space | 18 |
| abstract_inverted_index.Halloween | 228 |
| abstract_inverted_index.accurate. | 212 |
| abstract_inverted_index.developed | 46, 260 |
| abstract_inverted_index.empirical | 62, 244 |
| abstract_inverted_index.potential | 169, 275 |
| abstract_inverted_index.practical | 250 |
| abstract_inverted_index.precision | 139 |
| abstract_inverted_index.published | 235 |
| abstract_inverted_index.purposes, | 253 |
| abstract_inverted_index.satellite | 173 |
| abstract_inverted_index.stressing | 280 |
| abstract_inverted_index.submitted | 15 |
| abstract_inverted_index.(NRLMSIS), | 76 |
| abstract_inverted_index.Incoherent | 72 |
| abstract_inverted_index.Laboratory | 69 |
| abstract_inverted_index.accuracies | 197 |
| abstract_inverted_index.manuscript | 11 |
| abstract_inverted_index.percentage | 217 |
| abstract_inverted_index.prediction | 33 |
| abstract_inverted_index.principals | 37 |
| abstract_inverted_index.principles | 165, 257 |
| abstract_inverted_index.simulation | 6 |
| abstract_inverted_index.Atmospheric | 52 |
| abstract_inverted_index.Challenging | 143 |
| abstract_inverted_index.Circulation | 43, 151 |
| abstract_inverted_index.Comparisons | 82 |
| abstract_inverted_index.Ionsosphere | 40 |
| abstract_inverted_index.conditions. | 281 |
| abstract_inverted_index.differences | 218, 232 |
| abstract_inverted_index.forecasting | 172 |
| abstract_inverted_index.operational | 252, 271 |
| abstract_inverted_index.satellites. | 154 |
| abstract_inverted_index.Spectrometer | 71 |
| abstract_inverted_index.Thermosphere | 39 |
| abstract_inverted_index.Weather</em> | 19 |
| abstract_inverted_index.assimilative | 240 |
| abstract_inverted_index.Additionally, | 125 |
| abstract_inverted_index.Minisatellite | 144 |
| abstract_inverted_index.observational | 3 |
| abstract_inverted_index.substantially | 186 |
| abstract_inverted_index.Jacchia-Bowman | 80 |
| abstract_inverted_index.accelerometers | 140 |
| abstract_inverted_index.Electrodynamics | 41 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |