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Identifying the radiation belt source region by data assimilation
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| Zeitschriftentitel: | Journal of Geophysical Research: Space Physics |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Journal of Geophysical Research: Space Physics, 112, 2007, A6 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Geophysical Union (AGU)
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| Schlagwörter: |
| author_facet |
Koller, J. Chen, Y. Reeves, G. D. Friedel, R. H. W. Cayton, T. E. Vrugt, J. A. Koller, J. Chen, Y. Reeves, G. D. Friedel, R. H. W. Cayton, T. E. Vrugt, J. A. |
|---|---|
| author |
Koller, J. Chen, Y. Reeves, G. D. Friedel, R. H. W. Cayton, T. E. Vrugt, J. A. |
| spellingShingle |
Koller, J. Chen, Y. Reeves, G. D. Friedel, R. H. W. Cayton, T. E. Vrugt, J. A. Journal of Geophysical Research: Space Physics Identifying the radiation belt source region by data assimilation Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics |
| author_sort |
koller, j. |
| spelling |
Koller, J. Chen, Y. Reeves, G. D. Friedel, R. H. W. Cayton, T. E. Vrugt, J. A. 0148-0227 American Geophysical Union (AGU) Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics http://dx.doi.org/10.1029/2006ja012196 <jats:p>We describe how assimilation of radiation belt data with a simple radial diffusion code can be used to identify and adjust for unknown physics in the model. We study the dropout and the following enhancement of relativistic electrons during a moderate storm on 25 October 2002. We introduce a technique that uses an ensemble Kalman filter and the probability distribution of the forecast ensemble to identify if the model is drifting away from the observations and to find inconsistencies between model forecast and observations. We use the method to pinpoint the time periods and locations where most of the disagreement occurs and how much the Kalman filter has to adjust the model state to match the observations. Although the model does not contain explicit source or loss terms, the Kalman filter algorithm can implicitly add very localized sources or losses in order to reduce the discrepancy between model and observations. We use this technique with multisatellite observations to determine when simple radial diffusion is inconsistent with the observed phase space densities indicating where additional source (acceleration) or loss (precipitation) processes must be active. We find that the outer boundary estimated by the ensemble Kalman filter is consistent with negative phase space density gradients in the outer electron radiation belt. We also identify specific regions in the radiation belts (<jats:italic>L</jats:italic>* ≈ 5–6 and to a minor extend also <jats:italic>L</jats:italic>* ≈ 4) where simple radial diffusion fails to adequately capture the variability of the observations, suggesting local acceleration/loss mechanisms.</jats:p> Identifying the radiation belt source region by data assimilation Journal of Geophysical Research: Space Physics |
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10.1029/2006ja012196 |
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Online Free |
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Technik Geologie und Paläontologie Geographie Chemie und Pharmazie Land- und Forstwirtschaft, Gartenbau, Fischereiwirtschaft, Hauswirtschaft Biologie Allgemeine Naturwissenschaft Physik |
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| title |
Identifying the radiation belt source region by data assimilation |
| title_unstemmed |
Identifying the radiation belt source region by data assimilation |
| title_full |
Identifying the radiation belt source region by data assimilation |
| title_fullStr |
Identifying the radiation belt source region by data assimilation |
| title_full_unstemmed |
Identifying the radiation belt source region by data assimilation |
| title_short |
Identifying the radiation belt source region by data assimilation |
| title_sort |
identifying the radiation belt source region by data assimilation |
| topic |
Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics |
| url |
http://dx.doi.org/10.1029/2006ja012196 |
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2007 |
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<jats:p>We describe how assimilation of radiation belt data with a simple radial diffusion code can be used to identify and adjust for unknown physics in the model. We study the dropout and the following enhancement of relativistic electrons during a moderate storm on 25 October 2002. We introduce a technique that uses an ensemble Kalman filter and the probability distribution of the forecast ensemble to identify if the model is drifting away from the observations and to find inconsistencies between model forecast and observations. We use the method to pinpoint the time periods and locations where most of the disagreement occurs and how much the Kalman filter has to adjust the model state to match the observations. Although the model does not contain explicit source or loss terms, the Kalman filter algorithm can implicitly add very localized sources or losses in order to reduce the discrepancy between model and observations. We use this technique with multisatellite observations to determine when simple radial diffusion is inconsistent with the observed phase space densities indicating where additional source (acceleration) or loss (precipitation) processes must be active. We find that the outer boundary estimated by the ensemble Kalman filter is consistent with negative phase space density gradients in the outer electron radiation belt. We also identify specific regions in the radiation belts (<jats:italic>L</jats:italic>* ≈ 5–6 and to a minor extend also <jats:italic>L</jats:italic>* ≈ 4) where simple radial diffusion fails to adequately capture the variability of the observations, suggesting local acceleration/loss mechanisms.</jats:p> |
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| author | Koller, J., Chen, Y., Reeves, G. D., Friedel, R. H. W., Cayton, T. E., Vrugt, J. A. |
| author_facet | Koller, J., Chen, Y., Reeves, G. D., Friedel, R. H. W., Cayton, T. E., Vrugt, J. A., Koller, J., Chen, Y., Reeves, G. D., Friedel, R. H. W., Cayton, T. E., Vrugt, J. A. |
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| description | <jats:p>We describe how assimilation of radiation belt data with a simple radial diffusion code can be used to identify and adjust for unknown physics in the model. We study the dropout and the following enhancement of relativistic electrons during a moderate storm on 25 October 2002. We introduce a technique that uses an ensemble Kalman filter and the probability distribution of the forecast ensemble to identify if the model is drifting away from the observations and to find inconsistencies between model forecast and observations. We use the method to pinpoint the time periods and locations where most of the disagreement occurs and how much the Kalman filter has to adjust the model state to match the observations. Although the model does not contain explicit source or loss terms, the Kalman filter algorithm can implicitly add very localized sources or losses in order to reduce the discrepancy between model and observations. We use this technique with multisatellite observations to determine when simple radial diffusion is inconsistent with the observed phase space densities indicating where additional source (acceleration) or loss (precipitation) processes must be active. We find that the outer boundary estimated by the ensemble Kalman filter is consistent with negative phase space density gradients in the outer electron radiation belt. We also identify specific regions in the radiation belts (<jats:italic>L</jats:italic>* ≈ 5–6 and to a minor extend also <jats:italic>L</jats:italic>* ≈ 4) where simple radial diffusion fails to adequately capture the variability of the observations, suggesting local acceleration/loss mechanisms.</jats:p> |
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| imprint_str_mv | American Geophysical Union (AGU), 2007 |
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| language | English |
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| publishDate | 2007 |
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| spelling | Koller, J. Chen, Y. Reeves, G. D. Friedel, R. H. W. Cayton, T. E. Vrugt, J. A. 0148-0227 American Geophysical Union (AGU) Paleontology Space and Planetary Science Earth and Planetary Sciences (miscellaneous) Atmospheric Science Earth-Surface Processes Geochemistry and Petrology Soil Science Water Science and Technology Ecology Aquatic Science Forestry Oceanography Geophysics http://dx.doi.org/10.1029/2006ja012196 <jats:p>We describe how assimilation of radiation belt data with a simple radial diffusion code can be used to identify and adjust for unknown physics in the model. We study the dropout and the following enhancement of relativistic electrons during a moderate storm on 25 October 2002. We introduce a technique that uses an ensemble Kalman filter and the probability distribution of the forecast ensemble to identify if the model is drifting away from the observations and to find inconsistencies between model forecast and observations. We use the method to pinpoint the time periods and locations where most of the disagreement occurs and how much the Kalman filter has to adjust the model state to match the observations. Although the model does not contain explicit source or loss terms, the Kalman filter algorithm can implicitly add very localized sources or losses in order to reduce the discrepancy between model and observations. We use this technique with multisatellite observations to determine when simple radial diffusion is inconsistent with the observed phase space densities indicating where additional source (acceleration) or loss (precipitation) processes must be active. We find that the outer boundary estimated by the ensemble Kalman filter is consistent with negative phase space density gradients in the outer electron radiation belt. We also identify specific regions in the radiation belts (<jats:italic>L</jats:italic>* ≈ 5–6 and to a minor extend also <jats:italic>L</jats:italic>* ≈ 4) where simple radial diffusion fails to adequately capture the variability of the observations, suggesting local acceleration/loss mechanisms.</jats:p> Identifying the radiation belt source region by data assimilation Journal of Geophysical Research: Space Physics |
| spellingShingle | Koller, J., Chen, Y., Reeves, G. D., Friedel, R. H. W., Cayton, T. E., Vrugt, J. A., Journal of Geophysical Research: Space Physics, Identifying the radiation belt source region by data assimilation, Paleontology, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous), Atmospheric Science, Earth-Surface Processes, Geochemistry and Petrology, Soil Science, Water Science and Technology, Ecology, Aquatic Science, Forestry, Oceanography, Geophysics |
| title | Identifying the radiation belt source region by data assimilation |
| title_full | Identifying the radiation belt source region by data assimilation |
| title_fullStr | Identifying the radiation belt source region by data assimilation |
| title_full_unstemmed | Identifying the radiation belt source region by data assimilation |
| title_short | Identifying the radiation belt source region by data assimilation |
| title_sort | identifying the radiation belt source region by data assimilation |
| title_unstemmed | Identifying the radiation belt source region by data assimilation |
| topic | Paleontology, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous), Atmospheric Science, Earth-Surface Processes, Geochemistry and Petrology, Soil Science, Water Science and Technology, Ecology, Aquatic Science, Forestry, Oceanography, Geophysics |
| url | http://dx.doi.org/10.1029/2006ja012196 |