TY - JOUR
T1 - Local ensemble transform Kalman filter for ionospheric data assimilation
T2 - Observation influence analysis during a geomagnetic storm event
AU - Durazo, Juan A.
AU - Kostelich, Eric
AU - Mahalov, Alex
N1 - Funding Information:
The TIEGCM was obtained from the High-Altitude Observatory, NCAR (http://www.hao.ucar.edu/ modeling/tgcm/tie.php). Data files containing solar and geomagnetic parameters used in this paper are published by NOAA and are packaged in the TIEGCM code. The COSMIC data information was obtained from the COSMIC Data Analysis and Archive Center (CDAAC) at UCAR (http://cdaac-www.cosmic.ucar.edu). This material is based upon work supported by the Air Force Office of Scientific Research under the award FA9550-15-1-0096. J.D. was also sup ported in part by the National Science Foundation grant DMS-0940314. The authors thank the anonymous referees for their careful reading of the manuscript and for many useful suggestions.
Publisher Copyright:
©2017. The Authors.
PY - 2017/9
Y1 - 2017/9
N2 - We propose a targeted observation strategy, based on the influence matrix diagnostic, that optimally selects where additional observations may be placed to improve ionospheric forecasts. This strategy is applied in data assimilation observing system experiments, where synthetic electron density vertical profiles, which represent those of Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa satellite 3, are assimilated into the Thermosphere-Ionosphere-Electrodynamics General Circulation Model using the local ensemble transform Kalman filter during the 26 September 2011 geomagnetic storm. During each analysis step, the observation vector is augmented with five synthetic vertical profiles optimally placed to target electron density errors, using our targeted observation strategy. Forecast improvement due to assimilation of augmented vertical profiles is measured with the root-mean-square error (RMSE) of analyzed electron density, averaged over 600 km regions centered around the augmented vertical profile locations. Assimilating vertical profiles with targeted locations yields about 60%–80% reduction in electron density RMSE, compared to a 15% average reduction when assimilating randomly placed vertical profiles. Assimilating vertical profiles whose locations target the zonal component of neutral winds (Un) yields on average a 25% RMSE reduction in Un estimates, compared to a 2% average improvement obtained with randomly placed vertical profiles. These results demonstrate that our targeted strategy can improve data assimilation efforts during extreme events by detecting regions where additional observations would provide the largest benefit to the forecast.
AB - We propose a targeted observation strategy, based on the influence matrix diagnostic, that optimally selects where additional observations may be placed to improve ionospheric forecasts. This strategy is applied in data assimilation observing system experiments, where synthetic electron density vertical profiles, which represent those of Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa satellite 3, are assimilated into the Thermosphere-Ionosphere-Electrodynamics General Circulation Model using the local ensemble transform Kalman filter during the 26 September 2011 geomagnetic storm. During each analysis step, the observation vector is augmented with five synthetic vertical profiles optimally placed to target electron density errors, using our targeted observation strategy. Forecast improvement due to assimilation of augmented vertical profiles is measured with the root-mean-square error (RMSE) of analyzed electron density, averaged over 600 km regions centered around the augmented vertical profile locations. Assimilating vertical profiles with targeted locations yields about 60%–80% reduction in electron density RMSE, compared to a 15% average reduction when assimilating randomly placed vertical profiles. Assimilating vertical profiles whose locations target the zonal component of neutral winds (Un) yields on average a 25% RMSE reduction in Un estimates, compared to a 2% average improvement obtained with randomly placed vertical profiles. These results demonstrate that our targeted strategy can improve data assimilation efforts during extreme events by detecting regions where additional observations would provide the largest benefit to the forecast.
KW - Kalman filter
KW - TIEGCM
KW - data assimilation
KW - geomagnetic storm
KW - targeted observations
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U2 - 10.1002/2017JA024274
DO - 10.1002/2017JA024274
M3 - Article
AN - SCOPUS:85049201747
SN - 2169-9380
VL - 122
SP - 9652
EP - 9669
JO - Journal of Geophysical Research A: Space Physics
JF - Journal of Geophysical Research A: Space Physics
IS - 9
ER -