Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)

The goal of the Multimodel Ensemble Prediction System (MEPS) program is to improve space weather specification and forecasting with ensemble modeling. Space weather can have detrimental effects on a variety of civilian and military systems and operations, and many of the applications pertain to the ionosphere and upper atmosphere. Space weather can affect over‐the‐horizon radars, HF communications, surveying and navigation systems, surveillance, spacecraft charging, power grids, pipelines, and the Federal Aviation Administration (FAA's) Wide Area Augmentation System (WAAS). Because of its importance, numerous space weather forecasting approaches are being pursued, including those involving empirical, physics‐based, and data assimilation models. Clearly, if there are sufficient data, the data assimilation modeling approach is expected to be the most reliable, but different data assimilation models can produce different results. Therefore, like the meteorology community, we created a Multimodel Ensemble Prediction System (MEPS) for the Ionosphere‐Thermosphere‐Electrodynamics (ITE) system that is based on different data assimilation models. The MEPS ensemble is composed of seven physics‐based data assimilation models for the ionosphere, ionosphere‐plasmasphere, thermosphere, high‐latitude ionosphere‐electrodynamics, and middle to low latitude ionosphere‐electrodynamics. Hence, multiple data assimilation models can be used to describe each region. A selected storm event that was reconstructed with four different data assimilation models covering the middle and low latitude ionosphere is presented and discussed. In addition, the effect of different data types on the reconstructions is shown.

[1]  Xiaoqing Pi,et al.  Development of the Global Assimilative Ionospheric Model , 2004 .

[2]  Xiaoqing Pi,et al.  A performance evaluation of the operational Jet Propulsion Laboratory/University of Southern California Global Assimilation Ionospheric Model (JPL/USC GAIM) , 2005 .

[3]  Robert W. Schunk,et al.  The Utah State University Gauss-Markov Kalman Filter of the Ionosphere: The Effect of Slant TEC and Electron Density Profile Data on Model Fidelity , 2006 .

[4]  Xiaoqing Pi,et al.  Assimilative Modeling of Ionospheric Disturbances with FORMOSAT-3/COSMIC and Ground-Based GPS Measurements , 2009 .

[5]  Timothy Fuller-Rowell,et al.  Global Assimilation of Ionospheric Measurements (GAIM) , 2001 .

[6]  R. Schunk,et al.  Validation study of the Ionosphere Forecast Model using the TOPEX total electron content measurements , 2006 .

[7]  J. Sojka Global scale, physical models of the F region ionosphere , 1989 .

[8]  Xiaoqing Pi,et al.  COSMIC GPS Ionospheric Sensing and Space Weather , 2000 .

[9]  R. Schunk,et al.  Plasmasphere and upper ionosphere contributions and corrections during the assimilation of GPS slant TEC , 2009 .

[10]  Xiaoqing Pi,et al.  Assimilative modeling of low latitude ionosphere , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[11]  B. Wilson,et al.  An adjoint method based approach to data assimilation for a distributed parameter model for the ionosphere , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[12]  R. Schunk,et al.  Duration of an ionospheric data assimilation initialization of a coupled thermosphere‐ionosphere model , 2007 .

[13]  B. Wilson,et al.  Ensemble Modeling with Data Assimilation Models: A New Strategy for Space Weather Specifications, Forecasts, and Science , 2014 .

[14]  Robert W. Schunk,et al.  Development of a physics‐based reduced state Kalman filter for the ionosphere , 2004 .

[15]  R. Schunk,et al.  The USU-GAIM-FP data assimilation model for ionospheric specifications and forecasts , 2014, 2017 XXXIInd General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS).

[16]  Robert W. Schunk,et al.  Expanded Capabilities for the Ionospheric Forecast Model , 1997 .

[17]  Robert W. Schunk,et al.  Global Assimilation of Ionospheric Measurements‐Gauss Markov model: Improved specifications with multiple data types , 2014 .

[18]  Xiaoqing Pi,et al.  Estimation of E × B drift using a global assimilative ionospheric model: An observation system simulation experiment , 2003 .

[19]  R. Schunk,et al.  Data Assimilation Models: A ‘New’ Tool for Ionospheric Science and Applications , 2011 .

[20]  Xiaoqing Pi,et al.  Data assimilation of ground GPS total electron content into a physics‐based ionospheric model by use of the Kalman filter , 2004 .

[21]  R. Schunk,et al.  Assessing models for ionospheric weather specifications over Australia during the 2004 Climate and Weather of the Sun-Earth-System (CAWSES) campaign , 2007 .

[22]  Robert W. Schunk,et al.  Importance of data assimilation technique in defining the model drivers for the space weather specification of the high‐latitude ionosphere , 2012 .

[23]  D. Pancheva,et al.  Aeronomy of the earth's atmosphere and ionosphere , 2011 .

[24]  S. Solomon,et al.  Driving the TING model with GAIM electron densities: Ionospheric effects on the thermosphere , 2008 .

[25]  Robert W. Schunk,et al.  USU global ionospheric data assimilation models , 2004, SPIE Optics + Photonics.

[26]  Robert W. Schunk,et al.  An Operational Data Assimilation Model of the Global Ionosphere , 2005 .

[27]  Robert W. Schunk,et al.  Ionospheric Weather Forecasting on the Horizon , 2005 .

[28]  R. Schunk A mathematical model of the middle and high latitude ionosphere , 1988 .

[29]  R. Schunk,et al.  Ionospheric Reconstructions for Various Solar, Seasonal, and Geomagnetic Conditions Obtained from the Global Assimilation of Ionospheric Measurements – Gauss Markov (GAIM-GM) Model , 2014 .

[30]  Robert W. Schunk,et al.  Utah State University Global Assimilation of Ionospheric Measurements Gauss‐Markov Kalman filter model of the ionosphere: Model description and validation , 2006 .

[31]  Robert W. Schunk,et al.  Ionospheric dynamics and drivers obtained from a physics‐based data assimilation model , 2009 .

[32]  R. Schunk,et al.  Ionosphere Data Assimilation: Problems Associated with Missing Physics , 2011 .

[33]  Xiaoqing Pi,et al.  JPL/USC GAIM: On the impact of using COSMIC and ground‐based GPS measurements to estimate ionospheric parameters , 2010 .