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Space weather forecasting with a Multimodel Ensemble Prediction System (MEPS)
Author(s) -
Schunk R. W.,
Scherliess L.,
Eccles V.,
Gardner L. C.,
Sojka J. J.,
Zhu L.,
Pi X.,
Mannucci A. J.,
Butala M.,
Wilson B. D.,
Komjathy A.,
Wang C.,
Rosen G.
Publication year - 2016
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1002/2015rs005888
Subject(s) - data assimilation , space weather , ionosphere , meteorology , ensemble forecasting , thermosphere , plasmasphere , computer science , numerical weather prediction , weather forecasting , environmental science , spacecraft , geography , aerospace engineering , physics , geophysics , magnetosphere , plasma , quantum mechanics , engineering
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.