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

Abstract

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.

Assimilation of Multiple Data Types to a Regional Ionosphere Model With a 3D‐Var Algorithm (IDA4D)

“…Examples of data assimilation schemes include Kalman filter (Lee et al, 2012;Lin et al, 2015;Scherliess et al, 2004Scherliess et al, , 2006Schunk et al, 2004;Thompson et al, 2006;Yue, Wan, Liu, Zheng, et al, 2007;Yue et al, 2011Yue et al, , 2012, optimal interpolation (Gerzen et al, 2015), a three-dimensional variational (3D-Var) (Aa et al, 2016;Bust et al, 2001Bust et al, , 2004Bust et al, , 2007Bust & Datta-Barua, 2014), and four-dimensional variational (4D-Var, Pi et al, 2003;Ssessanga et al, 2018;Wang et al, 2004). Some of the matured data assimilation schemes have been tested and analyzed to the extent of being included in operational services: Utah State University (USU) Global Assimilation of Ionospheric Measurements (USU-GAIM) algorithm, University of Southern California and Jet Propulsion Laboratory Global Assimilative Ionospheric Model (GAIM), and Applied Research Laboratories, University of Texas at Austin Ionospheric Data Assimilation Four-Dimensional (IDA4D) algorithm (e.g., Bust et al, 2004Bust et al, , 2007Bust & Datta-Barua, 2014;McNamara et al, 2011;Schunk et al, 2003Schunk et al, , 2016Thompson et al, 2006). To provide a more accurate nowcast and forecast of ionospheric weather, these schemes ingest different data types that may include slant total electron content (STEC) from ground-based Global Positioning System (GPS); STEC from radio occultation using Low Earth Orbit satellites; and electron density from ionosonde, in situ and incoherent scatter radar measurements.…”

Assessment of the Impact of FORMOSAT‐7/COSMIC‐2 GNSS RO Observations on Midlatitude and Low‐Latitude Ionosphere Specification: Observing System Simulation Experiments Using Ensemble Square Root Filter

“…The Global Assimilation of Ionospheric Measurement (GAIM) developed by Utah State University (USU GAIM) is another well-recognized ionospheric data assimilation system. This system has two different data assimilation approaches, the USU GAIM-GM (Scherliess et al, 2006;Schunk et al, 2004Schunk et al, , 2016 and the USU GAIM-FP (Scherliess, Thompson, & Schunk, 2009;Schunk et al, 2016). The model used in the USU GAIM-GM is the Ionosphere Forecast Model (IFM), and the data assimilation scheme is Gauss-Markov Kalman filter.…”

“…The data assimilation scheme used in the USU GAIM-FP is ensemble Kalman filter (Scherliess, Thompson, & Schunk, 2009;Schunk et al, 2004). Both the USU GAIM-GM and the USU GAIM-FP can assimilate TEC data into models adequately (Scherliess et al, 2006;Scherliess, Thompson, & Schunk, 2009;Schunk et al, 2016).…”

Space Weather Quarterly Volume 16, Issue 4, 2019

“…In addition, there is a strong connection between the lower atmosphere state and the ionosphere that was highlighted initially by Immel et al (2006) and demonstrated in later modeling studies (e.g., Pedatella et al, 2016). Furthermore, data assimilation (DA) schemes are already used for operational ionosphere models (e.g., Schunk et al, 2016), and experimental systems show that assimilation can improve model initial conditions in the thermosphere (e.g., Murray et al, 2015), the magnetosphere (e.g., Merkin et al, 2016), and the heliosphere (e.g., Lang & Owenst, 2019).…”