L. C. Gardner

Physics‐based Data Assimilation (DA) has been shown to be a powerful technique for specifying and predicting space weather. However, it is also known that different data assimilation models simulating the same geophysical event can display different space weather features even if the same data are assimilated. In this study, we used our Multimodel Ensemble Prediction System (MEPS) of DA models to elucidate the similarities and differences in the individual DA model reconstructions of the mid‐low latitude ionosphere when the same data are assimilated. Ensemble model averages were also obtained. For this ensemble modeling study, we selected the quiet/storm period of 16 and 17 March 2013 (equinox, solar medium). Five data assimilation models and one physics‐based model were used to produce an ensemble mean output for Total Electron Content (TEC), ionospheric peak density (N_mF₂), and ionospheric peak height (h_mF₂) for latitudes less than 60° and all longitudes. The data assimilated included ground‐based Global Positioning Satellite TEC and topside plasma densities near 800 km altitude derived from the COSMIC (Constellation Observing System for Meteorology, Ionosphere, and Climate) satellites. Both a simple average and a weighted average of the models were used in the ensemble averaging in order to determine if there was an improvement of the ensemble averages over the individual models.

To address challenges of assessing space weather modeling capabilities, the Community Coordinated Modeling Center is leading a newly established International Forum for Space Weather Modeling Capabilities Assessment. This paper presents preliminary results of validation of modeled foF2 (F₂ layer critical frequency) and TEC (total electron content) during the first selected 2013 March storm event (17 March 2013). In this study, we used eight ionospheric models ranging from empirical to physics‐based, coupled ionosphere‐thermosphere and data assimilation models. The quantities we considered are TEC and foF2 changes and percentage changes compared to quiet time background, and the maximum and minimum percentage changes. In addition, we considered normalized percentage changes of TEC. We compared the modeled quantities with ground‐based observations of vertical Global Navigation Satellite System TEC (provided by Massachusetts Institute of Technology Haystack Observatory) and foF2 data (provided by Global Ionospheric Radio Observatory) at the 12 locations selected in middle latitudes of the American and European‐African longitude sectors. To quantitatively evaluate the models' performance, we calculated skill scores including correlation coefficient, root‐mean square error (RMSE), ratio of the modeled to observed maximum percentage changes (yield), and timing error. Our study indicates that average RMSEs of foF2 range from about 1 MHz to 1.5 MHz. The average RMSEs of TEC are between ~5 and ~10 TECU (1 TEC Unit = 10¹⁶ el/m²). dfoF2[%] RMSEs are between 15% and 25%, which is smaller than RMSE of dTEC[%] ranging from 30% to 60%. The performance of the models varies with the location and metrics considered.

The Utah State University Global Assimilation of Ionospheric Measurements‐Gauss Markov model has been used to investigate the distribution of ionospheric plasma during storm times over the continental United States. Storm periods dramatically increase the effects of space weather on the ionosphere and upper atmosphere, leading to impacts on over‐the‐horizon radars, Global Positioning System location determination, spacecraft charging, power grid overloads, and disruption of the Federal Aviation Administration Wide Area Augmentation System to name a few. Four storm periods were investigated where strong storm‐enhanced densities (SEDs) were present: two strong, October 2003 and November 2003, and two moderate, August 2010 and August 2011. It was found that a fundamental difference in the SED formation exists between the strong and moderate storms. For the strong storms, the SED was formed from the plasma in the northern equatorial anomaly crest, with the plasma in the SED channel lifting the closer it came to the high latitudes. For the moderate storms, the SED appeared to be unconnected to the northern anomaly crest but was rather produced locally in the SED channel, along with no corresponding increase in layer height associated with the SED evident in the model.

The Nowcast of Atmospheric Ionizing Radiation for Aviation Safety climatological model and the Automated Radiation Measurements for Aerospace Safety (ARMAS) statistical database are presented as polynomial fit equations. Using equations based on altitude, L shell, and geomagnetic conditions an effective dose rate for any location from a galactic cosmic ray (GCR) environment can be calculated. A subset of the ARMAS database is represented by a second polynomial fit equation for the GCR plus probable relativistic energetic particle (REP; Van Allen belt REP) effective dose rates within a narrow band of L shells with altitudinal and geomagnetic dependency. Solar energetic particle events are not considered in this study since our databases do not contain these events. This work supports a suggestion that there may be a REP contribution having an effect at aviation altitudes. The ARMAS database is rich in Western Hemisphere observations for L shells between 1.5 and 5; there have been many cases of enhanced radiation events possibly related to effects from radiation belt particles. Our work identifies that the combined effects of an enhanced radiation environment in this L shell range are typically 15% higher than the GCR background. We also identify applications for the equations representing the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety and ARMAS databases. They include (i) effective dose rate climatology in comparison with measured weather variability and (ii) climatological and statistical weather nowcasting and forecasting. These databases may especially help predict the radiation environment for regional air traffic management, for airport overflight operations, and for air carrier route operations of individual aircraft.

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground‐based GPS TEC measurements during the 2006 AGU storm event (14–15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics‐based, coupled ionosphere‐thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 10¹⁶el m⁻²) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.

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.

The Earth's ionosphere is a highly dynamic region that is almost constantly in a state of flux. Solar radiation, geomagnetic activity, chemical reactions, and natural dynamics all act to perturb the state of the ionosphere. The ionosphere changes on time scales of hours to days, with the fine‐scale ionospheric structures that are frequently observed lacking in global physics‐based models due to time step and spatial resolution constraints. To properly specify the ionosphere, data are needed, thus data assimilation. The Utah State University Global Assimilation of Ionospheric Measurements‐Gauss Markov (GAIM‐GM) model uses a data assimilation method to correct a physics‐based model of the ionosphere using five different data types, divided into nine different data sources. Multiple data types are necessary because the data from any individual data source will not be sufficient for global reconstructions. The GAIM‐GM specification (in real time) can then be used to correct for ionospheric propagation delays, thereby improving geolocation and communications. The focus here is to show the quantitative effects that multiple data types have on GAIM‐GM ionospheric specifications for a relatively quiet day (19 April) in 2012.

During high‐speed stream (HSS) events the solar wind speed increases, and the cross polar cap potential increases, leading to increased Joule heating at high latitudes. The heat input at high latitudes heats the polar regions, which then conducts to lower latitudes, producing global heating. The heating occurs during the risetime of the cross polar cap potential and throughout the period of high cross polar cap potential as seen in our simulation. These simulations are performed using the Utah State University global thermosphere model driven by Joule heating rates that are consistent with electric fields observed by DMSP‐15 observations of HSS events. Cooling occurs as the cross polar cap potential decreases and continues for several days after the cross polar cap potential has returned to background values. Polar cap ionospheric observations are compared to model simulations of heating and cooling, providing evidence that the thermospheric model is capturing the HSS energy input and the post‐HSS multiday return to pre‐HSS conditions. The HSS heating can be as high as 100 K (as seen from both the model and the data) at high latitudes, with a corresponding, but lower, global increase in thermospheric temperature.