Ionospheric storms can have important effects on radio communications and navigation systems.Storm time ionospheric predictions have the potential to form part of effective mitigation strategies to these problems. Ionospheric storms are caused by strong forcing from the solar wind. Electron density enhancements are driven by penetration electric fields, as well as by thermosphere-ionosphere behavior including Traveling Atmospheric Disturbances and Traveling Ionospheric Disturbances and changes to the neutral composition. This study assesses the effect on 1 h predictions of specifying initial ionospheric and thermospheric conditions using total electron content (TEC) observations under a fixed set of solar and high-latitude drivers. Prediction performance is assessed against TEC observations, incoherent scatter radar, and in situ electron density observations. Corotated TEC data provide a benchmark of forecast accuracy. The primary case study is the storm of 10 September 2005, while the anomalous storm of 21 January 2005 provides a secondary comparison. The study uses an ensemble Kalman filter constructed with the Data Assimilation Research Testbed and the Thermosphere Ionosphere Electrodynamics General Circulation Model. Maps of preprocessed, verticalized GPS TEC are assimilated, while high-latitude specifications from the Assimilative Mapping of Ionospheric Electrodynamics and solar flux observations from the Solar Extreme Ultraviolet Experiment are used to drive the model. The filter adjusts ionospheric and thermospheric parameters, making use of time-evolving covariance estimates. The approach is effective in correcting model biases but does not capture all the behavior of the storms. In particular, a ridge-like enhancement over the continental USA is not predicted, indicating the importance of predicting storm time electric field behavior to the problem of ionospheric forecasting.

Dense, fast‐moving regions of ionization called polar cap patches are known to occur in the high‐latitude F region ionosphere. Patches are widely believed to be caused by convection of dense, sunlit plasma into a dark and therefore low‐density polar cap ionosphere. This leads to the belief that patches are a winter phenomenon. Surprisingly, a long‐term analysis of 3 years of ionospheric measurements from the Swarm satellites shows that large density enhancements occur far more frequently in local summer than local winter in the Southern Hemisphere (SH). The reverse is true in the Northern Hemisphere (NH). Previously reported patch detections in the SH are reexamined. Detection algorithms using only a relative doubling test count very small density fluctuations in SH winter due to extremely low ambient densities found there, while much larger enhancements occurring in SH summer are missed due to especially high ambient densities. The same problem does not afflict results in the NH, where ambient densities are more stable year‐round due to the ionospheric annual asymmetry. Given this new analysis, the definition of a patch as a doubling of the ambient density is not suitable for the SH. We propose a test for patches linked to long‐term averaged solar flux activity, characterized by the 81 day centered mean F10.7 index. Importantly, the current patch formation theory is at least incomplete in that it does not predict the observed lack of patches in SH winter, or the many large enhancements seen in SH summer.

In the high‐latitude ionosphere, predicting transport of polar cap patches is important because of their impact on radio communications and navigation systems. Lagrangian coherent structures (LCSs) are barriers to transport in nonlinear time‐varying flow fields, found by computing the local maximum finite‐time Lyapunov exponent (FTLE). We propose that LCSs are barriers governing patch formation. In this work, we compute and visualize the LCSs in high‐latitude ionospheric convection by computing the FTLE field with the Ionosphere‐Thermosphere Algorithm for Lagrangian Coherent Structures (ITALCS). The Weimer 2005 high‐latitude electric potential model and the 12th‐generation International Geomagnetic Reference Field (IGRF‐12) are used to generate the trueE→×trueB→ drift field at each gridpoint in the ionosphere. The trueE→×trueB→ drifts are used as the input to ITALCS. Time‐varying structures are detected in two‐dimensional ionospheric drifts at high latitudes based on locally maximum forward time FTLE values for both geomagnetically stormy and quiet periods. Typically, the dominant structure is shaped like the letter “U,” or a “horseshoe,” oriented with the curved portion of the “U” on the dayside around local noon. The LCSs during the geomagnetically stormy period have more complex topology and shift equatorward compared to the LCSs during quiet times. Analysis of a polar cap patch observed on 17 March 2015 with the Multi‐Instrument Data Analysis System indicates that a necessary condition for its formation and transport is that storm enhanced density exist poleward of the LCS.

[1] Data assimilation has been used successfully for real-time ionospheric specification, but it has not yet proved advantageous for forecasting. The most challenging and important ionospheric events to forecast are storms. The work presented here examines the effectiveness of data assimilation in a storm situation, where the initial conditions are known and the model is considered to be correct but the external solar and geomagnetic drivers are poorly specified. The aim is to determine whether data assimilation could be used to improve storm time forecast accuracy. The results show that, in the case of the storm of Halloween 2003, changes made to the model's initial thermospheric conditions improve electron density forecasts by at least 10% for 18 h, while changes to ionospheric fields alone result in >10% forecast accuracy improvement for less than 4 h. Further examination shows that the neutral composition is especially important to the accuracy of ionospheric electron density forecasts. Updating the neutral composition gives almost all the benefits of updating the complete thermospheric state. A comparison with real, globally distributed observations of vertical total electron content confirms that updating the thermospheric composition can improve forecast accuracy.