Quantification of Representation Error in the Neutral Winds and Ion Drifts Using Data Assimilation

In this work we quantify the representation error of the algorithm Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), which estimates global neutral winds and ion drifts given time‐varying plasma densities. SAMI3 (Sami3 is A Model of the Ionosphere) serves as the background climate model and pseudo‐measurements for the EMPIRE observation system. This configuration allows the data assimilation inputs to be self‐consistent between each other and with the validation data. The estimated neutral winds and ion drifts are compared to the Horizontal Wind Model (HWM14) and SAMI3 “truth.” For both the quiet period on 25 August 2018 and subs7equent storm on 26 August, the EMPIRE estimation of ion drifts is better at low‐to‐mid geomagnetic latitudes with mean error up to 20 m/s. For the high latitudes (poleward of ±60° magnetic), the mean errors exceed 50 m/s with variances up to 200 m/s, and the relative errors are higher than the “truth.” At latitudes of ±87°, the large errors are attributed to a boundary effect. However, the neutral wind mean errors peak at 20 m/s at mid‐latitudes (40°–60° magnetic), with larger uncertainties, then converge to 0 approaching higher latitudes. By conducting this study, we define a method for obtaining the representation error covariance for future use of EMPIRE with SAMI3 as background. Plain Language Summary Data assimilation in the field of space weather is a technique that reduces the gap between background model and instrument measurements to estimate the current or future conditions. Data assimilation needs an estimate of the possible errors, to optimize the calculation of atmospheric variables. The model error and measurement errors can be provided by the model data source and instrument. However, another error source, called the representation error, plays an important role. The representation error is due to simplifying reality to a mathematical model that can be run by a computer. In this study, we compute the representation error for a specific data assimilation algorithm (EMPIRE). To achieve this, we use a model‐simulated reality as the truth. We import information from background models to the data assimilation algorithm, then compare the algorithm outputs to the simulated truth of neutral winds and ion drifts. We conclude that the error for ion drifts from the data assimilation is small at the low‐to‐mid latitudes, but much larger at high latitudes. For neutral wind, the error is larger at mid‐lat