Data Assimilation for Ionospheric Space-Weather Forecasting in the Presence of Model Bias - Durazo, Juan; Kostelich, Eric J.; Mahalov, Alex (Frontiers in Applied Mathematics and Statistics, 2021)
The dynamics of many models of physical systems depend on the choices of key parameters. This paper describes the results of some observing system simulation experiments using a first-principles model of the Earth’s ionosphere, the Thermosphere Ionosphere Electrodynamics Global Circulation Model (TIEGCM), which is driven by parameters that describe solar activity, geomagnetic conditions, and the state of the thermosphere. Of particular interest is the response of the ionosphere (and predictions of space weather generally) during geomagnetic storms. Errors in the overall specification of driving parameters for the TIEGCM (and similar dynamical models) may be especially large during geomagnetic storms, because they represent significant perturbations away from more typical interactions of the earth-sun system. Such errors can induce systematic biases in model predictions of the ionospheric state and pose difficulties for data assimilation methods, which attempt to infer the model state vector from a collection of sparse and/or noisy measurements. Typical data assimilation schemes assume that the model produces an unbiased estimate of the truth. This paper tests one potential approach to handle the case where there is some systematic bias in the model outputs. Our focus is on the TIEGCM when it is driven with solar and magnetospheric inputs that are systematically misspecified. We report results from observing system experiments in which synthetic electron density vertical profiles are generated at locations representative of the operational satellite observing platforms during a moderate geomagnetic storm event. The synthetic data are assimilated into the TIEGCM using the Local Ensemble Transform Kalman Filter with a state-augmentation approach to estimate a small set of bias-correction factors. We show that even simple approximations of the TIEGCM bias can reduce root-mean-square errors in 1-hour forecasts of total electron content (a key ionospheric variable) by 20 to 45 percent, compared to no bias correction. These results suggest that our approach is computationally efficient and can be further refined to improve short-term predictions (1-hour) of ionospheric dynamics during geomagnetic storms.