Observing system experiments with an ionospheric electrodynamics model

We assess the performance of an ensemble Kalman filter for data assimilation and forecasting of ion density in a model of the ionosphere given noisy observations of varying sparsity. The domain of the numerical model is a mid-latitude ionosphere between 80 and 440 km. This domain includes the D-E layers and the peak in the F layer in the ionosphere. The model simulates the time evolution of an ion density field and the coupled electrostatic potential as charge-neutral winds from gravity waves propagate up from the stratosphere. Forecasts are generated for an ensemble of initial conditions, and synthetic observations, which are generated at random locations in the model domain, are assimilated into the ensemble at time intervals corresponding to about a half-period of the gravity wave. The data assimilation scheme, called the local ensemble transform Kalman filter (LETKF), incorporates observations within a fixed radius of each grid point to compute a unique linear combination of the forecast ensembles at each grid point. The collection of updated grid points forms the updated initial conditions (analysis ensemble) for the next forecast. Even when the observation density is spatially sparse, accurate analyses of the ion density still can be obtained, but the results depend on the size of the local region used. The LETKF is robust to large levels of Gaussian noise in the observations. Our results suggest that the LETKF merits consideration as a data assimilation scheme for space weather forecasting.