On Space Weather Data Assimilation

Most if not all terrestrial weather prediction services today are based on data assimilation and numerical weather prediction models. Space Weather services are expected to follow a similar path towards data assimilation. However, the application of data assimilation in Space Weather requires a different implementation compared to terrestrial weather because space systems tend to be strongly forced and because the amount of data available for assimilation is critically small. In this paper we review the implementation of an ensemble Kalman filter data assimilation system based on the Space Weather Prediction Center operational Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model. We present assimilation results for neutral mass density during geomagnetically quiet and disturbed conditions and discuss the future use of data assimilation for the thermosphere ionosphere system

Storm time neutral density assimilation in the thermosphere ionosphere with TIDA

To improve Thermosphere–Ionosphere modeling during disturbed conditions, data assimilation schemes that can account for the large and fast-moving gradients moving through the modeled domain are necessary. We argue that this requires a physics based background model with a non-stationary covariance. An added benefit of using physics-based models would be improved forecasting capability over largely persistence-based forecasts of empirical models. As a reference implementation, we have developed an ensemble Kalman Filter (enKF) software called Thermosphere Ionosphere Data Assimilation (TIDA) using the physics-based Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model as the background. In this paper, we present detailed results from experiments during the 2003 Halloween Storm, 27–31 October 2003, under very disturbed (Kp = 9) conditions while assimilating GRACE-A and B, and CHAMP neutral density measurements. TIDA simulates this disturbed period without using the L1 solar wind measurements, which were contaminated by solar energetic protons, by estimating the model drivers from the density measurements. We also briefly present statistical results for two additional storms: September 27 – October 2, 2002, and July 26 – 30, 2004, to show that the improvement in assimilated neutral density specification is not an artifact of the corrupted forcing observations during the 2003 Halloween Storm. By showing statistical results from assimilating one satellite at a time, we show that TIDA produces a coherent global specification for neutral density throughout the storm – a critical capability in calculating satellite drag and debris collision avoidance for space traffic management

Hindcasting the ionosphere via assimilation of neutral mass density into physics-based models

The understanding of the upper atmosphere coupling mechanisms depends to a large extent on an accurate estimate of the true state of the Thermosphere Ionosphere system. We assimilate measurements into physics-based models to provide a better estimate of the state of the system than the one obtained using the model alone. Some of the most important parameters that define the state of the thermosphere are neutral composition and density. Since their changes can modify the production-loss processes and impact the plasma density, in this study we assimilate neutral mass density measurements to evaluate the impact that changes in the thermosphere produce in the ionosphere. The analysis is done during a period of quiet solar geomagnetic conditions (4-8 March 2008) using the physics-based Coupled Thermosphere Ionosphere Plasmasphere electrodynamics model (CTIPe) coupled to an ensemble Kalman filter (EnKF) assimilation scheme, the Thermosphere-Ionosphere Data Assimilation (TIDA). The model results from assimilating accelerometer-derived neutral mass density from CHAMP and GRACE are compared with neutral mass density independent measurements to assess the changes in the thermosphere. Total Electron Content (TEC) observations from the International GNSS Service (IGS) as well as ionosonde data from the GIRO database are used to evaluate the effect over the ionosphere

The effect of neutral mass density data assimilation on the quality of thermosphere-ionosphere state estimation

Physics based models play an important role in the analysis of the complex coupling processes between the ionosphere and the thermosphere and predicting the processes happening in the upper atmosphere. The Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model and the Thermosphere Ionosphere Electrodynamics General Circulation model (TIE-GCM) are two state-of-the-art numerical models commonly used for that purpose. However, the quality of the results of the physics-based models depends on the accuracy in the estimation of the external forcing of the system and the accuracy of the initial state of the thermosphere-ionosphere system. In order to investigate to what extent the assimilation of in-situ neutral mass density helps to improve the estimation of the state of the thermosphere-ionosphere (TI) system, we will assimilate accelerometer-derived neutral mass density from CHAMP and compare the model results with GRACE data to evaluate the improvement in the thermosphere. In the same way, we will analyze the differences with Total Electron Content (TEC) measurements from the International GNSS Service (IGS) to assess the changes in the ionosphere due to neutral mass density assimilation. Therefore, the method will allow us not only to quantify the improvement in the estimation of the TI system, but to expand our understanding of the coupling mechanisms between the thermosphere and the ionosphere. Since this analysis might depend on model capabilities and implementation of data assimilation schemes, we approach this task with two different models and data assimilation schemes. A Thermosphere-Ionosphere Data Assimilation (TIDA) scheme has been implemented to the CTIPe model and the Data Assimilation Research Testbed (DART) has been coupled to TIE-GCM model. By this means, differences in model capabilities can also be indicated