Quantifying the Storm Time Thermospheric Neutral Density Variations Using Model and Observations. Issue 2 (14th February 2019)

• Record Type: Journal Article
• Authors: Kalafatoglu Eyiguler, E. Ceren; Shim, J. S.; Kuznetsova, M. M.; Kaymaz, Z.; Bowman, B. R.; Codrescu, M. V.; Solomon, S. C.; Fuller‐Rowell, T. J.; Ridley, A. J.; Mehta, P. M.; Sutton, E. K.
• Is Part Of: Space weather. Volume 17:Issue 2(2019)
• Page Start: 269
• Page End: 284
• Publication Date: 2019-02-14
• Subjects: thermospheric neutral density -- validation -- IT models -- CHAMP -- metrics -- GEM‐CEDAR challenge; Space environment -- Periodicals; 551.509992
• DOI: 10.1029/2018SW002033
• Languages: English
• ISSNs: 1542-7390

Abstract

Abstract: Accurate determination of thermospheric neutral density holds crucial importance for satellite drag calculations. The problem is twofold and involves the correct estimation of the quiet time climatology and storm time variations. In this work, neutral density estimations from two empirical and three physics‐based models of the ionosphere‐thermosphere are compared with the neutral densities along the Challenging Micro‐Satellite Payload satellite track for six geomagnetic storms. Storm time variations are extracted from neutral density by (1) subtracting the mean difference between model and observation (bias), (2) setting climatological variations to zero, and (3) multiplying model data with the quiet time ratio between the model and observation. Several metrics are employed to evaluate the model performances. We find that the removal of bias or climatology reveals actual performance of the model in simulating the storm time variations. When bias is removed, depending on event and model, storm time errors in neutral density can decrease by an amount of 113% or can increase by an amount of 12% with respect to error in models with quiet time bias. It is shown that using only average and maximum values of neutral density to determine the model performances can be misleading since a model can estimate the averages fairly well but may not capture the maximum value or vice versa. Since each of the metrics used for determining model performances provides different aspects of the error, among these, we suggest employing mean absolute error, prediction efficiency, and normalized root mean square error together as a standard set of metrics for the neutral density. Plain Language Summary: Thermospheric neutral density is the largest source of uncertainty in atmospheric drag calculations. Consequently, mission and maneuver planning, satellite lifetime predictions, collision avoidance, and orbit determination depend on the accurate estimation of the thermospheric neutral density. Thermospheric neutral density varies in different timescales. In short timescales, the largest variations occur due to the geomagnetic storms. Several empirical and physics‐based models of the ionosphere‐thermosphere system are used for estimating the variations in the neutral density. However, the storm time responses from the models are clouded by the climatology (background variations), upon which the effect of geomagnetic storms is superimposed. In this work, we show that it is critical to use reference levels for the neutral density to extract the true performance of the models for the evaluation of the storm time performances. We demonstrate that mean absolute error, prediction efficiency, and normalized root mean square error should be considered together for the performance evaluations, since each of them provides different aspects of the error. Key Points: Using the average and maximum values of neutral densities to determine the model performances can be misleading Removing the quiet time trend from the neutral density reveals the actual performance of the model in simulating the storm time variations Mean absolute error, prediction efficiency, and normalized root mean square error should be considered together for the evaluations