Estimating and predicting corrections for empirical thermospheric models

Abstract

Quantifying spatial and temporal changes in thermospheric neutral density is important for various applications such as precise orbit determination, estimating mission lifetime and reentry prediction of Earth orbiting objects. It is also crucial for analysis of possible collisions between active satellite missions and space debris. Empirical models are frequently applied to estimate neutral densities at the position of satellites. But their accuracy is severely constrained by model simplifications and the sampling limitation of solar and geomagnetic indices used as inputs. In this study, we first estimate thermospheric neutral density by processing the high-accuracy accelerometer measurements on-board of the twin-satellite mission Gravity Recovery And Climate Experiment (GRACE). Daily density corrections (in terms of scales) are then computed for the commonly used NRLMSISE-00 empirical model. The importance of these daily scales is examined within an orbit determination practice. Finally, three data-driven prediction techniques based on Artificial Neural Network (ANN) are applied to forecast the daily density corrections for few days to months. Our numerical results indicate that GRACE derived scales are correlated with solar and geomagnetic indices and can improve the timing (from few hours to days) and magnitude of model simulations (up to 10-100 times) during high solar or geomagnetic activity when they usually perform poorly. We found that the Non-linear Autoregressive with Exogenous (External) Input (NARX) ANN technique performs well in predicting the corrections with an average fit of 0.8 or more in terms of squared correlation coefficients for timescales of 7-90 days.

Forecasting global and multi-level thermospheric neutral density and ionospheric electron content by tuning models against satellite-based accelerometer measurements

“…1 ) shows the standardized solar index along-side of PC1. This confirms the previous results of e.g., Forootan et al 32 who reported that empirical models do not reflect recent neutral mass density changes caused by solar activity. The first mode of daily TND differences Fig.…”

Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique

“…These models consist of multilayer neurons that make a connection between inputs and outputs. This means that the output of each layer is a function of weight and bias of the existing neurons that pass through an activation function, and they are transmitted to the next layer until they end up at the last layer [25]. Therefore, an ANN can be considered a mapping function that projects the parameter of the input layer to the corresponding objective values in the output layer.…”

“…In this study, six radiosonde stations are considered, whose locations can be seen in Figure 5. For evaluating the results, the RMSE, Correlation Coefficients ( ), Mean Absolute Error (MAE), and Refined Willmott Index (RWI) statistical values are used, which can be calculated, respectively, as [4,25,48]:…”

“…Artificial Neural Network (ANN) techniques have been used to improve the accuracy of empirical models [16,25,26]. This method has the capability of learning the structure of 3 of 17 non-linear processes by constructing neuron-based mathematical models [27].…”

How can along-track accelerations be used to predict global and multi-level thermospheric neutral density?

“…A logical step to make the best use of along-track TND observations and available models can be realised through the mathematical merging frameworks that build a connection between them. For example, (1) correction fields are applied as a ratio of TNDs from LEO satellites and those of models (Doornbos et al, 2005(Doornbos et al, , 2008Pérez et al, 2014;Shi et al, 2015;Ruan et al, 2018;Forootan et al, 2019;Weng et al, 2017). However, this method can only be used for reanalyzing and now-casting the thermosphere, and the reliability of these ratio fields, derived by a limited number of satellite tracks, might be treated with caution.…”