EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Accounting for Uncertainties in MSIS 2.0

Piyush Mehta1, Richard Licata1, Daniel Weimer2, Douglas Drob3, W Kent Tobiska4, and Jean Yoshi4
Piyush Mehta et al.
  • 1Mechanical and Aerospace Engineering, West Virginia University, Morgantown WV, USA (
  • 2Center for Space Science and Engineering Research, Virginia Tech, Blacksburg, VA, USA
  • 3Space Science Division, U.S. Naval Research Laboratory, Washington, DC, USA
  • 4Space Environment Technologies, Palisades CA, USA

Modeling of the upper atmosphere, specifically the thermosphere mass density, remains the primary source of uncertainty in satellite drag and orbital operations in low Earth orbit (LEO). The variations in mass density are dominated by changes in solar irradiance on the timescales of the solar cycle, however, short-term space weather changes can significantly impact the state of the thermosphere, especially during geomagnetic storms. Because of our limited understanding of such variations and the resulting inaccurate modeling, quantifying the uncertainty in density specification and forecasting becomes critical for space operations including decision making for collision avoidance and safeguarding of our space assets.

The Naval Research Laboratory’s MSIS model is one of the most widely used models in operations, especially in the commercial industry. Several different versions of the models have been developed, the most recent being MSIS 2.0. A new methodology for calibration of the MSIS model with exospheric temperatures inverted using accelerometer-derived density estimates has recently been developed. In this work, we apply a similar but updated methodology to the MSIS 2.0 model and use machine learning, specifically a neural network, to develop a version of the MSIS 2.0 model calibrated to the accelerometer-derived density estimates  that also provides reliable uncertainty estimates.

How to cite: Mehta, P., Licata, R., Weimer, D., Drob, D., Tobiska, W. K., and Yoshi, J.: Accounting for Uncertainties in MSIS 2.0, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2946,, 2022.