Shortly after submitting my proposal, I had received confirmation that I had in fact been granted the URSP award. During the months of April through August 2011, I had been working daily as an undergraduate research assistant under the direction of Dr. Pawlowski. This research was my first plunge into the discipline of computational physics. Over this time period I had gained experience in the subject and performed the following:
• Running computer simulations of Earth’s upper atmosphere based on observations of the sun. Creating a probabilistic forecast for space weather.
• Programmed software scripts using IDL and FORTRAN
• Completed NASA’s Information Security Training, granted access to NASA’s Pleiades supercomputer.
• Parallel computing protocol with Message Passing Interface (MPI)
This post is the contents of a research poster I had put together for a presentation I had made to three groups of visiting 7th and 8th graders, fellow science undergraduates and faculty. You may also download my poster in PDF format here.
Abstract
The prospect of what we can do to increase the knowledge of space weather is promising. One of the practical reasons for doing so is the fact that perturbations in density and temperature in the upper atmosphere can have a significant effect on satellites and instrumentation in low orbit of Earth. By utilizing ensembles of simulations or ensembles that are based on the uncertainty of the system drivers, we can run computer simulations to help us make probabilistic forecasts about space weather. In this study, we prepared an ensemble simulation based on uncertainties in the solar extreme ultraviolet (EUV) flux. Then, using the Global Ionosphere-Thermosphere Model (GITM) we ran simulations simultaneously using these ensembles, and analyzed the results.
Introduction
•Space weather
•the conditions in Earth’s upper atmosphere (ionosphere and thermosphere) in response to changing conditions on the sun.
Why do we scarcely hear meteorologists in our local news reports making predictions about space weather?
•The overarching reasons:
•Society at large generally doesn’t know that space weather affects expensive infrastructure we use daily (e.g. satellite communications, GPS navigation)
•Space weather forecasting is currently in significant need of more scientific research in order to make reasonably accurate, long-term predictions.
•Ensemble forecasting
•Create a statistical sample of weather outcomes by performing multiple computer simulations simultaneously.
•Ensembles, are based on a number of input conditions called “system drivers”.
•Ensemble members are created based on the uncertainty of these drivers and are used as inputs to our Global Ionosphere and Thermosphere Model (GITM) and simulations are performed which will create a probabilistic forecast of space weather.
This first segment of this long-term study involves understanding and collecting solar flux data. Utilizing the known uncertainties of this data, we created an ensemble of drivers that were used as input to multiple GITM simulations. This study examines the effect of the uncertainty in the solar flux on the state of the upper atmosphere.
About the Global Ionosphere and Thermosphere Models
•3-D coupled thermosphere-ionosphere model [Ridley et al., 2006]
•Solves for 11 Neutral and 10 Ion species, neutral winds (horizontal and vertical), ion and electron velocities and neutral, ion, and electron temperatures
•Does not assume hydrostatic equilibrium: Coriolis, vertical ion drag, non- constant gravity, massive auroral zone heating
•Flexible grid resolution, fully parallel, with an altitude grid
•Allows for 1D simulations to perform long duration
For this study: 2.5o Latitude x 5o Longitude resolution using 64 processors on the NASA Pleiades supercomputer
Drivers: Solar Flux
Plot of the Solar Flux vs. Time. The black line represents the actual solar flux. The colored lines represent the 5 ensemble members that are used to drive the simulations.
Thermosphere Results
The following plots show the mean mass atmospheric density which is determined from the mean of 5 ensemble members (top plots) and their standard deviations (bottom plots) of the thermosphere at a 403 km altitude.
1:00 UT: The simulation has just started and thus there is small difference between the simulations due to the different solar fluxes being used.
4:00 UT: The effects of the flare on the mass density begin to appear.
6:00 UT: Here the effects of the flare reaches its maximum. On a large scale, this is when the largest effects occur. Notice the time delay in effects from Figure 1's peak.
13:00 UT: The effects of the uncertainty have propagated globally. High levels of standard deviation are probably due to significantly different wind patterns. The black arrow points to a maximum uncertainty of 1.7%
Ionosphere Results
The following plots show the mean electron density which is determined from the mean of the 5 ensemble members (top plots) and their standard deviations (bottom plots) of the ionosphere at an altitude of 118 km (left) and 403km (right) .
3:00 UT (118 km): Flare has just ended. Ionosphere is significantly uncertain on the day side.
3:00 UT (403 km): At this higher altitude our standard deviation has significantly increased along with the mean density of electrons.
Conclusions and Future Work
Given the uncertainties in the solar flux, it takes several hours for thermosphere to become uncertain. Within 12 hours, the uncertainties have propagated globally in the thermosphere. This is interesting because the thermosphere only affects the day side directly.
•At 13 UT the maximum uncertainty is 1.7%
The ionosphere, however can become uncertain very quickly.
•At 3:00 UT (403 km): The maximum uncertainty is 47%.
The ultimate goal of this research is to predict space weather over one solar rotation period (27 days). We need to study the uncertainty involved in using data on this month’s drivers to predict next month’s space weather. This research also has yet to account for both types of uncertainty in the driver: flare and daily uncertainty. In the future, it will use even more ensemble inputs, like interplanetary magnetic fields (IMF) and solar wind to the GITM model.
References and Acknowledgements
Ridley, A.J., Deng, Y., Toth, G., The Global Ionosphere-Thermosphere Model, J. Atmos. Sol-Terr. Phys., 68, 839, 2006
Flare irradiance data was processed and provided by Phil Chamberlin and Anne Wilson, University of Colorado. http://lasp.colorado.edu/lisird/fism/fism.html
Special thanks to Eastern Michigan University for proving the Undergraduate Research Stimulus Program.
