The Earth's magnetosphere as depicted by a computer model, showing a geomagnetic storm in January 1997.
Click on image for full size
Image courtesy of the Space Plasma Physics group (with help from the Advanced Visualization Laboratory) at University of Maryland.
Modeling Space Weather
Scientists who study space weather make extensive use of computer models to make sense
of complex phenomena. This is a way in which space weather is quite
similar to Earthly weather, for weather forecasters on our planet also
employ sophisticated models to predict weather and climate.
Space weather researchers must fuse together separate models covering
the Sun's interior, the solar atmosphere, interplanetary space, Earth's magnetosphere,
and Earth's atmosphere. These separate models must mesh smoothly at their borders.
Predictions generated by models should
match data from actual events. Scientists compare results from models with real events to verify the
accuracy of their models, fine tuning the evolving models as they go. Successful
models generate data that matches well with reality in a wide range of circumstances.
Some hybrid models blend actual data with the models' predictions.
What are space weather models used for? One use is forecasting and prediction.
If we observe an event of the Sun, a model can predict its impact near and
on Earth. When a big space weather storm is coming, we can warn spacewalking
astronauts, put orbiting satellites into "safe" modes, prepare electrical power
grids for voltage surges, and anticipate spectacular auroral displays. Models
can also help us improve our understanding of natural phenomena and the laws
of physics by taking advantage of a natural laboratory that produces conditions,
such as high temperatures, powerful magnetic fields, or large distances, that
are unattainable in Earthly laboratory settings. Improving our understanding
of physical laws in extreme conditions often aids our ability to better apply
those laws in more mundane settings. Finally,
model "runs" with specific settings, when compared with data from similar actual
events, help us refine and improve our models for future use.
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