Determining and understanding substorm energy loss and partitioning

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The substorm is a repeatable earthquake-like disturbance to near-Earth Space, which, apparently unpredictably, recurs after anything from 2 hours to a day or more and dumps typically one thousand million million Joules of energy into the upper atmosphere equivalent to ten Oklahoma tornados or the largest nuclear weapon in the US arsenal. The substorm's intermittency and variable size makes it arguably the greatest source of uncertainty in predicting the state of the upper atmosphere. Its most obvious effect is the aurora, which would be nice to know when its happening so that we could plan our Arctic holidays, but substorm prediction is also important for mitigating the effects of natural changes in the upper atmosphere on geostationary satellite communications and navigation, low-altitude satellite orbits and remote sensing, electricity power grids, and oil and mineral prospecting. Prediction is also the ultimate test for our scientific understanding. Progress requires measuring and analysing substorm variability in order to test and develop models based on maths and physics. We already know the statistics of substorm timing and have explained this with a simple mathematical model (that has also been used for understanding neuron firing in the brain!). However, knowing and understanding the variability of substorm size is much harder because it requires to measure simultaneously over large regions of the polar upper atmosphere and out into Space.In this project, we propose to attempt this by examining lots of substorms over more than a decade using spacecraft together with networks of magnetometers (sophisticated scientific compasses) and radars in both the Arctic and Antarctic. The resulting stats will be compared with what we already know from much more limited observations and with the predictions of new and existing substorm theories and models. The outcomes will be knowing things like how likely a really big substorm is that could mess things up, as well as models to explain why and hopefully when that might occur.