People have been modeling different parts of Earth's systems for decades, on different scales and with different goals from short term weather forecasting through actuarial risk prediction to long term climate models. In this talk I'll explore some of the typical models, methods, data formats, infrastructure layouts and design assumptions that go into such models, and discuss some low hanging fruit available to improve them.
Earth is a pretty complicated system, consisting of numerous sub-systems operating at different time and energy scales. All the systems are strongly coupled. These include the atmosphere, oceans, freshwater, cryosphere and biosphere, all of which can be further subdivided by various schemes.
The problems facing people trying to model these systems are numerous: there's a lot of data, all of it is bad, most of the code is written in Fortran, and all of it is horribly slow.
To make matters worse, modeling Earth is computationally intractable without some simplifying assumptions. For instance, if your global grid for weather prediction has "pixels" that represent more than 16km², the physical parameterization can't "see" convection, so you miss most storms. And yet somehow people manage.
In this talk, we'll start with a brief introduction to how some Earth systems work, describe some parameterizations, and then look at different free software/open source models operating under different domains, assumptions, and scales. Finally, we'll do a quick review of some of the many places where there is room for improvement.