Autoclim

It's exceedingly common to require reliable data when studying a specific climate event, like a tornado, for example. Significant local deviations from climatological normals,(a 30-year average of a weather variable for a given time of year, eg. JJA) can help us improve our understanding of these events. Faster, more accurate, and stronger forecasting is only attainable through an improved understanding of the mean climate system from which weather stems. That being said, it's still unjustifiably cumbersome to be able to use this data to draw meaningful conclusions. It's an unruly process that involves identifying relevant datasets (from hundreds of options), downloading dozens of gigabytes of data, parsing through data structures, matrix manipulation, and endless graphing parameterization to finally come to a basic understanding of the climate system's state at the time of the event. It's a fairly routine ordeal, but remains an ordeal autoClim's trying to change that. Essentially, we're looking to create a framework that allows a user to develop a set (see 'Output Categories') of useful atmospheric profiles automatically, when given a date. We wanted to create a system that's automated end-to-end, that gives you the full picture you need, when you need it. Used effectively, the sheer number of variables autoClim brings should be able to give you a high-level idea of what's going on in the system, so that you can figure out how and where to dig a little deeper.