Data analysis, data science, and advanced statistics have an important role to play in climate science.

James Elsner’s blog Hurricane & Tornado Climate offers salient examples, in this regard.

Yesterday’s post was motivated by an Elsner suggestion that the time trend in maximum wind speeds of larger or more powerful hurricanes is strongly positive since weather satellite observations provide better measurement (post-1977).

Here’s a powerful, short video illustrating the importance of proper data segmentation and statistical characterization for tornado data – especially for years of tremendous devastation, such as 2011.

Events that year have a more than academic interest for me, incidentally, since my city of birth – Joplin, Missouri – suffered the effects of a immense supercell which touched down and destroyed everything in its path, including my childhood home. The path of this monster was, at points, nearly a mile wide, and it gouged out a track several miles through this medium size city.

Here is Elsner’s video integrating data analysis with matters of high human import.

There is a sort of extension, in my mind, of the rational expectations issue to impacts of climate change and extreme weather. The question is not exactly one people living in areas subject to these events might welcome. But it is highly relevant to data analysis and statistics.

The question simply is whether US property and other insurance companies are up-to-speed on the type of data segmentation and analysis that is needed to adequately capture the probable future impacts of some of these extreme weather events.

This may be where the rubber hits the road with respect to Bayesian techniques – popular with at least some prominent climate researchers, because they allow inclusion of earlier, less-well documented historical observations.