Disclaimer: This is my first attempt at a grandma-friendly explanation of one of the key instruments in a climate scientist’s bag: climate reanalyses.


My new job is all about finding weather observations that can feed into things called reanalyses. A reanalysis product is a massive dataset that can be used to recreate how we think the weather and climate behaved. Having this kind of ‘guess-timate’ of the recent atmosphere helps scientists learn more about how weather patterns form and decay, different ways that the atmosphere is responding to climate change, and all sorts of cool  ways to understand how the weather works. Reanalyses are used to study things like extreme weather events, improving weather forecasting, how climate change is affecting the atmosphere, and sun, wind and rain availability for renewable energy and agriculture.

Start with the data

The basis of all reanalysis products is weather observations. Reanalyses use data from the surface (land and sea), from weather balloons and from satellites. From here, physics and models are used to fill in the gaps, or assimilate the data, at the surface and up into the atmosphere. This is done for every six hours or so over the period covered by the reanalysis (most of them start in the 1960s when weather observations become more widespread). It’s kind of like a global connect-the-dot that is in three dimensions, where the dots are weather observations and the lines are complex equations. And all the connect-the-dots are in a flip book with a different page for every six hours. And you can use the page before and the page after to help you with your current page. And all the pages add up to terabytes and terabytes of data. Simple!

Regional reanalyses

Most reanalyses cover the whole world. As you can imagine, reanalysis products are most reliable in places that have more weather observations. If you have fewer dots, then you can’t see the picture as well. In some parts of the world with plenty of dots, like Europe, they are even starting to make ‘regional reanalyses’, which takes the global reanalysis as a starting point, and then creates a more high-resolution version over a particular area. These regional reanalyses are a bit like taking the connect-the-dot pages, making them into a jigsaw puzzle (still with me?), and then nesting another jigsaw puzzle inside each puzzle, with much smaller pieces. My current job is helping to find more dots for these kinds of puzzles.

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