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Topology Can Help Us Find Patterns in Weather

Topology––the study of shapes– seems to be all the rage. You could even say that data has shape, and shape matters. Shapes are comfortable and familiar concepts, so it is intriguing to see that many applications are being recast to use topology. For instance, looking for weather and climate patterns.

[...] Thanks to high-performance computing, weather predictions have become both more accurate and more precise (localized) in recent years. While this is true for most weather, it is far less the case for extreme weather events. It turns out that the extreme weather, such as thunderstorms, blizzards, heavy rains, dry spells, and hurricanes, are more challenging to forecast than more ordinary weather. The immediate and tangible benefits of better forecasting of extreme weather better are obvious. Additionally, there are longer-term trends to consider as well. In this vein, puzzling over the apparent supercharging of extreme weather events due to human activity is one of the youngest and most important branches of climate science.

[...] Researchers at the University of Liverpool, working with researchers at Lawrence Berkeley National Laboratory, are exploring the use of topological data analysis for detecting and classifying patterns (shapes) in climate data.

[...] The researchers combined ideas from topological data analysis with machine learning for detecting, classifying and characterizing extreme weather events, such as certain atmospheric rivers. While these researchers were developing their techniques to analyze climate model output, it will have applicability to weather model output as well. They have successfully demonstrated this approach on the Cori supercomputer. Cori, one of the world's dozen most powerful supercomputers, with high performance Intel multicore processors, is operated by the National Energy Research Scientific Computing Center (NERSC).

Researchers have published results showing that their accuracy (up to 90%) is higher than any prior published results for detection and classification of atmospheric rivers. They applied their algorithm to climate models, using data spanning nearly four decades of weather data, including four different spatial resolutions and two different temporal resolutions.

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