Abstract
Lagrangian detection and tracking algorithms are frequently used to study the development, distribution, and trends of extratropical cyclones. Past research shows that results from these algorithms are sensitive to both spatial and temporal resolutions of the gridded input fields, with coarser resolutions typically underestimating cyclone frequency by failing to capture weak, small, and short-lived systems. The fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) offers finer resolution, and, therefore, more precise information regarding storm locations and development than previous global reanalyses. However, our sensitivity tests show that using ERA5 sea level pressure fields at their finest-possible resolution does not necessarily lead to better cyclone detection and tracking. If a common number of nearest neighbors is used when detecting minima in sea level pressure (like past studies), finer spatial resolution leads to noisier fields that unrealistically break up multicenter cyclones. Using a common search distance instead (with more neighbors at finer resolution) resolves the issue without smoothing inputs. Doing this also makes cyclone frequency, life span, and average depth insensitive to refining spatial resolution beyond 100 km. Results using 6- and 3-h temporal resolutions have only minor differences, but using 1-h temporal resolution with a maximum allowed propagation speed of 150 km h−1 leads to unrealistic track splitting. This can be counteracted by increasing the maximum propagation speed, but modest sensitivity to temporal resolution persists for several cyclone characteristics. Therefore, we recommend caution if applying existing algorithms to temporal resolutions finer than 3 h and careful evaluation of algorithm settings.
Significance Statement
Many researchers use computer algorithms that automate detection of extratropical storms and then track those storms through time to better understand how they develop, where they impact people, and how they are changing as the world warms. Conventional wisdom is that using finer spatial and temporal resolutions as inputs to these algorithms improves results by capturing more storms more accurately. However, we find that storm frequency is more sensitive to algorithm settings than to spatial resolution. Making temporal resolution 1-hourly instead of 3-hourly or 6-hourly incorrectly breaks up the tracks of some storms into several smaller pieces. In either case, our datasets have improved enough so that a finer resolution is no longer always better.
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