Intercomparison of Four Tropical Cyclones Detection Algorithms on ERA5

Abstract

Abstract. The assessment of Tropical Cyclones (TC) statistics requires the direct, objective, and automatic detection and tracking of TCs in reanalyses and model simulations. Research groups have independently developed numerous algorithms during recent decades in order to answer that need. Today, there is a large number of algorithms, often referred to as trackers, that aim to detect the positions of tropical cyclones in gridded datasets. This paper compares four trackers with very different formulations in detail. We assess their performances by tracking tropical cyclones in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. The first section of the paper finds typical detection rates of the trackers ranging from 75 to 85 %. At the same time, false alarm rates (FAR) greatly vary across the four trackers and can sometimes exceed the number of detected genuine cyclones. Based on the finding that many of these false alarms are extra-tropical cyclones, we adapt two existing filtering methods common to all trackers. Both post-treatments dramatically impact FARs, which range from 9 to 36 % in our final catalogs of tropical cyclones tracks. We then show that different traditional metrics can be very sensitive to the particular choice of the tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 tropical cyclones intensities, a result already noted in previous studies. We conclude by advising against using as many trackers as possible and averaging the results. A more efficient approach would involve selecting one or a few trackers with well-known properties.

Can low-resolution CMIP6 ScenarioMIP models provide insight into future European post-tropical-cyclone risk?

“…This should involve further work on our theoretical understanding of what drives TC genesis and further quantification of the uncertainty associated with different TC identification methods (e.g. Bourdin et al, 2022).…”

Contrasting Responses of Atlantic and Pacific Tropical Cyclone Activity to Atlantic Multidecadal Variability

“…TempestExtremes uses sea level pressure as its primary feature‐tracking variable, with additional constraints from air temperature, wind speed, and other environmental variables at 6‐hourly resolution. Compared to other tracking algorithms, TempestExtremes requires less but well‐known variables as inputs (Bourdin et al., 2022), making TC detection with the GCM outputs possible. We used the following procedures to track TCs.…”

Intercomparison of four algorithms for detecting tropical cyclones using ERA5

“…The IBTrACS database is provided by NOAA at https://www.ncdc.noaa.gov/ibtracs/ (last access: 22 August 2022). All the scripts used to produce the present paper's results are available at https://doi.org/10.5281/zenodo.6424432 (Bourdin, 2022a). These include the code to run the UZ and OWZ trackers and the original TRACK and CNRM databases, the code for the post-treatment and the tracks matching Python scripts for the whole analysis, the code to reproduce the figures, and finally, a copy of v.0.5 of the dynamicoPy package used in the Python scripts (https://doi.org/10.5281/zenodo.7015245; Bourdin, 2022b).…”