Intercomparison of four algorithms for detecting tropical cyclones using ERA5

Abstract

Abstract. The assessment of tropical cyclone (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 trackers that aim to detect the positions of TCs in gridded datasets. The questions we ask here are the following: does the choice of tracker impact the climatology obtained? And, if it does, how should we deal with this issue? This paper compares four trackers with very different formulations in detail. We assess their performances by tracking TCs in the ERA5 reanalysis and by comparing the outcome to the IBTrACS observations database. We find typical detection rates of the trackers around 80 %. At the same time, false alarm rates (FARs) greatly vary across the four trackers and can sometimes exceed the number of genuine cyclones detected. Based on the finding that many of these false alarms (FAs) are extra-tropical cyclones (ETCs), 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 TC tracks. We then show that different traditional metrics can be very sensitive to the particular choice of tracker, which is particularly true for the TC frequencies and their durations. By contrast, all trackers identify a robust negative bias in ERA5 TC 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 and complementary properties.

The Relationship Between African Easterly Waves and Tropical Cyclones in Historical and Future Climates in the HighResMIP‐PRIMAVERA Simulations

“…Simulated TCs were objectively tracked in the EC‐Earth3P, CMCC‐CM2, and HadGEM3‐GC3.1 models. Objectively tracked TCs are sensitive to the choice of tracking algorithm, as well as the metrics used within the trackers (Bourdin et al., 2022; Zarzycki & Ullrich, 2017). Intensity thresholds in particular have been shown to drive differences in TC frequencies between trackers (Horn et al., 2014).…”

Tropical Cyclone Genesis Potential Indices in a New High‐Resolution Climate Models Ensemble: Limitations and Way Forward

“…All models reasonably reproduce the spatial pattern and seasonality of cyclone genesis. There are significant biases in the total yearly number of detected TCs (Bourdin et al., 2022). However, that is not an issue for the sake of comparison with GPIs as those indices by construction only describe variations of cyclone number and not its absolute value.…”

An Ensemble Machine Learning Approach for Tropical Cyclone Localization and Tracking From ERA5 Reanalysis Data

“…To provide a fair comparison with the deterministic TC trackers, the same IBTrACS data set including ET cyclones was used. This required using the Subtropical Jet Cut-off post-treatment method described in Bourdin et al (2022) to filter out ET cyclones. We found that the POD value is almost identical to most of the deterministic trackers compared, while the FAR value is in-line (but slightly higher) with two of the trackers considered (OWZ and TRACK).…”