An inter-comparison of tropical cyclone datasets for the Australian region

Abstract Tropical cyclone (TC) best track datasets have temporal inhomogeneity, mostly associated with changes in monitoring practices and technological improvements. Temporal inconsistencies are often mitigated by using TC data from more homogeneous periods. For example, TC records since 1980 are preferred for frequency and track analysis, while records for intensity analysis have become more consistent since ∼2000. However, such measures reduce the sample size for trend analysis, potentially leading to conflicting conclusions due to natural climate-variability. Inter-agency best track data can also vary, due to differences in the way best track information—such as centre fix locations and associated intensity estimates—are defined and assessed. When comparing global datasets and regional datasets, additional inconsistencies can be introduced where TCs form or track just outside the official area of responsibility for each agency. We highlight discrepancies in Australian TC best track data from various sources by comparing it to a more rigorously scrutinized dataset compiled by the Australian Bureau of Meteorology. This dataset is found to have highly accurate TC records for the Australian region. We also highlight the implications of data differences on TC-related trend analysis, aiming to increase awareness of dataset inconsistencies while guiding credible climate-change detection and attribution messages.

Authors

Institutions

Publication Details

Journal
Environmental Research Letters
Published
2024-12-11
DOI
https://doi.org/10.1088/1748-9326/ad9d5e
Citations
2
Primary Topic
Tropical and Extratropical Cyclones Research
Type
article
Field-Weighted Citation Impact
0.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An inter-comparison of tropical cyclone datasets for the Australian region

Hamish A. Ramsay, Philip J. Klotzbach, Savin S. Chand, Sushil Kumar et al.
2 citations
Environmental Research Letters
Tropical and Extratropical Cyclones Research
0.75
article

An inter-comparison of tropical cyclone datasets for the Australian region

Hamish A. Ramsay, Philip J. Klotzbach, Savin S. Chand, Sushil Kumar, Valentina Koschatzky, Sarvesh Kumar, Joe Courtney
article en
2 citations

Abstract

Abstract Tropical cyclone (TC) best track datasets have temporal inhomogeneity, mostly associated with changes in monitoring practices and technological improvements. Temporal inconsistencies are often mitigated by using TC data from more homogeneous periods. For example, TC records since 1980 are preferred for frequency and track analysis, while records for intensity analysis have become more consistent since ∼2000. However, such measures reduce the sample size for trend analysis, potentially leading to conflicting conclusions due to natural climate-variability. Inter-agency best track data can also vary, due to differences in the way best track information—such as centre fix locations and associated intensity estimates—are defined and assessed. When comparing global datasets and regional datasets, additional inconsistencies can be introduced where TCs form or track just outside the official area of responsibility for each agency. We highlight discrepancies in Australian TC best track data from various sources by comparing it to a more rigorously scrutinized dataset compiled by the Australian Bureau of Meteorology. This dataset is found to have highly accurate TC records for the Australian region. We also highlight the implications of data differences on TC-related trend analysis, aiming to increase awareness of dataset inconsistencies while guiding credible climate-change detection and attribution messages.

Environmental Research LettersVol. 20(1)
Bureau of Meteorology (AU), Commonwealth Scientific and Industrial Research Organisation (AU), Federation University (AU), Gallagher (United States) (US), University of the South Pacific (FJ), Colorado State University (US)
Climate action
Openalex Percentile: Top 29%
Tropical and Extratropical Cyclones Research
0.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.