Preprints
https://doi.org/10.5194/essd-2023-460
https://doi.org/10.5194/essd-2023-460
30 Nov 2023
 | 30 Nov 2023
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset

Gabriel Morin, Mathieu Boudreault, and Jorge Luis García-Franco

Abstract. Tropical cyclone precipitation (TCP) has significant impacts on coastal communities through its modulation of flood event frequency as well as the water supply in many regions of the world. Satellite estimates provide our most reliable observations of TCP available globally, however, satellite precipitation estimates were limited because most products only have coverage starting in 1997. In this paper, we present a global dataset of TCP using the newly developed high-resolution Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2) which spans from January 1979 to date. This Global Multi-Source Tropical Cyclone Precipitation (MSTCP) dataset is comprised of two main files in the format of tables: the main and profile datasets, both from January 1979 to February 2023. The main file provides various TCP statistics per TC track for using the full record from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, including mean and maximum precipitation rates. The profile dataset comprises the azimuthally averaged precipitation every 10-km away from the center of each storm (until 500 km). The case study of Hurricane Harvey is used to show that MSWEP estimates agree well with another commonly used satellite product. The main statistics of the dataset are analyzed as well, including the differences in the dataset metrics for each of the six TC basins and for each Saffir-Simpson category for storm intensity. The dataset es freely available at: https://zenodo.org/records/10105751.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Gabriel Morin, Mathieu Boudreault, and Jorge Luis García-Franco

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-460', Anonymous Referee #1, 05 Jan 2024
    • AC1: 'Reply on RC1', Jorge Garcia-Franco, 27 Jan 2024
  • RC2: 'Comment on essd-2023-460', Anonymous Referee #2, 09 Jan 2024
    • AC2: 'Reply on RC2', Jorge Garcia-Franco, 27 Jan 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-460', Anonymous Referee #1, 05 Jan 2024
    • AC1: 'Reply on RC1', Jorge Garcia-Franco, 27 Jan 2024
  • RC2: 'Comment on essd-2023-460', Anonymous Referee #2, 09 Jan 2024
    • AC2: 'Reply on RC2', Jorge Garcia-Franco, 27 Jan 2024
Gabriel Morin, Mathieu Boudreault, and Jorge Luis García-Franco

Data sets

A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset Gabriel Morin, Mathieu Boudreault, and Jorge Luis García-Franco https://zenodo.org/records/10105751

Model code and software

A Global Multi-Source Tropical Cyclone Precipitation (MSTCP) Dataset Gabriel Morin, Mathieu Boudreault, and Jorge Luis García-Franco https://github.com/GabrielMorin1109/MSTCP-Dataset

Gabriel Morin, Mathieu Boudreault, and Jorge Luis García-Franco

Viewed

Total article views: 593 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
404 145 44 593 44 38
  • HTML: 404
  • PDF: 145
  • XML: 44
  • Total: 593
  • BibTeX: 44
  • EndNote: 38
Views and downloads (calculated since 30 Nov 2023)
Cumulative views and downloads (calculated since 30 Nov 2023)

Viewed (geographical distribution)

Total article views: 580 (including HTML, PDF, and XML) Thereof 580 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Dec 2024
Download
Short summary
The rainfall from tropical cyclones often causes severe impacts due to flooding. Research including flood-risk assessment, vulnerability as well as climate science and meteorology all require estimates of rainfall from tropical cyclones to understand the causes, predictability and mechanisms for the disasters caused by these phenomena. This dataset provides key statistics of tropical cyclone rainfall estimated from global datasets from 1979–2023.
Altmetrics