Published February 18, 2025 | Version v1.0.0
Dataset Open

Lagrangian Tropical Cyclone Precipitation Estimates and Moisture Sources (LagTCPMoS) Dataset

  • 1. Centro de Investigación Mariña, Universidade de Vigo, Environmental Physics Laboratory (EPhysLab), Campus As Lagoas s/n, 32004 Ourense, Spain
  • 2. Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal.
  • 3. Departamento de Meteorologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-919, Brazil
  • 4. Galicia Supercomputing Center (CESGA), Santiago de Compostela, Spain

Description

The Lagrangian Tropical Cyclone Precipitation Estimates and Moisture Sources (LagTCPMoS) dataset compiles Lagrangian precipitation estimates and moisture sources for all tropical cyclones (TCs) that occurred in the North Atlantic basin from 1980 to 2023 (Landsea and Franklin, 2013). This dataset was created using the Lagrangian moisture and heat tracking (LATTIN) tool (Pérez-Alarcón et al., 2024), which backtracked up to 10 days the air parcels residing over the TC area (delimited by a fixed and symmetrical radius of 500 km) at each 6-hourly time interval. The air parcel trajectories were filtered from the global outputs (Vázquez et al., 2024) of the Lagrangian particle dispersion model FLEXPART (Pisso et al., 2019).

To identify moisture sources, we applied the source attribution method based on the one proposed by Sodemann et al. (2008). We also followed the specific threshold values for TCs found by Pérez-Alarcón et al. (2025). Additionally, we utilized the high-resolution Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2019) to bias-correct the moisture sources and Lagrangian precipitation estimates, as detailed in Pérez-Alarcón et al. (2025).

For each TC, LagTCPMoS has a NetCDF file containing the 6-hourly Lagrangian precipitation estimates and moisture uptake. The file has been named according to the TC code provided by the HURDAT2 dataset (Landsea and Franklin, 2013).

This database is fully described in Pérez-Alarcón, A.; Trigo, R.M.; Nieto, R.; and Gimeno, L. (2025). Lagrangian Tropical Cyclone Precipitation Estimates and Moisture Sources (LagTCPMoS) Dataset. Scientific Data. 12, 1161. https://doi.org/10.1038/s41597-025-05490-y.

Files

NATL_1980.zip

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Additional details

Related works

Is derived from
Journal article: 10.1016/j.atmosres.2024.107822 (DOI)

Funding

Xunta de Galicia
Postdoctoral grant ED481B-2023/016
Xunta de Galicia
Research Project Excelencia-ED431F-2024/03
Xunta de Galicia
Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva ED431C2021/44
Ministerio de Ciencia, Innovación y Universidades
SETESTRELO Project PID2021-122314OB-I00
European Union
ERDF A way of making Europe PID2021-122314OB-I00

References

  • Beck, H. E. et al. Mswep v2 global 3-hourly 0.1 precipitation: methodology and quantitative assessment. Bulletin of the American Meteorological Society 100, 473–500 (2019). https://doi.org/10.1175%2FBAMS-D-17-0138.1.
  • Landsea, C. W., & Franklin, J. L. (2013). Atlantic hurricane database uncertainty and presentation of a new database format. Monthly Weather Review, 141(10), 3576-3592. https://doi.org/10.1175/MWR-D-12-00254.1.
  • Pérez-Alarcón, A., Fernández-Alvarez, J. C., Nieto, R., & Gimeno, L. (2024). LATTIN: A Python-based tool for Lagrangian atmospheric moisture and heat tracking. Software Impacts, 20, 100638.
  • Pérez-Alarcón, A., Vázquez, M., Trigo, R. M., Nieto, R., & Gimeno, L. (2025). Towards an understanding of uncertainties in the Lagrangian analysis of moisture sources for tropical cyclone precipitation through a study case. Atmospheric Research, 107822. https://doi.org/10.1016/j.atmosres.2024.107822.
  • Pisso, I., Sollum, E., Grythe, H., Kristiansen, N. I., Cassiani, M., Eckhardt, S., ... & Stohl, A. (2019). The Lagrangian particle dispersion model FLEXPART version 10.4. Geoscientific Model Development, 12(12), 4955-4997. https://doi.org/10.5194/gmd-12-4955-2019.
  • Sodemann, H., Schwierz, C., & Wernli, H. (2008). Interannual variability of Greenland winter precipitation sources: Lagrangian moisture diagnostic and North Atlantic Oscillation influence. Journal of Geophysical Research: Atmospheres, 113(D3). https://doi.org/10.1029/2007JD008503.
  • Vázquez, M, Alvarez-Socorro, G., Fernández-Alvarez, J.C., Nieto, R., & Gimeno, L. (2024). Global FLEXPART-ERA5 Simulations Using 30 Million Atmospheric Parcels Since 1980 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13682647.