David Romero *
Christian M. Appendini **, ***
Kerry Emanuel ****
Chia-Ying Lee *****
Kees Nederhoff ******
Nadia Bloemendaal *******, ******** 
Pablo Ruiz-Salcines **, ********* 
Jonathan Vigh **********
Christian Domínguez ***********

* Escuela Nacional de Estudios Superiores Unidad Mérida, Universidad Nacional Autónoma de México
** Laboratorio de Ingeniería y Procesos Costeros, Instituto de Ingeniería, Universidad Nacional Autónoma de México
*** Aarhus Institute of Advanced Studies, Aarhus University
**** Lorenz Center, Massachusetts Institute of Technology
***** Lamont-Doherty Earth Observatory, Columbia University
****** Deltares USA
******* Royal Netherlands Meteorological Institute
******** Institute for Environmental Studies, Vrije Universiteit Amsterdam
********* Centre for Climate Change, Geography Department, Universitat Rovira i Virgili
********** U.S. National Science Foundation, National Center for Atmospheric Research
*********** Departamento de Ciencias Atmosféricas, Grupo de Hidroclimatología Tropical,  Instituto de Ciencias de la Atmósfera y Cambio Climático, UNAM

Abstract

Tropical cyclones (TCs) pose significant risks due to their associated hazards, including powerful winds, inland and coastal flooding, and wind waves. However, more reliable TC records are required to ensure a robust statistical analysis for risk assessment. To overcome this limitation, researchers have developed methods to generate synthetic tropical cyclones (STCs) that provide a larger sample size of occurrences at specific locations. This study compares STC databases from different sources such as Massachusetts Institute of Technology (MIT), Columbia HAZard model (CHAZ), Synthetic Tropical cyclOne geneRation Model (STORM), and Deltares with historical TCs from the International Best Track Archive for Climate Stewardship (IBTrACS) on a basin-wide scale in the North Atlantic Basin. The aim is to assess the effectiveness of STCs in replicating crucial historical tropical cyclones parameters for risk analysis and to identify potential biases in the STC generation models. The comparison uses a hexagonal mesh to evaluate characteristics such as maximum winds, translation speed, and residence time. The study acknowledges the validation paradox arising from the limited IBTrACS data at specific locations that make it difficult to rigorously validate the accuracy of STCs in those areas and from systematic differences across the STC datasets. Despite the historical TCs database limitation, comparing STC with IBTrACS characteristics remains the only viable method for assessing biases in STC generation models. The evaluated STCs reveal spatial bias patterns, which may indicate deficiencies in the underlying hazard models. Identifying and describing these biases aim to guide the use of these events and highlight key aspects for further development in STC generation methods.