Chasing rainfall: estimating event precipitation along tracks of tropical cyclones via reanalysis data and in-situ gauges

Jaffrés, Jasmine B.D.; Gray, Jim

doi:10.1016/j.envsoft.2023.105773

Cited by 4 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The underestimation could be related to the low intensity in the forcing field, resulting in a gap between the simulation tracking and the best tracks mentioned previously. The drawbacks associated with the WRF model were also discovered in the study conducted by Huang et al (2022), Jaffrés et al (2023), as well as Latifah and Adytia (2019), where the model revealed good accuracy in connection with the cyclone track, but it overestimated the cyclone intensities.…”

Section: Discussionmentioning

confidence: 90%

Investigation of the Development of Tropical Storm Nicholas based on Global and Regional Climate Data

Wahyuni,

Shabrina,

Lobma

et al. 2023

For. Geo.

View full text Add to dashboard Cite

This paper studies the simulation of Cyclone Nicholas that occurred close to the coastal area of Western Australia and fell on the mainland of Southwestern Australia. The simulation was conducted via a dynamical downscaling model, Weather Research and Forecasting (WRF), to obtain a higher resolution with reference to the regional climate data. The model simulation is generated using a global reanalysis of climate data for the initial and lateral boundary conditions. We investigated the response of the tropical storm to the model regarding the track and intensity using a modified Kyklop method that appears more appropriate for a landfall cyclone. Our study suggests that the regional climate data computed by the model deviates from the storm track of the global climate data forcing field. In this study, the track of the simulated storm is parallel to the satellite data, but it is shifted slightly to the east, closer to the mainland. Nevertheless, the model simulation can implement the intensity of the storm as strongly as the observation, while the forcing data delivers substantial underestimation.

show abstract

Section: Discussionmentioning

confidence: 90%