Developing spin-up time framework for WRF extreme precipitation simulations

Ying Liu*, Lu Zhuo, Dawei Han

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)
27 Downloads (Pure)

Abstract

Despite the wide application of the Weather Research and Forecasting (WRF) model in extreme precipitation simulations, there is a lack of consensus and clear guidance on identifying the suitable length of spin-up time. In this study, the WRF model was used to simulate the extreme precipitation events that happened on the 4 November 2015 at Alexandria of the Nile Delta. According to the observation from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), 21 spin-up time experiments of the 3-level-nested domain scenario and 21 spin-up time experiments of the 2-level-nested domain scenario were designed to explore the relationship between model required spin-up time and initial conditions. The simulation performances were evaluated by seven verification metrics and one overall performance score. Here we try to provide guidelines on how to determine the optimal spin-up time through satellite data without too many trial-and-error tests. An Optimal Spin-up Time Identifying (OSTI) framework with possible weather situations and spin-up time determining steps is proposed to help future work. It is found that the occurrences of disturbing weather events strengthen the influence of initial conditions on simulation outputs and increase model requirements for spin-up time lengths. Moreover, this framework is more useful for precipitation events with strong synoptic backgrounds because their simulation performances depend largely on the development of appropriate atmospheric circulations and physical equilibrium states in the model.
Original languageEnglish
Article number129443
Number of pages15
JournalJournal of Hydrology
Volume620
Early online date25 Mar 2023
DOIs
Publication statusPublished - 1 May 2023

Bibliographical note

Funding Information:
The first author is grateful to the China Scholarship Council and the University of Bristol for providing the joint scholarship (CSC No, 201908310086) for her PhD study at the University of Bristol. All authors acknowledge the dataset developers from NASA and ECMWF for producing and sharing the datasets employed in this study. We would also like to acknowledge the Advanced Computing Research Centre at the University of Bristol for providing access to the High-Performance Computing system BlueCrystal.

Publisher Copyright:
© 2023 The Author(s)

Structured keywords

  • Water and Environmental Engineering

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