| Issue |
E3S Web Conf.
Volume 604, 2025
The 4th International Conference on Disaster Management (The 4th ICDM 2024)
|
|
|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 9 | |
| Section | Disaster Monitoring, Broadcasting, Early Warning and Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202560404002 | |
| Published online | 16 January 2025 | |
The performance of the weather research & forecasting model (WRF) using ensemble method to predict weather parameters
1 Department of Atmospheric and Planetary Science, Institut Teknologi Sumatera, Lampung 35365, Indonesia
2 Department of Environmental Engineering, Sepuluh Nopember Institute of Technology, East Java 60111, Indonesia
3 Research Centre for Climate and Atmosphere, National Research and Innovation Agency of Indonesia, West Java 40135, Indonesia
* Corresponding author: alvin.pratama@sap.itera.ac.id
The performance of the WRF model is evaluated using different physical options for the Riau Province. There are 12 members, and the ensemble mean method is used to evaluate the temperature, humidity, wind direction, and wind speed. The primary purpose of this study is to determine the appropriate parameterization for the study area. In this study, two nested domains have been used for performance analysis, with the resolution of the coarser domain set at 9 km and the inner domain set at 3 km. The model was run for five days in 2019 during a forest fire episode in Riau Province. The analysis was carried out by looking at the values of the correlation coefficient, root mean square error (RMSE), and bias. From the weather forecast, WRF, with the sixth parameterization member, produces a better value than the others. RRTM and Dudhia parameterization gave better results for temperature parameters. Meanwhile, the parameterization of Yonsei University (YSU) produces better results for the parameters of wind direction and wind speed.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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