Validation and sensitivity analysis of the FLEXPART model using the Kincaid experiment data
FLEXPART, a Lagrangian particle dispersion model, is widely used in atmospheric dispersion and nuclear emergency response studies. However, a systematic sensitivity analysis of this model is still lacking. This study, which utilizes meteorological data output from the WRF model (with 1 km spatial re...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2025-06-01
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| Series: | International Journal of Advanced Nuclear Reactor Design and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468605025000481 |
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| Summary: | FLEXPART, a Lagrangian particle dispersion model, is widely used in atmospheric dispersion and nuclear emergency response studies. However, a systematic sensitivity analysis of this model is still lacking. This study, which utilizes meteorological data output from the WRF model (with 1 km spatial resolution and 1 h temporal resolution) to drive the FLEXPART model, validates FLEXPART using the Kincaid experiment data and assesses its sensitivity to key parameters: particle number, lower bound of the turbulence intensity, and grid size. The simulation results demonstrate that the model adequately reproduces the plume dispersion pattern and covers most monitoring stations. Furthermore, the numerical results show high consistency with the observational data (FAC2: 0.50; FB: −0.22; NMSE: 1.01). The sensitivity results indicate that the particle number and the grid size significantly influence the model performance. It is recommended to use a particle number of 1000 particles/min, a lower bound of the turbulence intensity of (0.6, 0.6, 0.3) m/s, a horizontal grid size of 100 m, and a vertical grid size of 10 m for similar scenarios. These findings offer actionable guidance for parameter selection in nuclear emergency scenarios and similar dispersion studies, supporting improved model reliability and applicability. |
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| ISSN: | 2468-6050 |