International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)

International Journal Of Coastal, Offshore And Environmental Engineering(ijcoe)

Impact of Atmospheric Forcing Data Quality on Numerical Prediction of Coupled Atmosphere–Ocean Dynamics: Tropical Cyclone Asna in the Northern Indian Ocean

Document Type : Case Studies

Authors
1 Khoramshahr University
2 Khoramshahr university
3 elm o sanat university
10.22034/ijcoe.2025.555750.1202
Abstract
Accurate prediction of atmosphere–ocean conditions in coastal and offshore regions requires high-quality input data and reliable coupled atmosphere–ocean modeling. In this study, two datasets—FNL forecast data and GFS reanalysis data—were employed to evaluate the performance of atmospheric and wave models in reconstructing the conditions of Tropical Cyclone Asna (2024) over the northern Indian Ocean. The results showed that limitations in the quality of FNL forecast data led to an inadequate representation of the intensity and structure of surface winds, and this weakness was directly reflected in the WWIII wave model. Consequently, the simulated significant wave height (SWH) was underestimated compared with observational or reanalysis values, and the timing of the wave peaks exhibited delays and unrealistic fluctuations. In contrast, the GFS reanalysis data successfully reproduced the wave growth and decay processes as well as the spatiotemporal structure of the storm with higher accuracy. However, due to their retrospective nature, these data are not applicable for operational real-time forecasting.

These findings clearly indicate that the lack of high-quality and up-to-date atmospheric input data is the main obstacle to producing accurate atmosphere–ocean forecasts in the target regions, and that relying solely on global operational datasets such as FNL or GFS cannot fully capture the dynamic and thermodynamic characteristics of tropical cyclones. Accordingly, the necessity of developing and implementing a native coupled atmosphere–ocean model at both global and regional scales is evident. Such a model, by integrating local observational data and advanced data assimilation techniques, can provide more accurate initial and boundary conditions and enable the production of forecasts with higher accuracy and reliability. Achieving this capability will be a fundamental step toward strengthening scientific and operational capacity in physical oceanography and will pave the way for applying modeling results to secondary marine and coastal activities such as crisis management, maritime safety, coastal infrastructure design, and the sustainable exploitation of marine resources.
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Articles in Press, Accepted Manuscript
Available Online from 24 December 2025