Abstract
The sea surface roughness parameterization and universal stability functions, as key components of the evaporation duct prediction models rooted in the Monin-Obukhov similarity theory, dictate the model performance which further impacts the efficiency and accuracy of offshore electromagnetic applications. In this paper, layered meteorological and hydrological observations are collected during two cruises and processed to obtain the reference modified refractivity profiles close to the sea surface, and then particle swarm algorithm is utilized to optimize the parameters of the sea surface roughness parameterization and universal stability functions. The results show that compared with the pre-optimization model, the prediction accuracy of the optimized model is improved by 5.09% and 8.12% under stable conditions, and by 9.97% and 31.51% under unstable conditions for observation dataset from each cruise, which proves the feasibility of the proposed method for evaporation duct prediction model optimization.
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