A Fractional Time¿Space Stochastic Advection¿Diffusion Equation for Modeling Atmospheric Moisture Transport at Ocean¿Atmosphere Interfaces

• Type: Article in International Scientific Journal
• Date: 2025

Authors

Moghaddam, BP (Author) Other; Zaky, MA (Author) Other; António Mendes Lopes (Author) FEUP; Galhano, A (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: FRACTAL AND FRACTIONAL

Vol. 389

Final page: 211

ISSN: 2504-3110

Other information

Authenticus ID: P-018-HFT

DOI: 10.3390/fractalfract9040211

Abstract (EN): <jats:p>This study introduces a novel one-dimensional Fractional Time¿Space Stochastic Advection¿Diffusion Equation that revolutionizes the modeling of moisture transport within atmospheric boundary layers adjacent to oceanic surfaces. By synthesizing fractional calculus, advective transport mechanisms, and pink noise stochasticity, the proposed model captures the intricate interplay between temporal memory effects, non-local turbulent diffusion, and the correlated-fluctuations characteristic of complex ocean¿atmosphere interactions. The framework employs the Caputo fractional derivative to represent temporal persistence and the fractional Laplacian to model non-local turbulent diffusion, and incorporates a stochastic term with a 1/f power spectral density to simulate environmental variability. An efficient numerical solution methodology is derived utilizing complementary Fourier and Laplace transforms, which elegantly converts spatial fractional operators into algebraic expressions and yields closed-form solutions via Mittag¿Leffler functions. This method¿s application to a benchmark coastal domain demonstrates that stronger advection significantly increases the spatial extent of conditions exceeding fog formation thresholds, revealing advection¿s critical role in moisture transport dynamics. Numerical simulations demonstrate the model¿s capacity to reproduce both anomalous diffusion phenomena and realistic stochastic variability, while convergence analysis confirms the numerical scheme¿s robustness against varying noise intensities. This integrated fractional stochastic framework substantially advances atmospheric moisture modeling capabilities, with direct applications to meteorological forecasting, coastal climate assessment, and environmental engineering.</jats:p>

Language: English

Type (Professor's evaluation): Scientific