Abstract:

This paper presents an approach to approximating Rossby waves using the Two-Strategy adaptive Artificial Bee Colony (TSaABC) algorithm with hard thresholding. Rossby waves, as large-scale planetary waves, play a critical role in atmospheric dynamics, influencing meteorological phenomena and climate patterns. The study employs the TSaABC algorithm to optimize the parameters of a nonlinear space-time model representing atmospheric temperature data, drawn from the Aura (MLS) satellite.  By solving the inverse problem involving minimizing the data discrepancy and $L_{1}$-norm of harmonic amplitudes, the method achieves a good accuracy and sparsity in the large dictionary of harmonics. To solve $L_{1}$-minimization, we design hard thresholding strategy within TSaABC. The implementation of hard thresholding allows for a reduction in dimensionality, which enhances computational efficiency. The results highlight the algorithm's potential for improving atmospheric modeling and forecasting.