In the field of spatial aeronomy, atmospheric profile datasets often contain partial data. Probabilistic models, particularly Gaussian processes (GPs), offer promising solutions for filling these data gaps. However, traditional GP algorithms encounter challenges when handling multiple sequences simultaneously, both in terms of performance and computational complexity. Recently, an algorithm named MAGMA was introduced to address these issues. This paper evaluates MAGMA’s performance using the SOIR Venus atmosphere dataset, marking the first application of MAGMA to atmospheric profiles. Results indicate that MAGMA represents a significant advancement towards the efficient application of GPs for extrapolating atmospheric profiles.