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The widespread availability of GPS-enabled mobile devices has contributed towards an unprecedented volume of data on human movement. Human mobility data are the key input for developing accurate mobility models that can support decision-making in, for example, urban planning, transportation planning and disease spread. However, the increasing geoprivacy concerns have been limiting the use of and access to such data. For this reason, the WHO-WHERE-WHEN (3W) model, a privacy-protective model for generating synthetic mobility data, has been developed. However, human mobility is affected by multiple factors that must be accounted for to produce synthetic mobility trajectories that accurately simulate the fluctuations of population in a study area. The 3W model already considers four main factors affecting human mobility: size and shape of activity spaces, circadian rhythm, and home and work locations. Yet, meteorological factors are known to affect human mobility patterns but, to our knowledge, there is not a model that accounts for weather conditions. In this paper, we propose a theoretical framework to extend the 3W model to a 4W model: WHO-WHERE-WHEN-WEATHER. We hypothesise that accounting for weather conditions in human mobility predictions will increase the overall accuracy of predicted mobility patterns.