Directional measurements such as wind directions are often recorded in a finite number of angular categories rather than as exact angles. When two such measurements are observed jointly, the resulting bivariate observations lie on a discrete torus. Commonly used bivariate circular models are formulated for continuous angular variables. Applying these models to categorical observations requires integrating their densities over regions corresponding to observed category pairs. We propose two parametric models defined directly on the discrete torus, with interpretable parameters for marginal locations and concentrations, and for dependence between the two circular variables. The models provide closed-form probability mass functions and trigonometric moments, which are used to show that, under certain conditions, the dependence parameter characterizes circular--circular correlation. Parameters are estimated by maximum likelihood, and the finite-sample performance is investigated through simulation. The proposed models are applied to three datasets of paired wind direction measurements recorded in 16 equally spaced compass directions at stations in India and compared with discretized versions of established continuous bivariate circular models. They provide competitive fits while allowing likelihood evaluation directly on the observed discrete support. The fitted models are also used to assess the dependence between the paired wind directions in each dataset.

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