This work introduces a novel solution to approximate in real time the 2D wind flow present in a geometrically known environment. It is grounded on the probabilistic
framework provided by a Markov random field and enables the
estimation of the most probable wind field from a set of noisy observations, for the case of incompressible and steady wind flow. Our method delivers reasonably precise results without falling into common unrealistic assumptions like homogeneous wind
flow, absence of obstacles, etc., and performs very efficiently (less
than 0.5 seconds for an environment represented with a 100x100
cell grid). This approach is then quite suitable for applications
that require real-time estimation of the wind flow, as for example, the localization of gas sources, prediction of the gas dispersion, or the mapping of the gas distribution of different chemicals released in a given scenario.