This study presents and compares two models for estimating motion in meteorological images sequences. The first method makes use of the grey level pixel conservation hypothesis. It produces a dense vector field through a variational formulation, and authorizes discontinuities in the resulting field. A second method use a model taking affine motion as ground hypothesis. Motion parameters are then estimated with an incremental least-square procedure. One of its principal advantages results in a modeling of the variation of the grey level values. The two methods are complementary: the second computes a global estimation of the motion, which is locally enhanced by the first.