The application discloses a
meteorological satellite cloud map extrapolation method based on a geographical attention mechanism, characterized by a
deep learning framework constructed by GeoAttX, the introduction of a geographical attention mechanism to enhance the modeling ability of spatial features, and the synergistic effect of geographical semantic modeling, prediction path optimization and distribution correction mechanism, using the
observation data of meteorological satellites to realize high-precision and low-error accumulation cloud prediction. The method uses a reverse
greedy algorithm to realize flexible forecast time by reducing the autoregressive step, and proposes a second-order
standardization method to normalize the pixel distribution, to maintain the consistency of brightness statistics and reduce error propagation. Compared with the prior art, the application has the advantages of using real-time stationary
meteorological satellite observation data, having error suppression capability, geographical structure modeling capability, long-term stable prediction capability, high precision and stability, and can effectively improve the cloud motion extrapolation capability, and has important application value for
weather forecasting and disaster warning.