<p>During the operation of meteorological satellites, their orbits are affected by the non-uniform gravitational field of the Earth, atmospheric resistance, solar radiation pressure, and other factors, causing perturbations in the satellite’s orbit. These perturbations can cause deviations between the actual and theoretical positions of the satellite. If the orbit parameters of the satellite cannot be accurately determined, it will be difficult to accurately obtain the geographic location information of the cloud map data, thereby affecting the accuracy of geometric correction. Therefore, a geometric correction method for the position of meteorological satellite cloud map data under the temporal correlation of multi-source data is studied. Construct an imaging model for meteorological satellite cloud images based on the conversion between the geocentric coordinate system, geodetic coordinate system, and map projection coordinate system. Secondly, through time series correlation analysis, the time series correlation between meteorological satellite cloud image data and ground cloud image key points in other geographic spatial data can be mined to accurately obtain cloud image key points and improve the accuracy of geometric correction. Finally, for the key points of the obtained meteorological satellite cloud images, a polynomial model is used for geometric correction. By solving the error equation of the key points in the cloud images, polynomial coefficients are obtained to complete the correction of geometric distortions. The experimental results show that the highest position error of the corrected satellite cloud image data is only 0.13, and the duration fluctuates between 1.2 and 1.8&#xa0;s. The F1 value always remains above 0.92, which is practical.</p>