Abstract: This study explores the forecasting of Beijing high mountain sea cloud to meet the demand for meteorological services for tourism and promote the development of the capital tourism economy. Toward this end, the spatiotemporal distributions, synoptic classification, vertical characteristics of the physical quantities, and formation and dissipation mechanisms of the Beijing high mountain cloud sea were investigated by utilizing multisource and three-dimensional detection and ERA5 reanalysis data and by cluster analysis. A cloud sea forecasting model was established by using the fuzzy logic algorithm, and its effect was evaluated. The results show that the cloud sea phenomenon occurs most frequently in summer, followed by autumn and winter, and occurs the least frequently in spring; its synoptic patterns can be classified into forward-tilting troughs, high ridges, and easterly winds. The vertical configurations of the cloud sea typically exhibit an “upper-dry and lower-moist” feature and inversion. The median relative humidity is greater than 80% at the levels below 850 hPa and less than 60% at the levels above 850 hPa. The strongest inversion of the gradient stations can reach up to 8°C. By combining microwave radiometer data, wind profile radar data, and the intensive gradient automatic weather station data, we can refine the vertical distribution and variation characteristics of temperature, humidity, and wind fields. These refined distributions and characteristics reflect the whole process of formation, maintenance, and dissipation of the cloud sea, thereby aiding cloud sea identification and nowcasting. Based on the increasing number of no-cloud-sea cases, a cloud sea probability forecasting model was established by using the fuzzy logic algorithm with relative humidity as a forecasting factor. This model has an accuracy of 94.5% and an F1 score of 0.81, indicating good forecasting performance and prospects for practical application and promotion.