Abstract: Ground visibility had significant influences on transportation and human outdoor activities. Stationary visibility observations are usually spatially sparse, inadequate to meet the demand for complete spatial coverage data. To tackle the problem, we put forward a novel data fusion framework by incorporating an ensemble deep learning method and a Barnes objective analysis approach. The ensemble deep learning method works to generate high-quality first-guess field by building complex linkages between data sets. The Barnes objective analysis method was used to remove remaining residual errors and biases. Furthermore, it was designed to be hierarchical to better address the environmental factors such as particulate concentrations and relative humidity that greatly affect visibility in a sub-layer. The data fusion framework is able to include multiple data sources of different types, i.e.gridded WRF meteorological predictions, CMAQ air quality predictions, stationary meteorological and atmospheric composition observations and other supporting land use/cover data sets. The model was implemented to generate gridded hourly visibility data sets in China. The fused gridded data was evaluated against observations from independent monitors, with an R² = 0.61.Comparatively the R2 values of interpolation approach and linear regression fusion were respectively 0.55 and 0.57. Besides, our gridded fusion data have much more and detailed spatial information than that of the smoothed interpolation data. Our data fusion approach is relatively easy to implement in operational system and also has good extensibility to handily include more data sets.