Use of satellite and ground based remote sensing observations has become more and more important in atmospheric and environmental research. Numerous sensors have been developed to retrieve atmospheric compositions including aerosols and trace gases, providing unprecedented opportunities to improve climate and air quality modeling and forecasting. On one hand, observations by different sensors can be joined to reveal a more complete picture of the composition of the atmosphere. For example, MODIS and CALIPSO measurements together form a 3D view of atmospheric aerosols. Observations of aerosol precursor such as SO₂ and NO₂ can provide insights into the causes of aerosol variability. Many research works have been conducted to understand the formation and evolution of air pollution using multiple observational datasets. On the other hand, because different sensors have different optical design, sampling frequency, and spatial/temporal resolution characteristics, it is necessary to explore the integrated usage of these multi-sensor data in order to maximize their capabilities. For example, satellite can provide spatial variability of atmospheric component, whereas ground based sensors typically have higher accuracy and temporal resolution. These two types of observations can thus be combined to for more accurate information with high spatial and temporal coverage. Many data synergy or data fusion techniques have been developed for this purpose. Considering the importance of combining multiple datasets in climate and environmental research, it is quite appropriate and necessary to hold a session about this topic at the 100^th AMS annual meeting, in order to better demonstrate recent progresses and to promote collaborate. This session will cover works including the validation/cross validation of multi-sensor data, developments of data fusion techniques, joint retrieval of atmospheric composition (including aerosols and trace gases) using multiple satellite and ground based platforms, joint application of multi-sensor data in climate and environmental problems and the assimilation of multi-sensor data in weather, climate and air quality modeling and forecasting.