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Atmospheric water vapor content or total precipitable water (TPW) is a highly variable atmospheric constituent, yet it remains one of the meteorological parameters that is most difficult to characterize accurately. We develop a framework for estimating atmospheric TPW from Visible Infrared Imaging Radiometer Suite (VIIRS) data in this study. First, TPW is retrieved from VIIRS top-of-atmosphere (TOA) radiance of channels 15 and 16 using the refined split-window covariance-variance ratio (SWCVR) method. Then, the VIIRS TPW is blended with the microwave integrated retrieval system (MIRS) derived TPW via Bayesian model averaging (BMA) to improve the accuracy of VIIRS TPW. Three years (2014-2017) of ground measurements collected from SuomiNet sites over North America are used to validate the VIIRS TPW and blended TPW. The mean bias error (MBE) and root mean square error (RMSE) of the VIIRS TPW are 0.21 g/cm(2) and 0.73 g/cm(2), respectively, and the accuracy of the VIIRS TPW in daytime is much better than at night time. The MBE and RMSE of BMA integrated TPW are 0.06 g/cm(2) and 0.35 g/cm(2), and the accuracy difference between daytime and nighttime is also removed. The global radiosonde measurements are also collected to validate the BMA integrated VIIRS TPW. The MBE and RMSE of the BMA integrated TPW are 0.09 g/cm(2) and 0.44 g/cm(2) compared to the radiosonde measurements. This accuracy is also superior to the VIIRS TPW. Therefore, it is concluded that the developed framework can be used to derive accurate clear-sky TPW for VIIRS. This is the first time that we can obtain high accuracy TPW from VIIRS. This study will certainly benefit the study of atmospheric processes and climate change.