There are two main types of methods available to obtain precipitable water vapor (
PWV) with high accuracy. One is to assimilate observations into a numerical weather prediction (NWP) model, for example, the Weather Research and Forecasting (WRF) model, to improve the
            
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            There are two main types of methods available to obtain precipitable water vapor (
PWV) with high accuracy. One is to assimilate observations into a numerical weather prediction (NWP) model, for example, the Weather Research and Forecasting (WRF) model, to improve the accuracy of meteorological parameters, and then obtain the 
PWV with improved accuracy. The other is the direct fusion of multi-source 
PWV products. Regarding the two approaches, we conduct a comparison experiment on the West Coast of the United States of America with the data from May 2018, in which the WRF data assimilation (DA) system is used to assimilate the Global Navigation Satellite System (GNSS) 
PWV, while the method by Zhang et al. to fuse the GNSS 
PWV, ERA5 
PWV and MODIS (moderate-resolution imaging spectroradiometer) 
PWV. As a result, four groups of 
PWV products are generated: the assimilated GNSS 
PWV, the unassimilated GNSS 
PWV, 
PWV from the fusion of the GNSS 
PWV and ECWMF (European Centre for Medium-Range Weather Forecasts) ERA5 (ECWMF Reanalysis 5) 
PWV, and 
PWV from the fusion of the GNSS 
PWV, ERA5 
PWV and MODIS 
PWV. Experiments show that the data assimilation based on the WRF model (WRFDA) and adopted fusion method can generate 
PWV products with similar accuracy (1.47 mm vs. 1.52 mm). Assimilating the GNSS 
PWV into the WRF model slightly improves the accuracy of the inverted 
PWV by 0.18 mm. The fusion of the MODIS 
PWV, GNSS 
PWV and ERA5 
PWV results in a higher accuracy than the fusion of GNSS 
PWV and ERA5 
PWV by a margin of 0.35 mm. In addition, the inland canyon topography appears to have an influence on the inversion accuracy of both the methods.
            
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