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With the recent advance in Global Positioning System (GPS) atmospheric sensing technology, slant wet delay along each ray path can be measured with a few millimeters accuracy. In this study, the impact of slant wet delay is assessed on the short-range prediction of a squall line. Since the current GPS observation network in the central United States is not of high enough density to capture the mesoscale variation of moisture in time and space, a set of observing system simulation experiments is performed to assimilate slant wet delay data from a hypothetical network of ground-based GPS receivers using the four-dimensional variational data assim- ilation technique. In the assimilation of slant wet delay data, significant changes in moisture, temperature, and wind fields within the boundary layer were found. These changes lead to a stronger surface cold front and stronger convective instability ahead of the front. Consequently, the assimilation of slant wet delay produces a considerably improved 6-h forecast of a squall line in terms of rainfall prediction and mesoscale frontal structure. Previous studies have shown that the assimilation of GPS-derived precipitable water data can improve moisture analysis and rainfall prediction. In order to assess the additional value of slant wet delay data assimilation, a parallel experiment is performed in which precipitable water data is assimilated. The assimilation of slant wet delay data is demonstrated to be superior in recovering water vapor information between receiver sites and in short-range precipitation forecast both in terms of rainfall distribution and intensity. As revealed by atmospheric soundings in the vicinity of the squall line, the assimilation of slant wet delay data more accurately retrieves the temperature and moisture structure in the convectively unstable region.

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Variational Assimilation of Slant-Path Wet Delay Measurements from a Hypothetical Ground-Based GPS Network. Part I: Comparison with Precipitable Water Assimilation