Abstract

Using GPS observations in conjunction with surface pressure and temperature to estimate precipitable water or PW has been evolving for the past decade. As both the GPS PW measurement and micro-wave radiometer or MWR measurement of PW at collocated sites became more accurate; a diurnal variation o... Show moreUsing GPS observations in conjunction with surface pressure and temperature to estimate precipitable water or PW has been evolving for the past decade. As both the GPS PW measurement and micro-wave radiometer or MWR measurement of PW at collocated sites became more accurate; a diurnal variation of the difference between the two independent measurements was noticed. GPS data do not provide a direct estimate of PW. GPS provide an estimate of the total zenith delay of the GPS signals. Removing the hydrostatic portion of this atmospheric delay based on surface pressure is quite accurate and leaves the additional delay due to water vapor called the wet delay. From the wet delay we can calculate a column amount of PW. This conversion depends on the mean temperature of the atmosphere. UCAR GPS PW estimates have been generated using the Bevis et al. (1994) approximation of linear coefficients to estimate the atmospheric mean temperature or Tmean based on surface temperature measurements. It has been found that using the Global Forecast System numerical weather model of GFS to compute the atmospheric mean temperature for converting GPS wet delay to PWV significantly reduces the diurnal variation with respect to MWR's, and also improves the mean difference and rms. between the two. Results comparing GPS PWV generated with Tmeans from the Bevis et al. (1994) coefficients and surface temperatures to GPS PWV generated with Tmeans computed using GFS model output from 2002 and 2003 compared to the ARM site MWR PWV values will be presented. Show less