Retrieval of Water Vapor Profiles from GPS/MET Radio Occultations

Abstract

Present Global Positioning System Meteorology (GPS/MET) refractivity profiles cannot distinguish between refractivity effects due to water vapor and those due to air density. Current methods of resolving the ambiguity rely heavily on ancillary upper-air data, such as National Centers for Environmental Prediction and European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. However, the accuracy of these ancillary sources suffers in regions where upper-air data are sparse. A method of separating the water vapor and temperature effects in GPS/MET-derived refractivity profiles with the addition of only ancillary surface pressure and temperature data and the hydrostatic assumption is discussed. Water vapor and temperature data derived from this method are presented and compared with accepted values. This method allows for the construction of temperature profiles with a mean bias of 0.33 K and a mean standard deviation of 1.86 K when compared with ECMWF data from 30 to 1000 mb. Height fields can also be corrected to within an average bias of 6 m and a standard deviation of 31 m. These corrected profiles result in retrieved water vapor pressure profiles with an average bias of 0.19 mb and a standard deviation of 0.53 mb.

On the detection of water vapor profiles and thin moisture layers from atmospheric radio occultations

“…Of these selected 3202 occultations, 746, about 23%, still led to negative water vapor pressure values at some heights when the temperature from NCEP was assumed correct and substituted into equation (1) for the standard processing. The mean and standard deviation of the differences with NCEP and the fit procedure are shown in Figure 4 as a function of pressure to enable easy comparison with a previous attempt to infer water vapor without using models [O'Sullivan et al, 2000]. Figure 4a shows the 2466 cases where water vapor pressure remained positive at all heights after combining NCEP's temperature and the observed refractivity in equation (1).…”

Validation of Water Vapour Profiles from GPS Radio Occultations in the Arctic

“…Also the humidity retrieval of GPS RO suffers from uncertainties in the analyses. Therefore, new approaches have been discussed in literature using statistical methods or ground-based meteorological data instead (Healy and Eyre 2000;O'Sullivan et al 2000). Further investigation will show whether these methods can overcome the problems in the remote Arctic region.…”

Atmospheric stability index using radio occultation refractivity profiles

“…We note that refractivity from radio occultation is available at different heights rather than pressure levels. However, if one knows surface pressure and temperature, one can estimate pressure as a function of height using refractivity profile reasonably accurately using the technique given by O'Sullivan et al (2000). It is found that O'Sullivan et al (2000) technique gives very minimal errors in the estimation of pressure profiles (RMS error less than 2 hPa around 500 hPa level) from refractivity and almost similar to pressure profile retrieved from 1-d variational assimilation technique (Jagadheesha et al 2009).…”

“…Given the refractivity profiles, one can derive atmospheric temperature, pressure, and humidity profiles either by using 1-d variational assimilation method (e.g., Gorbunov and Sokolovskiy 1993;Healy and Eyre 2000;Palmer et al 2000;Gorbunov and Kornblueh 2003;von Engeln et al 2003) or by other methods which use surface or a lower level atmospheric temperature and pressure (O'Sullivan et al 2000;. 1-d variational assimilation technique uses atmospheric temperature, pressure and humidity profiles from numerical model reanalysis/forecasts as a priori information.…”