Wet Refractivity Tomography with an Improved Kalman-Filter Method

An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering （KF） method, was developed to retrieve the wet refractivity profiles from slant wet delays （SWD） extracted by the double-differenced （DD） GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer （WVR） and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km^- 1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.

Wet refractivity tomography with an improved Kalman-Filter method

An improved retrieval method, which uses the solution with a Gaussian constraint as the initial state variables for the Kalman Filtering (KF) method, was developed to retrieve the wet refractivity profiles from slant wet delays (SWD) extracted by the double-differenced (DD) GPS method. The accuracy of the GPS-derived SWDs is also tested in this study against the measurements of a water vapor radiometer (WVR) and a weather model. It is concluded that the GPS-derived SWDs have similar accuracy to those measured with WVR and are much higher in quality than those derived from the weather model used. The developed method is used to retrieve the 3D wet refractivity distribution in the Hong Kong region. The retrieved profiles agree well with the radiosonde observations, with a difference of about 4 mm km?1 in the low levels. The accurate profiles obtained with this method are applicable in a number of meteorological applications.