NEW MODEL-BASED BAYESIAN INVERSION ALGORITHM FOR THE RETRIEVAL OF WETTROPOSPHERE PATH DELAY FROM RADIOMETRIC MEASUREMENTS

A new inversion algorithm has been developed for the retrieval of trop ospheric wet path delay from microwave radiometer measurements. The al gorithm is based on the principle of maximum probability and uses Baye s' theorem to determine the most probable state vector (comprised of d iscrete height profiles of temperature and water vapor density) for a given set of measurements. The solution probabilities depend both on c onformity to apriori statistics and a minimization of residuals betwee n measured observables and observables computed from candidate state v ectors. The new algorithm has been compared with standard statistical inversion techniques using simulations based on radiosonde, lidar, and acoustic sounding measurements of atmospheric temperature and water v apor profiles. For clear conditions the results indicate that the nonl inear algorithm produces a factor of 3-5 improvement in path delay ret rieval performance, relative to a nonstratified statistical algorithm, when the observational system includes both a water vapor radiometer and a microwave temperature profiler and measurement errors are minimi zed. The new algorithm is shown to be most useful when applied to radi ometric measurements of path delay fluctuations over minute to multiho ur timescales. Recommended applications include tropospheric calibrati on systems for radio science experiments such as very long baseline in terferometry astrometry and the planned Cassini gravitational wave exp eriment.