Implementation of a new refractivity estimation algorithm on a network of S-band radars

The retrieval of the surface-layer moisture field can be obtained by estimating the refractive index of air, measured in parts per million and referred to as refractivity. A technique developed to estimate the refractivity using radar has been demonstrated with validated results from field experiments [1], [2]. This technique utilizes the measured change in phase from stationary ground targets, which will be primarily due to changes in refractivity at warmer temperatures given a stable radar frequency. While this technique has been successfully demonstrated on individual radars, there is no clearly defined method of combining multiple radar estimations beyond gridding and averaging. Recently, however, a new algorithm was proposed as an alternative approach, especially when dealing with multiple radars [3]. The new algorithm uses a minimum least squared error approach, with a smoothing constraint and a method to address phase wrapping. This paper will explore the implementation of this constrained least squares (CLS) approach with data collected during a refractivity field experiment during the summer of 2006 in Colorado. It will also explore the implications of running the CLS algorithm in real-time, and briefly discuss future directions.