Interpolation of missing radar wind profiler data
We have developed and implemented a set of Python algorithms that combines a novel data interpolation scheme with the ability to systematically explore tradeoffs among different choices available to the user. The interpolation involves successive application of a principal component filter, with shorter intervals filled using linear interpolation. For estimation of the error associated with the interpolation, the missing data mask is shifted in time while preserving its phase relative to the diurnal cycle. The resulting interpolation performance is a function of altitude, and the choice of an optimal interpolation scheme and an optimal number of principal components depends upon how performance at different altitudes is weighted.
An outline of the algorithm and examples of its application to profiler data to the Second Texas Air Quality Study (TexAQS-II) will be presented.