A paper with Ying Sun of KAUST and former Master’s student Joshua Browning was recently accepted for publication in Environmetrics in which a new method for identifying outliers in skewed, heavy-tailed, multivariate distributions is developed.  The method is applied to wind observations from historical radiosonde launches in the National Center for Atmospheric Research archive.  The global historical radiosonde archives date back to the 1920’s and contain the only directly observed measurements of temperature, wind, and moisture in the upper atmosphere, but they contain many random errors. Most of the focus on cleaning these large datasets has been on temperatures, but winds are important inputs to climate models and in studies of wind climatology.