Robust bivariate error detection in skewed data with application to historical radiosonde winds
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2017-01-17Online Publication Date
2017-01-17Print Publication Date
2017-05Permanent link to this record
http://hdl.handle.net/10754/623903
Metadata
Show full item recordAbstract
The global historical radiosonde archives date back to the 1920s 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. The bivariate distribution of the wind vector does not have elliptical contours but is skewed and heavy-tailed, so we develop two methods for outlier detection based on the bivariate skew-t (BST) distribution, using either distance-based or contour-based approaches to flag observations as potential outliers. We develop a framework to robustly estimate the parameters of the BST and then show how the tuning parameter to get these estimates is chosen. In simulation, we compare our methods with one based on a bivariate normal distribution and a nonparametric approach based on the bagplot. We then apply all four methods to the winds observed for over 35,000 radiosonde launches at a single station and demonstrate differences in the number of observations flagged across eight pressure levels and through time. In this pilot study, the method based on the BST contours performs very well.Citation
Sun Y, Hering AS, Browning JM (2017) Robust bivariate error detection in skewed data with application to historical radiosonde winds. Environmetrics 28: e2431. Available: http://dx.doi.org/10.1002/env.2431.Sponsors
The authors would like to thank Adelchi Azzalini, Douglas Nychka, SteveWorley, and Joey Comeaux for discussions and ideas that helped to direct and focus this work. In addition, we are grateful to two referees for providing useful comments on the paper. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award OSR-2015-CRG4-2582.Publisher
WileyJournal
EnvironmetricsDOI
10.1002/env.2431Additional Links
http://onlinelibrary.wiley.com/doi/10.1002/env.2431/abstractae974a485f413a2113503eed53cd6c53
10.1002/env.2431