Premium
Robust bivariate error detection in skewed data with application to historical radiosonde winds
Author(s) -
Sun Ying,
Hering Amanda S.,
Browning Joshua M.
Publication year - 2017
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2431
Subject(s) - radiosonde , bivariate analysis , outlier , meteorology , nonparametric statistics , skew , computer science , copula (linguistics) , wind speed , environmental science , statistics , mathematics , econometrics , geography , telecommunications
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.