Bivariate Functional Quantile Envelopes With Application to Radiosonde Wind Data

Abstract

The global radiosonde archives contain valuable weather data, such as temperature, humidity, wind speed, wind direction, and atmospheric pressure. Being the only direct measurement of these variables in the upper air, they are prone to errors. Therefore, a robust analysis and outlier detection of radiosonde data is essential. Among all the variables, the radiosonde winds, which consist of wind speed and direction, are particularly challenging to analyze. In this article, we treat the wind profiles as bivariate functional data across several pressure levels. Since the bivariate distribution of the components of radiosonde winds at a given pressure level is not Gaussian but instead skewed and heavy-tailed, we propose a set of robust quantile methods to characterize the distribution as well as an outlier detection procedure to identify both magnitude and shape outliers. The proposed methods provide an informative visualization tool for bivariate functional data. We also introduce two methods of predicting this bivariate distribution at unobserved pressure levels. In our simulation study, we show that our methods are robust against different types of outliers and skewed data. Finally, we apply our methods to radiosonde wind data to illustrate our proposed quantile analysis methods for visualization, outlier detection, and prediction.

Keywords:

• Bivariate functional data
• Cubic spline smoothing
• Kriging
• Outliers
• Quantile contours

Additional information

Funding

The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number OSR-2019-CRG7-3800. The dataset used in this research was provided by the Research Data Archive at the National Center for Atmospheric Research (NCAR), Computational and Information Systems Laboratory.