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Using covariates to model dependence in nonstationary, high-frequency meteorological processes



This article is corrected by:

  1. Errata: Erratum: Using covariates to model dependence in nonstationary, high frequency meteorological processes Volume 25, Issue 7, 557, Article first published online: 13 August 2014


Meteorological processes that are measured at high temporal frequencies require nonstandard statistical models to adequately characterize their observed behavior. We examine this problem through the specific example of a nonstationary, bivariate process—the high-frequency changes in surface temperature and dew point—conditionally on the average hourly level of relative humidity, magnitude of minute-to-minute changes in wind direction, and presence of sunlight. One particularly interesting aspect of this process is that dew point is bounded above by temperature, and so a part of the modeler's challenge is to characterize the joint behavior as temperature approaches dew point (or equivalently as relative humidity approaches 100%). The data analyzed are from early May from the years 2003 through 2012 at the Atmospheric Radiation Measurement Program's Southern Great Plains site in Northern Oklahoma, at the central facility near Lamont, Oklahoma. Our model gives a parametric description of how the spectral matrix of the process varies with covariates over blocks of time. The spectral approach allows for convenient and interpretable models of bivariate processes in time, and our model captures many of the observed changes in the behavior of the process. Copyright © 2014 John Wiley & Sons, Ltd.