Total variation of atmospheric data: covariance minimization about objective functions to detect conditions of interest

Abstract. Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation. Most often, conditions of interest are determined as those that occur most frequently, given the need for well-converged statistics from observations against which model results are compared. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operational strategies. The current work explores a single metric for variation of a multivariate data sample that quantifies variability within each channel as well as covariance between channels. The total variation is used to identify periods of interest that conform to desired objective functions, such as quiescent conditions, ramps or waves of wind speed, and changes in wind direction. The direct detection and classification of events or periods of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.