An Examination of the Uncertainty in Interpolated Winds and Its Effect on the Validation and Intercomparison of Forecast Models

Abstract

Meteorological models need to be compared to long-term, routinely collected meteorological data. Whenever numerical forecast models are validated and compared, verification winds are normally interpolated to individual model grid points. To be statistically significant, differences between model and verification data must exceed the uncertainty of verification winds due to instrument error, sampling, and interpolation. This paper will describe an approach to examine the uncertainty of interpolated boundary layer winds and illustrate its practical effects on model validation and intercomparison efforts. This effort is part of a joint model validation project undertaken by the Environmental Verification and Analysis Center at the University of Oklahoma (http://www.evac.ou.edu) and the Battlefield Environment Directorate of the Army Research Laboratory. The main result of this study is to illustrate that it is crucial to recognize the errors inherent in gridding verification winds when conducting model validation and intercomparison work. Defendable model intercomparison results may rely on proper scheduling of model tests with regard to seasonal wind climatology and choosing instrument networks and variogram functions capable of providing adequately small errors due to sampling and imperfect modeling. Thus, it is important to quantify verification wind uncertainty when stating forecast errors or differences in the accuracy of forecast models.

Verification with Variograms

“…As such, a forecast field that has been smoothed in this way will necessarily yield higher quality forecasts, in terms of variograms, when compared with forecasts that have been smoothed in some variogram-independent fashion. Since the spatial structure is an important facet of forecast quality, this suggests that one should incorporate variograms into the analysis phase of NWP modeling, as is proposed by S xen (1997) and Greene et al (2002).…”

“…For example, S xen (1997) uses variograms as a basis of an interpolation scheme for performing analysis in NWP models. Greene et al (2002) utilize kriging (Cressie 1993)-wherein one fits variograms-to interpolate wind fields. Germann and Joss (2001) use variograms to find the spatial variation of the precipitation rate in the European Alps, the dependence of this rate on temporal and spatial averaging, and how precipitation measurements from two or more instruments can be compared.…”