Introduction

The mistake is often made of collecting measurement data over a short period (3-18 months), and then assuming the data represent mean conditions. In truth, measured on-site data are at best a random snapshot without any long-term historical context. For data records less than one year in length, the systematic error associated with the seasonal cycle can be extreme. Since short-term records can easily vary by +/-10% of the long-term mean, assuming that short-term, on-site observational data are representative of long-term mean conditions can result in significant financial consequences (Figure 1 of full poster). Unfortunately, in many areas of the world, suitable (high quality and long record) long-term reference measurement stations are not available, leading to simple extrapolation of the on-site data or the use of globally available reanalysis data as a long-term reference.

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Incorporating short-term, on-site observational data with mesoscale numerical weather prediction (NWP) model output provides increased certainty when estimating long-term wind resource variability. NWP models can simulate past periods of time to create a complete climatology that places the on-site data observational data in context of the long-term record. On-site data are integral to the process and are used to statistically correct raw model data and to provide uncertainty analysis. NWP models do not require off-site reference station data, unlike MCP analysis. Thus, NWP-based methodologies are extremely valuable at locations where suitable off-site data are unavailable.

Blending the best of direct on-site measurements with advanced NWP techniques provides a reliable, efficient, and globally available methodology for understanding the long-term wind resource variability. We compare this approach with simple extrapolation of on-site data and the use of the raw NCEP/NCAR Reanalysis data as the long-term reference.

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