3.4
Maximizing the value of short-term observational data using numerical weather prediction models

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Wednesday, 26 January 2011: 11:45 AM
Maximizing the value of short-term observational data using numerical weather prediction models
4C-4 (Washington State Convention Center)
Scott J. Eichelberger, 3TIER, Inc., Seattle, WA; and J. McCaa and P. Storck

The presentation will demonstrate how incorporating short-term, on-site observational data with mesoscale numerical weather prediction (NWP) model output provides increased certainty when estimating the long-term mean wind resource conditions. Several examples will be shown where this methodology allowed developers to confidently make decisions months earlier in the development process as compared to using only the available on-site observational data. By allowing developers to understand the long-term variability of a site earlier in the development cycle, potential project sites can be more readily sorted ensuring that resources are spent only on truly viable projects.

The mistake is often made of collecting measurement data over a short period (1-18 months), and then assuming the data represent mean conditions. In truth, measured on-site data are merely a random snapshot without any long-term historical context. Assuming that short-term, on-site observational data are representative of long-term mean conditions have significant financial consequences as short-term records can easily vary by +/-10% of the long-term mean.

Incorporating short-term, on-site observational data with 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 on-site observational data in the context of a long-term record. On-site data are integral to the process and are used to statistically correct raw model data and to generate uncertainty estimates. Unlike traditional Measure-Correlate-Predict (MCP) analysis, NWP models do not require off-site reference station data; thus, NWP-based methodologies are extremely valuable at locations where suitable off-site data for MCP analysis are unavailable.

Since the NWP methodology can be applied using only a few months of on-site data, developers can understand the long-term variability early on and confidently sort potential sites ensuring resources are spent on only truly viable projects. Furthermore, this methodology can be configured into a monthly-recurring, automated system further increasing the efficiency and decreasing costs. Blending the best of direct on-site measurements with advanced NWP techniques provides a reliable, efficient, and scalable methodology for understanding the long-term wind resource variability.