Record Details
Statistical surface wind forecasting at Goodnoe Hills, Washington
ScholarsArchive at Oregon State University
| Field | Value |
|---|---|
| Title | Statistical surface wind forecasting at Goodnoe Hills, Washington |
| Names |
Curtis, Joel C.
(creator) Murphy, Allan H. (advisor) |
| Date Issued | 1983-03-09 (iso8601) |
| Note | Graduation date: 1983 |
| Abstract | Multiple linear regression was used to develop equations for 12-, 24-, and 36-hour surface wind forecasts for the wind energy site at Goodnoe Hills. Equations were derived separately for warm and cool seasons. The potential predictors included LFM II model output, MOS surface wind forecasts extrapolated from surrounding stations, pressure observations corrected to mean sea level, and two types of climatological variables. Forecasts of wind speed and direction were formulated for an independent sample of predictands and predictors. The forecasts were evaluated using standard methods of forecast verification and the results are summarized in terms of several verification scores. Comparisons of scores were made by season, projection time, and cycle (or preparation) time, and some patterns were evident in the scores with respect to these stratifications. The minimum value of the mean absolute error attained by the forecast system presented here was 5.64 mph for a 12-hour, cool season forecast equation. The minimum value of the root mean square error was 7.57 mph for a 12-hour, warm season forecast equation. Comparison of these results with the results of other statistical wind forecasting studies indicates that the forecast equations for Goodnoe Hills are of comparable accuracy to the equations developed for other wind energy sites. Suggestions for future investigations of statistical wind forecasting are offered as well as recommendations concerning ways of improving the forecasting system described in this study. |
| Genre | Thesis/Dissertation |
| Topic | Wind forecasting -- Statistical methods |
| Identifier | http://hdl.handle.net/1957/29024 |
