Daniel Burtch

Date of Award

January 2014

Document Type


Degree Name

Master of Science (MS)


Atmospheric Sciences

First Advisor

Gretchen Mullendore


The Northern Plains region of the United States is home to a significant amount of potential wind energy. However, in winter months capturing this potential power is severely impacted by the meteorological conditions, in the form of icing. Predicting the expected loss in power production due to icing is a valuable parameter that can be used in wind turbine operations, determination of wind turbine site locations and long-term energy estimates which are used for financing purposes. Currently, losses due to icing must be estimated when developing predictions for turbine feasibility and financing studies, while icing maps, a tool commonly used in Europe, are lacking in the United States. This study uses the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset in conjunction with turbine production data and in-situ wind measurements to investigate six methods of predicting seasonal losses (October-March) due to icing at two sites located in Petersburg, ND and Valley City, ND. The prediction of icing losses is based on temperature and relative humidity thresholds and is accomplished using six methods. Three methods use a Measure-Correlate-Predict (MCP) and flow model (WAsP) analysis for the determination of wind speeds and MERRA for temperature and relative humidity, while three methods use MERRA for all three variables. For each season from 2002 to 2010, the predicted losses due to icing are determined for a range of relative humidity thresholds and compared with observed icing losses. An optimal relative humidity is then determined and tested on all seasons from 2002 to 2013. The prediction methods are then compared to a common practice used in the wind energy industry of assuming a constant percentage loss for icing over the same time period. The three methods using MERRA data alone show severe deficiencies in the accurate determination of wind speeds which leads to a large underprediction in accurate power output. Of the three MCP/WAsP methods, the method using boundary-layer similarity theory to determine temperature and relative humidity shows the most accuracy in predicting icing losses closest to observed losses at the Petersburg location with an average absolute difference of 2.13 % ± 1.29 %. This is a significant improvement over using a constant value which produces an average absolute difference of 5.87 % ± 4.64 %. Analysis at the Valley City location shows similar results when the datasets are retrained to that location.