Strong wind gust can cause severe damages, such as power outages, which are major concerns for emergency management of utility companies. Yet, gust speed is not a standard output from weather forecasting models. In this paper, we proposed a Bayesian hierarchical model which combines historical meteorological observations and weather model outputs to forecast hourly gust speed. The exploratory analysis suggested a two-step sequential spatio-temporal model as follows. In the first step, we calibrated sustained wind speed forecast and in the second step, we forecasted gust speed based on calibrated wind forecast. We demonstrated the use of our model by a real application concerning gust-caused damages for a local power utility company from New York area.
By: Hongfei Li; Fei Liu; Jonathan R. M. Hosking; Yasuo Amemiya
Published in: RC25087 in 2010
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.
Questions about this service can be mailed to reports@us.ibm.com .