EAS Seminar: Daniel Kirk-Davidoff (EPRI)

Synthesizing accurate long-term wind gust timeseries using reanalysis and satellite data

Accurate estimate of return levels of 3-second wind gust for a range of return periods is of great importance to engineering decisions. Durable structures that are deployed over large areas in low roughness length terrain, such as solar farms, can be subject to especially strong and extensive wind gusts and suffer costly damages, so to reduce the possibility of loss of a substantial fraction of the expected value of a facility, it is important to design them to resist hazards that are expected to occur on the order of one time over the life of the project. However, high wind events sufficient to cause substantial damage, due for example to powerful thunderstorm downbursts, are rare at any given observing station. There is a need for synthetic timeseries calibrated to relatively short observational records derived from long reanalysis or satellite observation time series.

We trained an ML algorithm to predict the probability of wind gust exceeding a set of thresholds (20, 30, 40 m/s). The training predictors derived from the ERA5 reanalysis, including humidity and temperature aloft and various convective indices, as well as a set of indices calculated from satellite infrared brightness temperature observations that measured cloud top temperature and area of cloud top below a range of temperature thresholds. The wind gust training data was derived from ASOS station data and public mesonet data. We found that the total totals index and area-average cloud top temperatures are particularly helpful predictors of the likelihood of very high wind gusts associated with convective storms and not well predicted by the ERA5 wind gust parameter which is based only on boundary layer properties and severely underestimates wind gusts due to downbursts and other convection-linked phenomena.

Bio: Daniel Kirk-Davidoff is a principal technical leader in Electric Power Research Institute’s Energy Systems and Climate Analysis Group, where he works to improve understanding of climate change impacts on the electrical power system, and of the behavior of renewable generation and electrical demand under changing technological conditions. In prior work as a meteorological practitioner he developed operational forecasting systems for wind and solar generation and electrical system outages and led the development of a modernization plan for the Uganda National Meteorological Authority. In his academic work, he investigated the interactions between climate change and ozone depletion, the relationship between renewable generation and local and global climate, observation strategies for satellite climate monitoring, metrics for diagnosing climate sensitivity, and paleoclimate problems such as the Eocene polar warmth and the Messinian Desiccation. He is a member of the NOAA Science Advisory Board’s Environmental Information Service Working Group, and the Sustainability Commission of the City of Albany, NY.