Daniel Wilks earned his B.S. and M.S. in Soil Science from the University of California at Berkeley and his Ph.D. in Atmospheric Science from Oregon State University. He is a fellow of the American Meteorological Society and a member of the Royal Meteorological Society and American Association for the Advancement of Science.
Wilks' work involves application of statistical methods to quantifying and dealing with uncertainty in meteorological and climatological data and forecasts in a variety of contexts.
Dr. Wilks' research is focused on forecast evaluation, ensemble forecasting, or both. Other areas are the use and economic value of forecasts in formal decision-making models, "weather generators" (time-domain time series models for weather data), interpretation and use of long lead ("climate") forecasts, and studies of climate-change impacts.
Wilks teaches courses in statistical methods in meteorology and climatology and microclimatology. He authored the textbook, Statistical Methods in the Atmospheric Sciences.
- Wilks, Daniel. 2017. "On assessing calibration of multivariate ensemble forecasts.." Quarterly Journal of the Royal Meteorological Society 143 (702): 164-172.
- Wilks, Daniel. 2016. "The stippling shows statistically significant grid points": how research results are routinely overstated and over-interpreted, and what to do about it.." Bulletin of the American Meteorological Society 97: 2263-2273.
- Wilks, Daniel. 2016. "Three new diagnostic verification diagrams." 23 (3): 371-378.
- Wilks, Daniel. 2016. "Modified "Rule-N" procedure for principal component (EOF) truncation." Journal of Climate 29: 3049-3056.
- Wilks, Daniel. 2015. "Multivariate ensemble Model Output Statistics using empirical copulas." Quarterly Journal of the Royal Meteorological Society 141 (688): 945-952.
Oregon State University 1986