Turning interval meter data into analytics to improve program performance for demand-side management.
Redefining Normal Temperatures
Resource planning and forecasting in a changing climate.
where they don’t properly account for the resource risks they face. Thus, failing to account for the trends in climate can have large and material implications.
Beyond temperature and precipitation—for example trends in high-impact storms including wind, snow, ice, and other short-term but highly damaging weather events—there’s little specific to recommend. At this time there’s no expert consensus or convincing evidence that the risks of these hazards are generally increasing. Arguments for their increase to date are largely anecdotal or supported by inadequate models. Continued climate warming will certainly lead to shifts in weather patterns, but there is a large uncertainty as to what these shifts will be and where. In the case of extreme weather, the best strategy for utilities is to continue to objectively monitor the sequence of year-to-year weather and the developing peer-reviewed science.
Adapting to the current trends in climate requires accounting for weather in a way that’s consistent with the data. For utilities, one aspect of this is to properly account for what normal weather will be going forward. Two alternatives—OCNs and hinge-fits—both have a basis in climate science and empirical verification, and thus provide better estimates of normal weather than traditional 30-year averages do. The risks of improper accounting for changes in normal weather are real, and ignoring them is potentially costly.
2. See Wilks, D. S., and R. E. Livezey. “Performance of Alternative ’Normals‘ for Tracking Climate Changes, Using Homogenized and Non-homogenized Seasonal U.S. Surface Temperatures,” Journal of Applied Meteorology and Climatology , in press.
3. See Livezey, R.E., K.Y. Vinnikov, M.M. Timofeyeva, R. Tinker, and H.M. van den Dool. “Estimation and extrapolation of climate normals and climatic trends,” Journal of Applied Meteorology and Climatology , 46 (2007), 1759-1776.
5. See Menne, M.J., and C.N. Williams. “Homogenization of temperature series via pairwise comparisons,” Journal of Climate , 22 (2009), 1700-1717. The website has a less-technical description of the process.
8. See Wilks and Livezey, op. cit. , Wilks, op. cit. , and Huang, J., H.M. van den Dool, and A.G. Barnston. “Long-lead seasonal temperature prediction using optimal climate normals,” Journal of Climate , 9 (1996), 809-817.
11. See Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller, Eds., Climate Change 2007: The Physical Science Basis . Cambridge, UK: Cambridge University Press, 2007.