Thomas M. Hamill, Jeffrey S. Whitaker, and Gary T. Bates
For renewable-energy forecasts, many of the crucial decisions are currently made hours to 1 day in advance. This is partly because to fit the existing decision process, e.g., unit commitments.
In the future it may be possible to provide skillful renewable weather forecast decision support guidance at far longer leads, e.g., week +2 solar and wind-energy potential forecasts. These might be used to estimate demand and plan for maintenance, say, to take turbines offline if the wind-energy generation potential is forecast to be much below average.
Given the inaccuracies in numerical weather forecast guidance due to chaotic growth of initial conditions, model error, and sampling error, statistical post-processing is an essential component in preparing decision support at these longer lead times. Statistical post-processing is much easier and more accurate when there is a long period of training data using the same model that is run operationally.
In this talk we will describe such a reforecast data set. Every day from late 1984 to present, 11-member global reforecasts were computed using the current (2012) operational version of the Global Ensemble Forecast System (GEFS). Forecasts extend to 16 days lead. As with the operational model, the forecasts were computed at T254L42 (about ½ degree grid spacing) in week 1 and T190L42 (about ¾-degree) in week 2. Data was archived every 3 h to 72 h, and every 6 h thereafter. 99 fields are freely available for fast download from NOAA/ESRL/PSD. A full data archive is maintained at the Department of Energy, where the data set was created. Prior to 2012, the Climate Forecast System Reanalysis supplied the control initial condition. Perturbed initial conditions were generated with the operational ensemble transform with rescaling technique. The talk will describe the data set in more detail, the download procedures, and then describe some simple applications for renewable energy forecasts, such as the use of the reforecasts for the calibration and statistical downscaling of real-time forecasts of wind and solar energy potential. We will sketch out some more speculative and creative applications of this data set.