A convection permitting forecast ensemble for the mid-Atlantic states: applications for renewable energy
The Renaissance Computing Institute (RENCI) of the University of North Carolina Chapel Hill (UNC) has led a team of member institutions to construct a high-resolution ensemble of numerical weather prediction forecasts for a large portion (NC, SC, and GA) of the Southeastern US. The ensemble consists of 20-30 different model runs, most of them being the Weather Research and Forecasting (WRF) model, but also includes the Regional Atmospheric Modeling System (RAMS) and the Non-hydrostatic Mesoscale Model (NMM) as members. RENCI provides over half of the model runs via the extensive computing infrastructure available to manage the data and provide web access (www.sensordatabus.org/wrf). Team members include the National Weather Service (NWS) forecast offices in: Columbia, SC, Greenville-Spartanburg, SC, Raleigh, NC, Wilmington, NC, Newport, NC, Blacksburg, VA, and Sterling VA with additional support from the National Centers for Environmental Prediction (NCEP), the National Severe Storms Laboratory (NSSL), the Department of Energy (DOE) Savannah River National Laboratory (SRNL), and Purdue University. All of the models have unique domains, initializations, and physics packages, but all are converted to a common 4km resolution grid spanning the Mid-Atlantic States.
The initial goal in creating this model ensemble is to provide advanced and detailed weather forecast information to all participants. This includes operational weather support including improved precipitation forecasts and fire weather support (prescribed burns and wild land fires). In addition to the operational assets the ensemble provides, the models also provide detailed wind and incoming solar radiation/cloud estimates that can be applied to the renewable energy industry. For wind energy applications, more accurate wind forecasts, especially in coastal areas, can help support successful wind energy and energy infrastructure operations. For solar, improved cloud cover forecasts can greatly improve the management of grid operations for impacts to solar powered local and/or home power systems. In particular, grid operations can be maintained properly as incoming or developing predicted clouds attenuate the energy from the sun, thus decreasing power generation. Both direct beam and total irradiance can be evaluated with a number of forecasting tools and radiation data. Advanced warning of these impacts can be used to delineate alternative power resources and can expand the ability to generate power over a wide region rather than through large power plants alone. The sun and wind are largely untapped power resources in the Southeastern US, yet, the need for power companies to diversify their portfolios and remain environmentally in compliance remain “hard” drivers for the development of these resources.
Supplementary URL: www.renci.org