Tuesday, 8 January 2013: 9:15 AM
Room 6A (Austin Convention Center)
The success of any commercial energy weather activity begins with data. The universe of data includes historical data, near real time data and forecast data. For optimal energy decision applications, historical and near real-time data should be cleaned to remove missing and erroneous data. Often, it is also valuable for the decision maker if the historical data sets have discontinuities removed and trends adjusted. The universe of station data includes standard worldwide GTS data, as well as networks such as are contained in MADIS, GHCN. There is an increasing need within the energy industry for gridded data sets, such as reanalysis or geospatial data sets at every increasing resolution both spatially and temporally. Forecast data is also provided as either point data or gridded data. One of the primary challenges of forecast data is the recent trends in demand for probabilistic forecasts in both the gridded environment and the point specific environment. While this trend has brought with it more interest for multi-model ensembling and the utility that it can deliver, this trend also illustrates the constraints the data can impose on internal resources due to the magnitude in size of the data.
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