A21E-0185
Probabilistic Predictions and Downscaling with an Analog Ensemble for Weather, Renewable Energy, Air Quality, and Hurricane Intensity
Probabilistic Predictions and Downscaling with an Analog Ensemble for Weather, Renewable Energy, Air Quality, and Hurricane Intensity
Tuesday, 15 December 2015
Poster Hall (Moscone South)
Abstract:
The analog of a forecast for a given location and time is defined as the observation that corresponds to a past prediction matching selected features of the current forecast. The best analogs form the analog ensemble (AnEn). First AnEn skill is analyzed for predictions of 10-m wind speed and 2-m temperature. We show that AnEn produces accurate predictions and a reliable quantification of their uncertainty with similar or superior skill compared to cutting-edge methods, while requiring considerably less computational resources. A preliminary example of an application of AnEn in 3D will also be shown.Second, results for wind power predictions are presented, which confirm AnEn performance obtained for meteorological variables. Further improvements can be obtained by implementing analog-predictor weighting strategies, as will be shown.
Third, AnEn is implemented for downscaling the wind speed and precipitation fields from a reanalysis data set. AnEn significantly reduces the systematic and random errors in the downscaled estimates, and simultaneously improves correlation between the downscaled time series and the measurements, over what is provided by a reanalysis field alone. The AnEn also provides a reliable quantification of uncertainties in the estimate, thereby permitting decision makers to objectively define confidence intervals to the estimated long-term energy yield. We inckude also a discussion of the implementation of AnEn in data-sparse regions, where in that case it can be used as a technique to drastically reduce the computational cost of NWP-based dynamical downscaling.
We conclude we the latest novel inplementations of AnEn for air quality and hurricane intensity predictions.