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Uncertainty Quantification and Probabilistic Forecasting

Accurate Point-Based Predictions

National security and energy agencies, private businesses such as wind turbine manufacturers and other related organizations need timely, accurate weather and air quality predictions, but also a quantification of their uncertainty. For national security and air quality, reliable uncertainty quantification is central to cost-effective decision-making, for adopting efficient strategies on the ground to protect the public health, and to mitigate the harmful effects of contaminants released either accidentally or deliberately in the atmosphere.

NCAR’s National Security Applications Program (NSAP) has extensive experience with advanced ensemble approaches to generate three-dimensional probabilistic predictions, which include systems based on multi-physics and/or multi-model and/or multi-boundary conditions approaches.

A recent advance in this focus area is the ability to generate accurate point-based predictions and reliable uncertainty quantification at a fraction of the computational cost of traditional ensemble methods, called the Analog Ensemble (AnEn), which has been successfully applied for a range of applications. For example, in a test performed for 0-72 hour predictions of wind power at a wind farm in Italy, the AnEn outperformed a power prediction based on the European Center for Medium range Weather Forecasting (ECMWF) ensemble wind predictions, a worldwide leader in operational forecasting. And, the AnEn computational cost was about one fourth of what was required to generate the ECMWF ensemble.

AnEn has been applied successfully also for the following applications:

  • Solar wind power predictions;
  • Probabilistic weather predictions over a 2/3D grid;
  • Cyclones intensity predictions;
  • Air quality predictions of ground-level ozone and surface PM;
  • Calibration of ensemble forecasts;
  • Wind resource assessment; and
  • Downscaling of reanalysis and coarse resolution model estimates. 

This critical work is sponsored by the following agencies:

  • Army Test and Evaluation Command (ATEC)
  • Defense Threat Reduction Agency (DTRA)
  • Department of Energy (DOE)
  • National Aeronautics and Space Administration (NASA)
  • National Oceanic and Atmospheric Administration (NOAA)
  • National Renewable Energy Laboratory (NREL)
  • Vattenfall
  • Vestas Wind Systems
  • Xcel Energy

Search through all publications in NCAR's OpenSky Library.

  1. Delle Monache, L., T. Nipen, Y. Liu, G. Roux, and R. Stull, 2011: Kalman filter and analog schemes to postprocess numerical weather predictions. Monthly Weather Review, 139, 3554–3570
  2. Delle Monache, L., T. Eckel, D. Rife, and B. Nagarajan, 2013: Probabilistic weather prediction with an analog ensemble. Monthly Weather Review, 141, 3498–3516
  3. Mahoney, W.P., K. Parks, G. Wiener, Y. Liu, W.L. Myers, J. Sun, L. Delle Monache, T. Hopson, D. Johnson, S.E. Haupt, 2012: A wind power forecasting system to optimize grid integration. IEEE Trans. Sustainable Energy, 3, 670–682
  4. Alessandrini, S., Delle Monache, L., Sperati, S., and Nissen, J, 2015. A novel application of an analog ensemble for short-term wind power forecasting. Renewable Energy, 76, 768-781
  5. Vanvyve, E., Delle Monache, L., Rife, D., Monaghan, A., Pinto, J., 2015. Wind resource estimates with an analog ensemble approach. Renewable Energy, 74, 761-773
  6. Nagarajan, B., Delle Monache, L., Hacker, J., Rife, D., Searight, K., Knievel, J., and Nipen, T., 2015. An evaluation of analog-based post-processing methods across several variables and forecast models. Weather and Forecasting, 30, 1623–1643
  7. Djalalova, I., Delle Monache, L., and Wilczak, J., 2015. PM2.5 analog forecast and Kalman filtering post-processing for the Community Multiscale Air Quality (CMAQ) model. Atmospheric Environment, 119, 431–442
  8. Junk, C., Delle Monache, L., Alessandrini, S., von Bremen, L., and Cervone, G., 2015. Predictor-weighting strategies for probabilistic wind power forecasting with an analog ensemble. Meteorologische Zeitschrift, 24, 361-379
  9. Alessandrini, S., Delle Monache, L., Sperati, S., and Cervone, G., 2015. Solar forecasting with an analog ensemble. An analog ensemble for short-term probabilistic solar power forecast. Applied Energy, 157, 95–110
  10. Eckel, T., and Delle Monache, L., 2015. A hybrid, analog-NWP ensemble. Monthly Weather Review, 144, 897–911
  11. Zhang, J., Draxl, C., Hopson, T., Delle Monache, L., and Hodge, B.-M., 2015. Comparison of deterministic and probabilistic wind resource assessment methods on numerical weather prediction. Applied Energy, 156, 528–541

Uncertainty Quantification and Probabilistic Forecasting