1 / 15

Integrated Forecasting System for Probabilistic Energy Demand Forecasts

A seamless system for probabilistic energy demand forecasts ranging from days to seasons, utilizing statistical post-processing and ensemble clustering strategies for enhanced predictive accuracy. The system enables consistent and hierarchical predictability and forecast confidence assessment. Postprocessed forecasts compete with multi-model ensembles, particularly effective for extreme event predictions.

sgill
Download Presentation

Integrated Forecasting System for Probabilistic Energy Demand Forecasts

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A seamless system for probabilistic forecasts of energy demand: days to seasons Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster

  2. Tropical Cyclones 1-15 Day Temperatures Seasonal Outlook 10-32 Day Temperatures

  3. ECMWF Integrated Forecasting System (IFS) Long-Range Month 0-13 51 ensemble mem 80 km Medium-Range Day 0-10 51 ensemble mem 32 km Extended-Range Day 10-32 51 ensemble mem 64 km High Resolution Day 0-10 1 member 16 km

  4. Statistical Post-Processing • Basis: • Reforecasts/hindcasts • Recent model performance • Statistical methods: • Bayesian bias correction • Quantile-to-Quantile distribution calibration • Model Output Statistics (MOS) IFS allows for consistent and integrated statistical post processing

  5. Comparison of two different post processing schemes for 1-15 day U.S. temperature forecasts (6/12 – 9/12) oF 7 6 5 4 3 2 ECMWF raw ens mean Method 1 (operational) Method 2 (test) RMSE 7 6 5 4 3 2 RMSE 1 2 3 4 5 6 7 8 9 10 11 12 13 14

  6. U.S. Daily Temperature Forecasts Input: ECMWF Variable Ensemble Prediction System Q-to-Q Mapping Developed from Hindcast Products Variable Averaging Bias Correction Using Recent Forecast Skill Output: Deterministic & Probabilistic Daily Max & Min Temp Deterministic: Daily Max/Min Temperature Forecasts for 105 U.S. Cities Based on Energy Trading Regions Probabilistic: Daily Max/Min Temperature Interpercentile Plumes for Each City

  7. Ensemble distribution of seasonal forecasts

  8. Hurricane Sandy Forecast: 10/23 12Z (Oct 30 landfall) ECMWF raw tracks ECMWF bias corrected tracks Bias corrected tracks gave 2 days advantage for landfall forecast

  9. Tropical Cyclones: Monthly Outlooks Input: ECMWF Monthly Forecast and Hindcasts Bias-track adjustment for TCs forming in the eastern Atlantic Determine prob. bias-correction from model and obs. climate Output: Bias-corrected track density probabilities and anomalies ECMWF Forecast - Climatology Hindcast Calibrated Forecast Observed Tropical Cyclones in Black • Contours show bias-calibrated probability of a tropical cyclone for specified forecast period and shading denotes anomaly relative to climate • Forecast confidence assigned based on phase and amplitude of the Madden-Julian Oscillation

  10. U.S. Monthly Temperature Forecasts Input: ECMWF Monthly Forecast and Hindcasts Theoretical Extreme Value Distribution from Hindcast Products Output: Probabilistic Extreme Temperature and Heat/Cold Wave Forecast Heat/Cold Wave Probability: Weekly Departures from Normal and Probability of Exceedances Output: Regional Temperature Outlook w/Forecast Confidence Regional & Averaged Outlooks:

  11. Objective Forecast Confidence Assessment • Historical predictability analyses • Recent prediction verification statistics • Phase and amplitude of the MJO and ENSO • Spread of the forecast ensemble members and intercorrelation of ensemble members • Relationship between ensemble spread and forecast error conditioned on teleconnection regimes.

  12. Ensemble Clustering: Grouping Members of Forecast Ensembles • Clustering strategies: • Self clustering • Regimes • Initial verification • HRES forecast • Subsequent shorter term forecasts

  13. Ensemble Mean Cluster Seasonal Forecast Clustering

  14. VarEPS Cluster Mean VarEPS Cluster Observations TC Track Cluster: Ophelia (2011) ECMWF Ensembles and HRES Cluster VarEPS Deterministic Mean VarEPS Observations • Cluster: Top five ensemble members whose correlation coefficient with the ECMWF HRES track is largest during the first 72 hr • Working Hypothesis: When the ensemble spread is large, the cluster is more likely to align closer to observations than either the HRES or ensemble mean

  15. Conclusions • ECMWF Integrated Forecast System enables: • Internally consistent postprocessing across time scales • Internally consistent and hierarchical predictability and forecast confidence assessment • Hierarchical ensemble clustering strategies • Postprocessed IFS forecasts are competitive with multi-model • Ensembles (better for extreme events) • Addressing distributional errors is essential for • extreme event forecasts • There is untapped prediction skill in ensemble • Interpretation through clustering

More Related