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A seamless system for probabilistic forecasts of energy demand: days to seasons

A seamless system for probabilistic forecasts of energy demand: days to seasons. Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster. Tropical Cyclones. 1-15 Day Temperatures. Seasonal Outlook. 10-32 Day Temperatures. ECMWF Integrated Forecasting System

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A seamless system for probabilistic forecasts of energy demand: days to seasons

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  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

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