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Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster 1

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Presentation on theme: "Judith Curry James Belanger Mark Jelinek Violeta Toma Peter Webster 1"— 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 1 1

2 Tropical Cyclones 1-15 Day Temperatures 10-32 Day Temperatures
Seasonal Outlook The 7th Annual Earth Networks Energy Weather Seminar:   Winter Outlook

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

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 4/12/2012

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 ECMWF Forecast - Climatology Hindcast Calibrated Forecast
Tropical Cyclones: Monthly Outlooks Input: ECMWF Monthly Forecast and Hindcasts Determine prob. bias-correction from model and obs. climate Bias-track adjustment for TCs forming in the eastern Atlantic 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 4/12/2012

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: 4/12/2012

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

13 Cluster Ensemble Mean Seasonal Forecast Clustering

14 TC Track Cluster: Ophelia (2011)
ECMWF Ensembles and HRES Cluster VarEPS Cluster Mean VarEPS Cluster Observations VarEPS Deterministic Mean VarEPS 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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