1. Ensemble Forecasts Andy Wood CBRFC

2. Forecast Uncertainties Meteorological Inputs: Meteorological Inputs: Precipitation & temperature Precipitation & temperature Model errors: Model errors: Parametric, structural Parametric, structural Initial conditions of the model: Initial conditions of the model: Soil water, snow water equivalent Soil water, snow water equivalent Flow regulation: Flow regulation: Reservoirs, diversions & returns Reservoirs, diversions & returns

3. The Big Picture: Seamless probabilistic forecasts for all lead times

4. NOAA Ensemble Climate Forecasts Short Range Ens. Forecasts (SREF) Global Forecast System (GFS) and “frozen GFS” or “GFS reforecast” Coupled Forecast System (CFS) Many ensemble forecasts are available from different centers: NASA, NOAA, ECMWF, etc. RFCs are now using those produced by NOAA Nat. Cent. Env. Prediction (NCEP)

5. Why Ensemble Prediction? Uncertainty has an impact on the uses of hydrologic forecasts. Uncertainty has an impact on the uses of hydrologic forecasts. The goal of ensemble forecasting is to quantify the forecast uncertainty in an objective manner. The goal of ensemble forecasting is to quantify the forecast uncertainty in an objective manner. Ensemble products can be used to communicate this uncertainty to customers. Ensemble products can be used to communicate this uncertainty to customers.

6. Experimental Ensemble Forecast System (XEFS) XEFS is an “evolutionary system” XEFS is an “evolutionary system” Currently in the experimental implementation stage Currently in the experimental implementation stage Focused on blending the short-term and long-term ensemble forecasts. Focused on blending the short-term and long-term ensemble forecasts. OHD and RFCs working closely to bring research to operations and provide feedback. OHD and RFCs working closely to bring research to operations and provide feedback.

7. Ensemble Pre- Processor Parametric Uncertainty Processor Data Assimilator Ensemble Post- Processor Ensemble Product Generator Hydrologic & Hydraulic Models Forecasts Precipitation, Temperature, etc. Observed Precipitation, Soil Moisture, Snow Water Equivalent, etc. Streamflow Reduced systematic bias and accounts for uncertainty in future forcing Reduced systematic bias and accounts for uncertainty in hydrologic and hydraulic models Reduces and accounts for uncertainty in model initial conditions Basic XEFS Components Reduces and accounts for bias due to uncertainty in model parameters Verification System First Steps

8. 8 Short- Range Medium- Range Long- Range Merging, Joining & BlendingPre-processor Other Ensembles

9. CBRFC current ESP approach Basic XEFS Components NowDay 15+9 months+12 months ESP climatology QPF * *QPF added based on forecaster judgment This approach does not make use of climate forecasts. Users can download traces from ESP and apply their own climate-based weighting, however.

10. Frozen GFS NowDay 15+9 months+12 months CFSESP climatology CBRFC Tests getting underway 1.ESP only (~ current practice) 2.Frozen GFS + ESP 3.CFS + ESP 4.Frozen GFS + CFS + ESP CBRFC target approach Basic XEFS Components Q: Why use Frozen GFS versus latest GFS? A: Like CFS, Frozen GFS has a long set of reforecasts that tell us how much to trust it. The latest GFS doesn’t have a reforecast

11. 11 The reforecast provides a long track record (back to 1979) - used to assess model skill - used to calibrate model forecasts

12. 12 Reforecast-based example: floods causing La Conchita, California landslide, 12 Jan 2005 week-2 from reforecast 6-10 day from reforecast

13. Basic XEFS Components: CFS GODAS 3DVAR Ocean Model MOMv4 fully global 1/2 o x1/2 o (1/4 o in tropics) 40 levels Atmospheric Model GFS (2007) T382 64 levels Land ModelIce Model LDAS GDAS GSI 6hr 24h r 6hr Ice Ext 6hr

14. 14CFS The hindcasts give basis for deciding when/where to use the CFS forecasts.

15. CFS Example: May forecast for June 2009 -- which was wet.

16. From Ensembles to Actionable Products Future precipitation Future streamflow Future temperature Probabilistic inundation map

17. From Ensembles to Actionable Products Future streamflow, Nashville Flooding, May 1-3, 2010

18. From Ensembles to Actionable Products Long lead forecast products (water supply) won’t change much -- they’re already probabilistic -- but may get better. Short range products may begin to change, but this is farther down the road.