1. REGIONAL-SCALE ENSEMBLE FORECASTS OF THE 7 FEBRUARY 2007 LAKE EFFECT SNOW EVENT Justin Arnott and Michael Evans NOAA/NWS Binghamton, NY Richard Grumm NOAA/NWS State College, PA George Young Penn State University, University Park, PA NROW IX, 7-8 November 2007

2. Motivation Past LES Forecasting LES a Pattern Recognition Problem GFS unable to resolve bands GFS unable to resolve bands Rely on tools such as BUFKIT Rely on tools such as BUFKIT

3. Motivation, continued 12 km NAM grossly resolves lake-parallel bands 12 km NAM grossly resolves lake-parallel bands –Each NWS office can run a local version of this model Individual runs often have problems with band location/orientation Individual runs often have problems with band location/orientation Can multiple simulations of the NAM (an ensemble) provide added value? Can multiple simulations of the NAM (an ensemble) provide added value? –This question has prompted the development of the Northeast Regional Ensemble Present/Future LES Forecasting

4. What is the Northeast Regional Ensemble? 12 km Workstation WRF 12 km Workstation WRF –24-36 hr run length 2007-2008: 7-8 Members 2007-2008: 7-8 Members –2 CTP members –1 Operational Goal: Improve operational forecasts of lake effect snowfall Goal: Improve operational forecasts of lake effect snowfall

5. Case Day: 07FEB2007 Part of a ~10-day prolific lake effect snow event east of Lake Ontario Part of a ~10-day prolific lake effect snow event east of Lake Ontario Band moved significantly throughout the day Band moved significantly throughout the day –Excellent test for the ensemble

6. The Ensemble – 07FEB2007 OfficeCoreIC/BCsMicroCPS #Z Lev OperationalNMMNAMFerrierBMJ60 BGMARWNAMLinKF31 CLEARWGFSLinKF40 CTP-1NMMNAMLinBMJ31 CTP-2ARWNAMLinBMJ31 BTVNMMGFSFerrierBMJ31

7. 07FEB2007 – Synoptic Setup

8. T LAKE : +4C

9. 07FEB2007 – Synoptic Setup

10. Radar Loop

11. Operational NAM Performance

12. Captures basic band evolution Captures basic band evolution –Slow with initial southward band movement Problems with inland extent of the band Problems with inland extent of the band –Frequently too far inland Can the ensemble add value to this simulation? Can the ensemble add value to this simulation?

13. Ensemble Performance

14. All members able to simulate a band All members able to simulate a band Like NAM, ensemble successfully captures basic band evolution Like NAM, ensemble successfully captures basic band evolution Probability plots indicate operational NAM an outlier with inland extent Probability plots indicate operational NAM an outlier with inland extent –Ensemble provides added value

15. Individual Member Performance Quantitatively assess each ensemble member Quantitatively assess each ensemble member –Method: MODE pattern matching software (Davis et al. 2006) Identify precipitation “objects” in forecast/observations Identify precipitation “objects” in forecast/observations Match objects based on different attributes Match objects based on different attributes –Distance apart, similarity in area/orientation, overlap Precipitation Obs: NCEP Stage IV Analysis Precipitation Obs: NCEP Stage IV Analysis

16. Individual Member Performance Example: Example:

17. Individual Member Performance The Statistics: Primary Band Identification The Statistics: Primary Band Identification –POD/FAR/CSI MODELPODFARCSI NAM-NMM*0.900.000.90 BGM-ARW0.850.080.79 BTV-NMM0.310.230.25 CLE-ARW0.480.080.43 CTP-ARW10.040.96 CTP-NMM0.810.270.64 * 3 hourly time steps

18. Individual Member Performance The Statistics: Basic Position/Intensity The Statistics: Basic Position/Intensity Average Bias (fcst-obs) MODELArea(gdpts)Angle(degs) Area-Avgd Intensity (mm/grdpt) NAM-NMM*96.3-2.70.10 BGM-ARW23.8-0.40.05 BTV-NMM2.8-5.3-0.04 CLE-ARW-33.2-1.10.10 CTP-ARW68.21.10.04 CTP-NMM9.8-0.5-0.01 * 3 hourly time steps

19. Conclusions Case study suggests ensemble approach to LES may be valuable Case study suggests ensemble approach to LES may be valuable –Hone in on high-probability impact areas –Highlight outlier (low-probability) outcomes Initial Quantitative Analysis shows diversity in “best member” for different variables Initial Quantitative Analysis shows diversity in “best member” for different variables –Ensemble mean likely to have increased skill over individual members

20. Contact Info/Acknowledgements Have Questions? Have Questions? –justin.arnott@noaa.gov justin.arnott@noaa.gov Acknowledgements: Ensemble Participants Ensemble Participants –For agreeing on a common domain/sharing data Ron Murphy, ITO BGM Ron Murphy, ITO BGM –For gathering 7 February 2007 case data MODE Software designers MODE Software designers –http://www.dtcenter.org/met/users/