1. Use of Mesoscale Ensemble Weather Predictions to Improve Short-Term Precipitation and Hydrological Forecasts Michael Erickson 1, Brian A. Colle 1, Jeffrey Tongue 2, Alan Cope 3, and Joseph Ostrowski 4 1 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 2 National Weather Service, Upton, NY 3 National Weather Service, Mt. Holly, NJ 4 Mid-Atlantic River Forecast Center, State College, PA

2. Motivations and Goals - Determine whether the probability of river flood forecasts can be improved using a multi-model ensemble (NCEP SREF, Stony Brook ensemble, GFS, and NAM). - Since ensembles will be run using convective parametrizations for several years (dx > 4-km grid spacing), it is important to understand the precipitation errors fed into the hydrological models. - Using the SBU ensemble, verify QPF using a high resolution precipitation dataset (stage IV) to determine whether model performance varies spatially and temporally. - Determine if certain members outperform others in order to allow for unequal weighting in the ensemble streamflow forecasts.

3. Hydrologic Flowchart SBU Ensemble Upton, NY and Mount Holly WFO: Ingested into AWIPS MARFC: Downscaling and Basin Averaging 6hr accumulated QPF ingested into Ensemble Streamflow Prediction Data ingested into Site Specific for the Passaic Basin

4. 0000 UTC 13-Member MM5/WRF Ensemble 7 MM5 Members: - **WRF-NMM (Grell, MRF, Sice)‏ - WRF-NMM (Grell, M-Y, Reis2)‏ - GFS (Betts-Miller, M-Y, Sice)‏ - GFS (KF2, MRF, Reis2)‏ - NOGAPS (Grell, Blackadar, Sice)‏ - CMC (KF2, M-T, Sice)‏ - 18 Z GFS + FDDA (Grell, Blackadar, Sice)‏ 6 WRF-ARW Members: - **WRF-NMM (KF2, YSU, Ferrier)‏ - WRF-NMM (Betts-Miller, M-Y, WSM3)‏ - GFS (Grell, YSU, Ferrier)‏ - GFS (KF2, M-Y, Ferrier)‏ - NOGAPS (Betts-Miller, YSU, WSM3)‏ - CMC (KF2, M-Y, WSM3)‏ All runs integrated down to 12-km grid spacing to hour 48** MM5/WRF members are run down to 4-km grid spacing

5. Ensemble MM5 36- and 12-km Domains

6. Hanna Case – 9/6/08 00z Run Select WRF Members Select MM5 Members Ensemble MeanStage IV Data

7. Ensemble Streamflow Prediction: Hanna Case 9/6/08 00z Run: Saddle River: Lodi, NY QPF from Ensemble River Response from Ensemble River Response: Mean and SpreadForecast and Observed River Height

8. Stage IV and model details Stage IV data consists of radar estimates and rain gauge data that were blended with some additional manual quality control. Accumulated rainfall between hours 18 and 42 of the model run were considered. The stage IV rain data was interpolated to the 12 km MM5/WRF model grid. Regions sufficiently offshore were masked. The 2007 and 2008 cold seasons (12/1- 3/31) and 2006 to 2008 warm seasons (5/1- 8/31) were analysed. Total Stage IV Warm Season Precip.

9. Model Error – Warm Season 2006 - 2008 18-42 Hr Acc. Precip Mean Absolute Error Average Member Ranking Model Bias MM5 WRF

10. Spatial Bias Plots – Warm Season 2006 – 2008 18-42 Hr Acc. Precip BMMY-ccm2.NEUS.avn GRMRF.NEUS.eta Ensemble MeanVariance in Bias across Members

11. Member Bias Variability: Passaic, NYC and LI - Warm Season 2008 7 Day % of Obs. ME – 2008 Season WRF 7 Day % of Obs. ME – 2008 Season MM5 Biases vary greatly in time and are negatively correlated (-0.35 to -0.50) to Stage IV rain data for the MM5 models.

12. Brier Score Plots – Warm Season 2006 – 2008 18-42 Hr Acc. Precip Brier Score: Threshold > 0.1”Brier Score: Threshold > 0.5” Ensemble Mean Bias: Threshold > 0.1” Ensemble Mean Bias: Threshold > 0.5”

13. Rank Histograms: Warm and Cool Seasons 18-42 Hr Acc. Precip Rank Histogram: Warm Season Rank Histograms are consistently underdispersed and show a general wet bias. Rank Histogram: Cool Season

14. Model Error – Cool Season 2007 - 2008 18-42 Hr Acc. Precip Mean Absolute Error Average Member Ranking Model Bias MM5 WRF

15. Spatial Bias Plots – Cool Season 2007 – 2008 18-42 Hr Acc. Precip GFS.MYJ.KFE.WSM3GRMRF.NEUS.eta Ensemble Mean Variance in Bias across Members

16. Member Bias Variability: Passaic, NYC and LI - Cool Season 7 Day % of Obs. ME – 2008 Season WRF 7 Day % of Obs. ME – 2008 Season MM5 Biases not as sensitive to low stage IV rain days, although there is still a slight negative correlation.

17. Brier Score Plots – Cool Season 2007 – 2008 18-42 Hr Acc. Precip Brier Score: Threshold > 0.1”Brier Score: Threshold > 0.5” Ensemble Mean Bias: Threshold > 0.1” Ensemble Mean Bias: Threshold > 0.5”

18. Conclusions Most ensemble members tend to have a overprediction bias for precipitation during both warm and cool seasons. The overprediction variability among members is largest during the warm season and areas that experience more convection (MD and DE area, major valleys, etc...). This suggests large sensitivities to the convective parametrization (and other physics). Overprediction is not as large for many members during the cool season, but the raw ensemble is still positively biased and underdispersed. Some ensemble members perform better than others, with WRF members better than MM5 during the cool season. SBU Ensemble data is now being used by the Ensemble Streamflow Prediction (ESP) system at MARFC and Site Specific at Upton/Mt. Holly. Ensemble will have to be bias corrected and weighted given the errors noted above.