Use of Mesoscale Ensemble Weather Predictions to Improve Short-Term

Precipitation and Hydrological ForecastsMichael Erickson1, Brian A. Colle1, Jeffrey Tongue2, Alan Cope3, and Joseph

Ostrowski4

1 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY2 National Weather Service, Upton, NY

3 National Weather Service, Mt. Holly, NJ4 Mid-Atlantic River Forecast Center, State College, PA

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.

Hydrologic Flowchart

SBU EnsembleSBU Ensemble

Upton, NY and Mount Holly Upton, NY and Mount Holly

WFO: Ingested into AWIPSWFO: Ingested into AWIPS

MARFC: MARFC:

Downscaling and Downscaling and

Basin AveragingBasin Averaging

6hr accumulated QPF 6hr accumulated QPF

ingested into Ensemble ingested into Ensemble

Streamflow PredictionStreamflow Prediction

Data ingested into Data ingested into

Site Specific for Site Specific for

the Passaic Basinthe Passaic Basin

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

Ensemble MM5 36- and 12-km Domains

Hanna Case – 9/6/08 00z Run Select WRF Members Select MM5 Members

Ensemble Mean Stage IV Data

Ensemble Streamflow Prediction: Hanna Case9/6/08 00z Run: Saddle River: Lodi, NY

QPF from Ensemble River Response from Ensemble

River Response: Mean and Spread Forecast and Observed River Height

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.

Model Error – Warm Season 2006 - 200818-42 Hr Acc. Precip

Mean Absolute Error

Average Member Ranking

Model Bias

MM

5W

RF

Spatial Bias Plots – Warm Season 2006 – 200818-42 Hr Acc. Precip

BMMY-ccm2.NEUS.avn GRMRF.NEUS.eta

Ensemble Mean Variance in Bias across Members

Member Bias Variability: Passaic, NYC and LI - Warm Season 2008

7 Day % of Obs. ME – 2008 Season WRF7 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.

Brier Score Plots – Warm Season 2006 – 200818-42 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”

Rank Histograms: Warm and Cool Seasons18-42 Hr Acc. Precip

Rank Histogram: Warm Season

Rank Histograms are consistently underdispersed and show a general wet bias.

Rank Histogram: Cool Season

Model Error – Cool Season 2007 - 200818-42 Hr Acc. Precip

Mean Absolute Error

Average Member Ranking

Model Bias

MM

5W

RF

Spatial Bias Plots – Cool Season 2007 – 200818-42 Hr Acc. Precip

GFS.MYJ.KFE.WSM3 GRMRF.NEUS.eta

Ensemble Mean Variance in Bias across Members

Member Bias Variability: Passaic, NYC and LI - Cool Season

7 Day % of Obs. ME – 2008 Season WRF7 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.

Brier Score Plots – Cool Season 2007 – 200818-42 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”

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.