Use of Mesoscale Ensemble Weather Predictions to Improve Short-Term

Precipitation and Hydrological ForecastsMichael Erickson1, Brian A. Colle1, Jeffrey Tongue2, Nancy Furbush2, 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

High Ensemble Variability: Hanna 9/6/08 00Z Run 18-42 Hour Acc. Precip.

Select Stony Brook Members Select Short Range Ensemble Forecast MembersGFS Solution “Observed”

Project Goals- To improve short-term (0-48h) hydrological forecasts using a combination of

ensemble quantitative precipitation forecasts (QPF) and streamflow models used by

the National Weather Service (NWS).

- Collaborative project between Stony Brook University (SBU), NWS offices at Upton,

NY and Mt. Holly, and the Mid-Atlantic River Forecast Center (MARFC).

- Run hydrologic models fed with SBU QPF data over various river basins such as the

Passaic.

- Correct ensemble problems based on

how the models performed in the recent

past.

- Check to see how the ensemble

correction performed for specific

weather cases.

Passaic Basin

Lodi, NJ

SBU MM5 and WRF Ensemble

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

NCEP SREF 21 Member Ensemble

Ensemble run 4 times per day (03z, 09z, 15z, 21z) but only the 21z run is considered for this study.

The SREF consists of the following:

- 10 ETA members at 32 km grid spacing.

- 5 with BMJ PBL and Ferrier MP.

- 5 with KF PBL and Ferrier MP.

- 5 RSM members at 45 km grid spacing.

- 3 with SAS PBL and Zhou GFS MP.

- 2 with RAS PBL and Zhou GFS MP.

- 3 WRF-NMM members at 40 km grid spacing.

- 3 WRF-ARW members at 45 km grid spacing.

Since the initial atmospheric state can never be perfectly known, SREF uses a breeding technique to account for uncertainty in initial conditions.

Stage IV and model details

NCEP Stage IV rain data at ~4 km resolution is available from 2001-present in

1-hr, 6-hr, and 1 day increments.

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 and SREF data were

interpolated to the 12 km MM5/WRF

model grid.

Regions sufficiently offshore were masked.

The 2006 to 2008 warm seasons

(5/1-8/31) were analysed.

Total Stage IV Warm Season Precip.

Hydrological Multi-Model Ensemble Forecasts

SBU, NCEP SREF, SBU, NCEP SREF,

NAM, and GFSNAM, and GFS

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

Hanna Case – 9/6/08 00z (21z) Run Select SREF Members Select SBU Members

SBU Ensemble Mean Stage IV DataSREF Ensemble Mean

KDXR ETA SREF Plume 9/5/08 21Z

Hanna Case – 9/6/08 00z (21z) Run

KDXR SBU MM5 Plume 9/6/08 00Z

KDXR SBU WRF Plume 9/6/08 00Z KDXR Non-ETA SREF Plume 9/5/08 21Z

Hanna Case: Ensemble Streamflow Prediction9/6/08 00z Run: Saddle River: Lodi, NJ

QPF from Ensemble River Response from Ensemble

River Response: Mean and Spread Forecast and Observed River Height

4”

3”

2”

1”

0”

-33% of members predict major flooding

-42% of members predict moderate flooding

-58% of members predict flooding

12’

10’

8’

6’

4’

2’

12’

10’

8’

6’

4’

10’

8’

6’

4’

2’

0’

Observed Flood Stage

~7.8’

Ensemble Mean Flood Stage ~6.8’

April 2007 Case – 4/15/07 00z Run Select SREF Members Select SBU Members

SREF Ensemble Mean Stage IV Data SBU Ensemble Mean

April 2007 Case – 4/15/07 00z RunKDXR ETA SREF Plume 4/14/07 21ZKDXR SBU MM5 Plume 4/15/07 00Z

KDXR SBU WRF Plume 4/15/07 00Z KDXR Non-ETA SREF Plume 4/14/07 21Z

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

SREF Model Bias

SBU Ensemble RankingSBU Model Bias

MM

5W

RF

SREF Ensemble Ranking

SBU has a wet bias,

but this is caused

mostly by light QPF

daysSome members

outperform others for

both ensembles

1.61.41.2

10.80.60.40.2

0

2

1.5

1

0.5

0

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

SREF Model Bias for Days > 1”

SBU Model Bias for Days > 1”

MM

5W

RF

1.61.41.2

10.80.60.40.2

0

SBU wet bias appears to be

caused by a large number of light

QPF eventsDry bias increases for both

ensembles on heavier QPF days.

1.61.41.2

10.80.60.40.2

0

Warm Season – Grid Averaged Statistics Avg Diurnal Cycle of Precip - SBU

0.030”

0.025”

0.020”

0.015”

0.010”

0.005”

0”

0.035”

0.030”

0.025”

0.020”

0.015”

0.010”

0.005”

0”

0.035”

0.030”

0.025”

0.020”

0.015”

0.010”

0.005”

0”

AM

Convective Peak Overestimated

AM AM AMPMPM

PM

PM

AM

Diurnal Cycle of Precip - SREF ETA Diurnal Cycle of Precip - SREF RSM/WRF

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

BMMY-ccm2.NEUS.avn sref.arw.n1

SBU Ensemble Mean SREF Ensemble Mean

Rank Histograms18-42 Hr Acc. Precip

SBU Rank Histogram

Ensembles frequently suffer from dispersion problems, meaning that model variability

is too great or small compared to reality.Our Stony Brook Ensemble does not have enough variability and has a wet bias.

SREF has the opposite problem, too much variability.

SREF Rank Histogram

Post-Processing - Bayesian Model Averaging (BMA)

Bayesian Model Averaging (BMA Raftery et al. 2005) has been shown to correct some model

deficiencies associated with reliability and dispersion.BMA creates a probability density function (PDF) for each ensemble member depending on the

uncertainty in the model forecast and weights the result based on its performance and uniqueness in

the recent past. The main advantages of BMA appear to be with probabilistic skill, although deterministic skill is also

increased.

PDF for Temperature PDF for Precipitation

BMA weights each member based on past performance and assigns a range of potential values

BMA Results - Warm Season Weights at KJFK18 -42 Hour Acc. Precip.

BMA Weights for Precip - MM5

BMA Weights for Precip - WRF

100%

80%

60%

40%

20%

0%

100%

80%

60%

40%

20%

0%

Raw Rank Histogram

BMA Adjusted

•BMA has been shown to improve ensemble

reliability and correct the rank histogram for

precipitation in the Pacific Northwest

(Sloughter et al. 2007).

Raw EnsembleBMA Rank HistogramBMA Rank Histogram

Raw Rank HistogramRaw Rank Histogram

BMA – Future Work

Reliability DiagramReliability Diagram

1 2 3 4 5 6 7 8 9 10

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0.0 0.2 0.4 0.6 0.8 1.0

1

0.8

0.6

0.4

0.2

0

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0.00

Conclusions

The SBU ensemble has a general wet bias caused by overprediction of mainly

light to moderate QPF events during the late afternoon.

The SREF ensemble has a dry bias and some members fail to properly simulate

the diurnal convective cycle.

Soil moisture and PBL scheme may influence the model bias for the SBU

ensemble. This will have to be looked into further.

The SBU ensemble suffers from a lack of variability, while the variability within the

SREF ensemble is overinflated compared to the observations.

Bayesian Model Averaging may help improve the model output and provide

probabilistic value for precipitation.

SBU and SREF ensemble data are currently being supplied to NWS WFO’s and

the MARFC for hydrological predictions.

MM

5W

RF

Positive Definite Moisture Transport

SBU Ensemble and a Positive Definite Moisture Transport Model