Validation of Storm Surge Models for the New York Bight and Long Island Regions and the Impact of Ensembles

Tom Di Liberto

Dr. Brian A. Colle

Stony Brook University

Motivation• How well can a surge (ocean) model do

for landfalling hurricanes over the Northeast U.S.?

• What is the skill of the Stony Brook Storm Surge system over the cool season? How does it compare with other models (NOAA and Stevens Institute)?

• What are the strengths and current limitations of ensemble surge modeling?

MM5/WRF Modeling of Gloria /ADCIRC Modeling of Storm Surge

• 108 K nodes ( 70km to 5 m)• Ensemble uses 5 MM5 / 3

WRF members

ADCIRCMM5 / WRF

• NCAR-AFWA Bogus Method• YSU PBL, GFS PBL, MY PBL runs

– NARR initial condition

36 km12 km

4 km

NARR Initial Conditions

Lin Microphysics

YSU PBL

Landfall occurs ~ 1 h delayed ~30 miles east of observed landfall

Gloria Tracks

PBL

PBL

PBL

NCAR-NCEP Global Reanalysis IC

Hurricane Gloria Wind Verification at Ambrose Lighthouse

0

10

20

30

40

50

60

70

26 th - 0

4 8

12

16

20

27 th - 0

4 8

12

16

20

28 th - 0

4 8

12

YSU 30m Wind

GFS 30m Wind

Observed

Hurricane Gloria Wind Verification at JFK

0

5

10

15

20

25

30

35

40

45

26 th - 0

4 8 12

16

20

27 th - 0

4 8 12

16

20

28 th - 0

4 8 12

YSU 10 m Winds

YSU 30 m Winds

GFS 30 m Winds

Observed

Win

d S

peed

kts

Win

d S

peed

kts

CREATE ANIMATION OF YSU 10m 1.0xYSU PBL

Model Landfall

Observed Landfall

1 h shift of track increases peak water level by ~.20m

Using 30m wind increases peak water level by ~.30m

Using different PBL (track) increases peak water level by ~.40m

*

What if GFS PBL scheme 1 h shifted?

Wave and Surface Stress Impact

SWAN wave model used to calculate wave radiation stress

Wave model takes winds from atmospheric runs to generate waves

• 5 MM5 / 3 WRF members

• MM5/WRF run at 12 km

resolution, once a day at 00z

and ADCIRC runs out 48 h.

• stormy.msrc.sunysb.edu

Real-time Modeling Systems Compared• Stevens Institute

– Atmospheric Forcing – 12-km NAM– Ocean Model – POMS– http://hudson.dl.stevens-tech.edu/maritimeforecast/

• NOAA ET Surge– Atmospheric Forcing – GFS

– http://www.weather.gov/mdl/etsurge/

• Stony Brook Storm Surge Model– Atmospheric Forcing – MM5/WRF– Ocean Model - ADCIRC Ocean Model– http://stormy.msrc.sunysb.edu/

Real Time Ensemble• 36 days during Nov. 2007 – March 2008 with Full

Ensemble– Nov – 9 days– Dec – 12 days– Jan – 15 days

Stony Brook Storm Surge Model Atmospheric Ensemble MembersMembers Model Microphysics PBL Scheme Radiation Initial Condition Cumulus Member #1 MM5 Simple Ice MRF Cloud Radiation WRF-NMM GrellMember #2 MM5 Simple Ice MY CCM2 GFS Betts Miller Member #3 MM5 Simple Ice Blackadar CCM2 NOGAPS GrellMember #4 MM5 Reisner MRF Cloud Radiation GFS Kain FritschMember #5 MM5 Simple Ice MY CCM2 Canadian Model Kain FritschMember #6 WRF Ferrier YSU RRTM WRF-NMM Kain FritschMember #7 WRF Ferrier YSU RRTM GFS model GrellMember #8 WRF WSM3 YSU RRTM NOGAPS Betts Miller

Wave Impacts during Cool Season

• SBSS model member 9a

• Average daily errors from Nov 2007- March 2008

• Correlation Coefficient = -.4711

Stevens Institute

Stevens Institute

1 – GRMRF.NEUS.eta (9a)

2 – 221.YSU.KFE.FERR.RRTM

3 – BMMY-CCM2.NEUS.avn

4 – GFS.YSU.GRE.FERR.RRTM

5 – GRBLK-CCM2.NEUS.nogaps

6 – K2MRF-Reis.NEUS.avn

7 – K2MY-CCM2.NEUS.cmc

8 – NOG.YSU.BMJ.WSM3.RRTM

1 – GRMRF.NEUS.eta (9a)

2 – 221.YSU.KFE.FERR.RRTM

3 – BMMY-CCM2.NEUS.avn

4 – GFS.YSU.GRE.FERR.RRTM

5 – GRBLK-CCM2.NEUS.nogaps

6 – K2MRF-Reis.NEUS.avn

7 – K2MY-CCM2.NEUS.cmc

8 – NOG.YSU.BMJ.WSM3.RRTM

Conclusions• Gloria:

- WRF-ADCIRC underestimated the surge even after adjusting winds to 30-m ASL.

- A small change in the track related to a different PBL (GFS rather thanYSU) and a small timing adjustment (1-h) resulted in a better peak water levelforecast.

- There are also relatively large sensitivities to surface stress andwaves in the ocean model.

• Real-time Verification– Stevens Institute Surge modeling system has smaller mean and root mean square

errors than NOAA ET and Stony Brook surge models. Negativesurge mean errors in the SSBS system may be related to the absence of waveforcing and/or a low wind bias over the water.

– Stony Brook surge ensemble is under-dispersed and shares many(negative) biases, even for members that have different wind biases.Suggests the need for multi-model surge models in operations (not justdifferent atmospheric forcings) and surge bias corrections.

– Need a larger sample to obtain some probabilistic verification.