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.

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Presentation transcript:

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 ADCIRC MM5 / WRF NCAR-AFWA Bogus Method YSU PBL, GFS PBL, MY PBL runs –NARR initial condition 36 km 12 km 4 km

NARR Initial Conditions Lin Microphysics YSU PBL Landfall occurs ~ 1 h delayed ~30 miles east of observed landfall

Gloria Tracks PBL NCAR-NCEP Global Reanalysis IC

Wind Speed kts

CREATE ANIMATION OF YSU 10m 1.0x YSU 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 – NOAA ET Surge –Atmospheric Forcing – GFS – Stony Brook Storm Surge Model –Atmospheric Forcing – MM5/WRF –Ocean Model - ADCIRC Ocean Model –

Real Time Ensemble 36 days during Nov – March 2008 with Full Ensemble –Nov – 9 days –Dec – 12 days –Jan – 15 days

Wave Impacts during Cool Season SBSS model member 9a Average daily errors from Nov March 2008 Correlation Coefficient =

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 than YSU) and a small timing adjustment (1-h) resulted in a better peak water level forecast. -There are also relatively large sensitivities to surface stress and waves 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. Negative surge mean errors in the SSBS system may be related to the absence of wave forcing 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 just different atmospheric forcings) and surge bias corrections. –Need a larger sample to obtain some probabilistic verification.