storm surge modeling with cygnss winds · • 10% wind speed measurement uncertainty at tropical...

GNSS+R 2017 Ann Arbor, MI23-25 May 2017

Storm Surge Modeling with CYGNSS WindsApril Warnock1, Chris Ruf2, Mary Morris3

(1) SRI International, Ann Arbor, MI(2) University of Michigan

(3) NASA Jet Propulsion Laboratory

Summary• The NASA CYGNSS Mission was launched on 15

Dec 2016• CYGNSS will provide high temporal resolution

measurements of ocean surface winds in tropical cyclones (TCs)

• Examine the use of CYGNSS wind products to improve prediction of coastal storm surge due to TCs

• Use the ADCIRC 2DDI storm surge forecast model with simulated CYGNSS winds over Hurricane Irene

• Results indicate that CYGNSS-derived winds have a positive impact on storm surge modeling predictions

Storm Surge Modeling, GNSS+R 201724 May 2017 2

CYGNSS Mission Overview• The Cyclone Global Navigation Satellite System (CYGNSS)

consists of 8 microsatellites, each with a 4-channel GPS bi-static radar receiver

• Measure GPS signals scattered from the ocean surface to determine ocean surface roughness and wind speed

• 10% wind speed measurement uncertainty at tropical cyclone wind speed levels

• Use small satellites so many can be flown to improve sampling• Low Earth orbit (520 km) at low (35o) inclination to maximize

temporal sampling at tropical latitudes


Motivation• Tropical Cyclone intensity (peak sustained winds, Cat #) is the

most common metric used to characterize storm strength and allude to its distructive potential

• Much more damage (property and loss of life) is caused by flooding than by high winds

• Storm surge is the increase in sea level due to storms. The primary forcing mechanism is water driven to the shore by near surface winds.

• Desire to improve the forecasting of storm surge


Storm Surge Forecasting• Storm surge forecasts use finite element, time evolving models

guided by underlying primitive hydrostatic momentum and continuity equations

• Boundary conditions imbedded in the model include coastal boundaries and bathymetry (ocean depth)

• The accuracy of storm surge forecast in extreme weather is typically limited by the quality and availability of meteorological observations that force the model


ADCIRC Storm Surge Model• ADvanced CIRCulation 2-Dimensional Depth Integrated

(ADCIRC-2DDI) model – Finite element mesh covering the Western North Atlantic, Gulf of

Mexico, and Caribbean Sea with 58,369 triangular elements of order 100 km in the open ocean and order 20 km near the storm

Storm Surge Modeling, GNSS+R 201724 May 2017

finite element mesh (a) and bathymetry (b)

6

Case Study – Hurricane IreneLife Cycle• 21Aug 2011 Tropical Storm• 22-24 Aug Intensification Period• 24 Aug 2011 Max Intensity = Cat 3 • 27 Aug 2011 Weaken to Cat 1• 27-29 Aug 2011 Make Landfall (North

Carolina to New York)Storm Surge• 1.2–1.8m typical over affected coastal

regions• 2.2 m max in North CarolinaForecast Skill• Winds generally well forecast by HWRF


ADCIRC Storm Surge Forecasts• Model Initialization

– Ramp function is implemented over first two days to transition from zero circulation initial conditions to full tidal amplitude values

– Spin-up time of 4 days prior to start time

• Meteorological Forcing– Navy Operational Global Atmospheric Prediction System (NOGAPS)

forecast winds and pressure for background fields (3 hr, 1 deg grid)– Hurricane Weather Research and Forecast (HWRF) reanalysis winds

during August 21, 2011 00:00 – August 29, 2011 00:00. HWRF uses dynamic nested grids to follow the trajectory of the hurricane (3 hr, three nested grids @ 1/4, 1/16 and 1/32 deg)


Storm Surge Obs and ADCIRC Predictions using Full Reanalysis Winds


80.0° W 77.5 ° W 75.0° W 72.5° W 70.0° W 67.5° W

35.0° N

37.5° N

40.0° N

42.5° N

8534720

8656483

8519483

8536110

86388638651370

8632200

8557380

851056084491308461490

8452660

8531680

8661070

8636580

ADCIRC Forecast Skill Using All Reanalysis Data

• Daily peak water surface level at 15 NOAA CO-OPS tide gauge stations during landfall period– 26 Aug 2011 18:00 –

28 Aug 2011 18:00• Compare Obs to

ADCIRC forecast– Mean error is -7.3 cm– RMS error is 17.5 cm


Vary Meteorological Forcing Inputs to ADCIRC

• Use NOGAPS background wind and pressure as inputs away from storm in all cases

• Three options for input winds in storm#1: Reduce HWRF winds by 20% within 3x(radius of maximum winds)#2: Increase HWRF winds by 20% within 3x(radius of maximum winds)#3: Simulated CYGNSS winds derived from HRWF using mission end-to-end simulator with realistic time and space sampling and retrieval errors

• Fit CYGNSS data to parametrized wind model over storm region to fill in gaps between samples


Typical CYGNSS Sampling During Storm Overpass

• U


Parametric TC Wind Model• Fit v(r) wind model to CYGNSS observations


2. . .

2 2.

122( )

2

M P M P M P

M P

r R V fRfrv r

R r

Validity of Parametric Model (1)• Apply estimator to ~200 storms

– Use HWRF as truth wind field– Derive simulated CYGNSS observations from HWRF and apply

parametric fitting model– Compare Vmax and Rmax of model to actual values in HWRF field


Validity of Parametric Model (2)• Compare model and actual significant wind radii (34,

50 and 64 knot winds)


Variable Met Forcing Results• 20% reduction tends

to produce lower surge

• 20% increase tends to produce high surge

• CYGNSS winds tend to improve storm surge forecast

RMS Errors• 17.7 cm (20% low)• 19.3 cm (20% high)• 14.4 cm (CYGNSS)


Conclusions and Next StepsConclusion• 20% errors in HWRF storm winds produce significant errors

in storm surge predictions, which can be largely corrected by the use of CYGNSS satellite observations

Future Work• Examine other storm case studies with poor HWRF forecast

skill• Refine CYGNSS data assimilation approach beyond simple

parametrized wind structure model to better represent azimuthal asymmetries in storm structure

• Add wave effects to ADCIRC using the tightly-coupled ADCIRC-SWAN framework


storm surge modeling with cygnss winds · • 10% wind speed measurement uncertainty at tropical...

Documents