model simulation studies of hurricane isabel in chesapeake bay jian shen virginia institute of...
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Model Simulation Studies of Model Simulation Studies of Hurricane Isabel in Chesapeake BayHurricane Isabel in Chesapeake Bay
Jian ShenJian ShenVirginia Institute of Marine SciencesVirginia Institute of Marine Sciences
College of William and MaryCollege of William and Mary
Background of Storm Surge Modeling Background of Storm Surge Modeling • Numerical models have been successfully applied to
simulate and predict tide and storm surge in coastal seas – SLOSH (Sea, Lake, and Overland Surges for Hurricanes
– ADCIRC (Advanced Circulation Model)
• Impact of the storm surge at any particular location is sensitive to meteorological and topographic parameters
• Inundation is crucial for disaster planning • Prediction of flooding areas depends on model grid
resolution
New Challenges for Numerical ModelingNew Challenges for Numerical Modeling
• More high resolution terrain data are available– LIDAR (LIght Detection And Ranging)
• More real-time observation data are available– Surface elevation – Vertical velocity profile– Wave
• Real-time simulation vs. prediction– Rescue– Inundation
• How to integrate high resolution terrain and real-time observation data into models ?
Airborne LIDAR DataAirborne LIDAR Data
• LIDAR (LIght Detection And Ranging)• Infrared laser ranger provides distance to
ground• Differential GPS provides aircraft location• Inertial reference system provides aircraft
orientation• Post-processing provides x,y,z coordinates of
ground surface• Construction of Digital Elevation Models
(DEMs)
DTMSDTMS
Legend
Elevation (NGVD 29, m)0 - 0.5
0.51 - 1
1.1 - 1.5
1.6 - 2
2.1 - 2.5
2.6 - 3
3.1 - 3.5
3.6 - 4
4.1 - 4.5
4.6 - 5
5.1 - 5.5
5.6 - 6
6.1 - 6.5
6.6 - 7
7.1 - 7.5
7.6 - 8
8.1 - 8.5
8.6 - 9
9.1 - 9.5
9.6 - 10
11 - 15
16 - 20
21 - 27
3 Sec (80-90 m) DTM Vertical Res. 1 m
10 DTM Vertical Res. 0.3 m
Elevation(feet)
Elevation(feet)
USGS 10m DEMUSGS 10m DEM LIDAR 10m DEMLIDAR 10m DEM
Example of LIDAR Data (Miami)Example of LIDAR Data (Miami)
Model Domain RepresentationModel Domain Representation• Small domain is inadequate for storm simulation• Coarse grid is inadequate to resolve irregular
shoreline and small topographic features in estuary• Structured grid is difficult to represent complex
bathymetric in estuary• Unstructured grid has advantage of storm surge
modeling– Use nested grids– Place fine grid in the areas of interest and coarse grid in
the remaining large areas
Model RequirementsModel Requirements
• Must resolve complex shoreline
• Must resolve land features– coastal ridge, dam, inlet, and river– small scale on the order of meters
• Must cover large modeling domain
• Must be computationally efficient
Example of Unstructured Grid (Miami)Example of Unstructured Grid (Miami)
Inlet
InletMiami River
Highway
Highway
0 2 km
Unstructured 3D Model (UnTRIM)Unstructured 3D Model (UnTRIM)
• “UnTRIM” incorporates an Unstructured grid into TRIM model (Tidal, Residual, Intertidal Mudflat), originally developed by Vincenzo Casulli
• It simulates three-dimensional hydrodynamic and transport processes
• It uses an orthogonal unstructured grid• It conserves mass locally as well as globally • It uses Eulerian-Langangian transport scheme • It employs semi-implicit finite difference and finite
volume method- very efficient computationally• It is capable of simulating wet-dry processes
Grid StructureGrid Structure
• Use polygons to represent a prototype estuary (3-, 4-, 5-sides)
• Better fitting complicated geometry in estuarine and coastal environment
• Using orthogonal grid simplifies the numerical algorithm
Model Simulation StudiesModel Simulation Studies• Study the accuracy of model prediction of Isabel forced by
a stationary, circular wind model• Compare model prediction with and without simulating
inundation• Study influence of open boundary condition specification
on surge simulation– still boundary condition vs. inverse pressure adjust boundary
condition
• Study influence of model domain size on surge prediction• Study influence of wind field on model prediction• Study influence of hurricane on transport•
Grid layouts at York and James RiversGrid layouts at York and James Rivers
Gloucester Pt.
0 2 km
0 2 km
Model CalibrationModel Calibration
• Calibrate model for tide
forced by 9 tidal constituents M2, S2, K1, O1, Q1, K2, N2, M4, and M6
• Model was run for 3 months and the results of the last 29 days were used for computing tidal harmonics
• Time step = 5 min.•
Tidal Constituents Comparison Tidal Constituents Comparison (Amplitude)(Amplitude)
Amplitude is in mObservations are based on 1992 data
ModeledObs. ModeledObs. ModeledObs. ModeledObs. ModeledObs. ModeledObs.Stations
Bay Bridge 0.38 0.37 0.06 0.07 0.09 0.09 0.07 0.06 0.05 0.05 0.01 0.02Kiptopeke 0.38 0.37 0.06 0.07 0.08 0.09 0.07 0.06 0.05 0.05 0.01 0.02Sewells Pt. 0.35 0.35 0.06 0.06 0.08 0.08 0.06 0.06 0.05 0.04 0.01 0.02
Gloucester Pt. 0.34 0.33 0.05 0.06 0.07 0.08 0.05 0.05 0.04 0.04 0.01 0.02W indmill Point 0.17 0.16 0.03 0.03 0.04 0.04 0.04 0.03 0.02 0.02 0.01 0.01Lewisetta 0.18 0.17 0.04 0.03 0.04 0.04 0.03 0.02 0.02 0.02 0.02 0.01Solomons Isaland 0.17 0.16 0.03 0.02 0.03 0.04 0.04 0.03 0.03 0.02 0.02 0.01Cambridge 0.24 0.22 0.04 0.03 0.05 0.05 0.06 0.05 0.05 0.04 0.03 0.01Annapolis 0.12 0.13 0.02 0.02 0.03 0.03 0.06 0.06 0.05 0.04 0.01 0.01Baltimore 0.16 0.14 0.03 0.02 0.04 0.03 0.07 0.07 0.06 0.05 0.02 0.01Tolchester Beach 0.18 0.16 0.04 0.02 0.04 0.04 0.08 0.07 0.06 0.05 0.02 0.01
O1 K2M2 S2 N2 K1
Tidal Constituents Comparison (phase)Tidal Constituents Comparison (phase)
ModeledObs. ModeledObs. ModeledObs. ModeledObs. ModeledObs. ModeledObs.
Bay Bridge 99.5 99.5 75.1 75.1 39.4 39.4 199.3 199.3 268.3 268.3 224.7 224.7Kiptopeke 112.0 110.4 79.6 85.6 51.1 49.6 206.4 207.0 275.0 273.7 193.4 235.4Sewells Pt. 122.7 125.4 93.1 101.1 62.8 66.2 211.5 213.4 279.4 281.6 194.6 252.4Gloucester Pt. 130.6 131.9 101.8 105.6 71.3 72.8 215.4 211.3 283.0 281.4 163.3 259.3W indmill Point 181.9 196.4 152.8 166.9 119.1 130.9 241.7 242.9 312.7 313.5 298.9 15.6Lewisetta 254.0 253.9 203.8 231.0 185.6 189.0 289.0 288.3 360.7 355.4 324.8 18.5Solomons Isaland 270.9 276.5 211.9 254.3 202.7 210.2 319.0 329.1 24.4 27.4 325.1 43.8Cambridge 320.7 336.3 257.5 311.1 255.9 271.6 341.3 355.3 40.4 46.8 352.8 105.9Annapolis 7.1 8.8 319.6 348.0 300.8 305.9 0.9 10.1 56.3 65.2 58.9 133.0Baltimore 56.1 53.9 4.9 37.4 345.4 348.3 14.1 20.8 67.6 75.1 120.0 173.9Tolchester Beach 59.8 64.3 3.4 36.9 348.0 356.6 12.4 18.3 65.8 72.3 120.0 184.2
M2 S2Station N2 K1 O1 K2
Phase is in degree
Wind Model (Wind Model (Myers and Malkin, 1961) Myers and Malkin, 1961)
dr
dVV
Vk
dr
p
ps
a
sin
1 2
222
sincoscos1
Vkdr
dV
r
VfV
dr
p
p na
r = the distance from the storm centerp(r) = pressure, pa = central pressure, = the inflow angle across circular isobar toward the storm centerV is the wind speed, f = Coriolis parameter, ks and kn are friction coefficients.
Example of Wind FieldExample of Wind Field
-79 -78 -77 -76 -75 -74
34
35
36
37
38
39
40
5
10
10
15
15
20
20
20
25
2530
35
Isabel Simulation ResultsIsabel Simulation ResultsWith and Without Simulating inundationWith and Without Simulating inundation
Comparison of Model ResultsComparison of Model ResultsWith and Without Simulating InundationWith and Without Simulating Inundation
G loucester Point (with flooding)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeledPredicted (HAM )O bserved
Sewells Point (without flooding)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeledPrediced (HAM )O bserved
Sewells Point (with flooding)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
Modeled
Sweells Hall (HAM)
Observed
Gloucester Point (without flooding)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vatio
n (m
)
ModeledPredicted (HAM)Observed
Comparison of Model ResultsComparison of Model ResultsWith and Without Simulating InundationWith and Without Simulating Inundation
Baltimore (with flood)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeled Prediced (HAM )O bserved
Baltimore (without flood)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeledPredicted (HAM )O bserved
Colonial Beach (with flood)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeledPredicted (HAM )
Observed
Colonial Beach (without flood)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeledPredicted (HAM )Observed
Test Influence of Open Boundary ConditionTest Influence of Open Boundary Condition• Apply inverse pressure adjustment at BC• Run large domain model (east coast) and apply
time series output from large domain model to force small domain model
Influence of Open Boundary ConditionInfluence of Open Boundary Condition
G loucester Point (with flooding)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeledM odeled (IPABC)O bserved
Sewells Point (with flooding)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
Modeled
Modeled (IPABC)
Observed
Swelles Hall (with flooding)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
Time Series BC
IPABC
Observed
Gloucester Point (with flooding)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
Time series BCIPABCObserved
Inverse Pressure Adjustment
Nested Grids
Influence of Wind FieldInfluence of Wind Field
-79 -78 -77 -76 -75 -74
34
35
36
37
38
39
40
5
10
10
15
15
20
20
20
25
2530
35
Comparison of Using Different Wind FieldsComparison of Using Different Wind Fields
Baltimore (with flood)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on (
m)
Modeled (Adj. wind parm.)
Modeled
Observed
Baltimore (with flood)
-0.5
0
0.5
1
1.5
2
2.5
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
M odeled Prediced (HAM )O bserved
Annapolis (with flooding)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on
(m
)
ModeledModeled (IPABC)Observed
Annapolis (with flood)
-0.5
0
0.5
1
1.5
2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72
Hour (from 9/17/2003 0:00)
Ele
vati
on (
m)
M odeled (Adj. w ind parm.)
M odeled
Observed
Current Simulation at Gloucester PointCurrent Simulation at Gloucester Point
Current Simulation at Gloucester Point (using local wind)
-1.0
-0.5
0.0
0.5
1.0
1.5
0 10 20 30 40 50 60 70 80 90 100
Hour (from 9/17/03 0:00)
Velo
city
(ms
-1)
obs. Suf obs. mid obs. Bottom Bottom mid surf
Current at Gloucester Point (Parametric wind model )
-1.0
-0.5
0.0
0.5
1.0
1.5
0 10 20 30 40 50 60 70
Hour (from 9/17/03 0:00 )
Vel
ocit
y (m
s-1
)
Obs. (surface) m/s Obs.(mid) m/s Obs. (bottom) m/sModeled(suface) Modeled(bottom) Modeled (Bottom)
Model Wind Field
Modified Wind Field
ConclusionsConclusions
• Unstructured model is very efficient in simulation tide and storm surge
• Open boundary condition specification influences the surge prediction
• Wind field is critical in the accurate simulation of storm surge
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