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Dynamical Seasonal Hurricane Hindcast Simulations

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Dynamical Seasonal Hurricane Hindcast Simulations. Tim LaRow Y.-K. Lim, D.W. Shin, E. Chassignet and S. Cocke CDPW Meeting – October 23, 2007- Tallahassee email: [email protected]. Outline. Motivation Previous Studies Detection/Tracking Algorithm Experimental Design Results - PowerPoint PPT Presentation
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Dynamical Seasonal Hurricane Hindcast Simulations Tim LaRow Y.-K. Lim, D.W. Shin, E. Chassignet and S. Cocke CDPW Meeting – October 23, 2007- Tallahassee email: [email protected]
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Page 1: Dynamical Seasonal Hurricane Hindcast Simulations

Dynamical Seasonal Hurricane Hindcast Simulations

Tim LaRowY.-K. Lim, D.W. Shin, E. Chassignet and S. Cocke

CDPW Meeting – October 23, 2007- Tallahasseeemail: [email protected]

Page 2: Dynamical Seasonal Hurricane Hindcast Simulations

Outline

•Motivation

•Previous Studies

•Detection/Tracking Algorithm

•Experimental Design

•Results

•Atlantic Domain

•Summary/Conclusions/Future

Page 3: Dynamical Seasonal Hurricane Hindcast Simulations

Motivation – Part 1Can We Simulate Interannual Variability?

1997 Observed Tracks 2005 Observed Tracks

Page 4: Dynamical Seasonal Hurricane Hindcast Simulations

Motivation – Part 2: Can We Simulate Intensity?

http://www.nhc.noaa.gov/pastall.shtml

Page 5: Dynamical Seasonal Hurricane Hindcast Simulations

Previous Studies

•Current climate models can simulate many of the features of observed tropical cyclones that have spatial scales resolvable by such models. These include:

•Warm core structure (upper-tropospheric anticyclonic circulation above cyclonic low-level circulation) •Existence of strong upward motion andHeavy precipitation accompanying the storm

•Geographical distributions, intraseasonal, and interannual variability of simulated storms are similar to observed (Manabe et al. 1970, Manabe 1990, 1992; Wu and Lau 1992Haarsma et al. 1993; Vitart et al. 2006, 2007; Bengtsson et al. 1982, 1995,2007;Camargo et al. 2005; Knutson et al. 2007)

Page 6: Dynamical Seasonal Hurricane Hindcast Simulations

Summary from Previous Studies

• Modeled tropical cyclones tend to be:• too weak• tracks too short and some have a poleward bias• storms too large and • lack of genesis in certain regions.

Problems in part due to low resolution models used. O(200-400km) –

although not the complete story.

Page 7: Dynamical Seasonal Hurricane Hindcast Simulations

Detection Algorithm

•Local relative vorticity maximum greater than 4.5x10-5 s-1 is located at 850hPa.

•Next, the closet local minimum in sea level pressure is detected and defines the center of the storm. Must exist within a 2°x2° radius of the vorticity maximum.

•Third, the closest local maximum in temperature averaged between 200hPa and 500hPa is defined as the center of the warm core. The distance from the warm core center and the center of the storm must not exceed 2°. The temperature must decrease by at least 6K in all directions from the warm core center within a distance of 4°.

Max/Min are located and gradients calculated using bicubic splines which allow for higher precision than the model resolution.

Page 8: Dynamical Seasonal Hurricane Hindcast Simulations

Tracking Algorithm

After the data base of storm snapshots are collected a check is performed to see if there are storms within 200km during the next 6 hours.

If no, the trajectory is stopped. If yes, the closest storm to the previous 6 hours storm's trajectory is picked. If more than one storm is identified, preference is given to storms which are west and poleward of the given location.

•Trajectories must last more than 2 days, have lowest model level wind velocity within a 8° radius circle centered on the storm center greater than 17 m s-1 during at least 2 days (does not have to be consecutive days).

Page 9: Dynamical Seasonal Hurricane Hindcast Simulations

Experiments

Page 10: Dynamical Seasonal Hurricane Hindcast Simulations

Experimental Design

•Atlantic hurricane season (June-November) hindcast simulations from 1986 to 2005 (20 years).•Weekly updated observed SSTs (Reynolds et al. 2002).•FSU/COAPS global spectral model – T126L27 resolution ~ 100km•4 member ensembles for each year. Time lagged ECMWF atmospheric initial conditions centered on 1 June of the respective year. A total of 80 experiments.

•RAS Convective Scheme (Hogan and Rosmond 1991) - Control NCAR (Zhang and McFarlane 1995) Convection Scheme

•6 hourly output

Page 11: Dynamical Seasonal Hurricane Hindcast Simulations

Storm Composite

Page 12: Dynamical Seasonal Hurricane Hindcast Simulations

Ensemble Model Results

Page 13: Dynamical Seasonal Hurricane Hindcast Simulations

Atlantic “HTV” Interannual Variability

r=0.78

EL EL EL EL EL EL ELLA LA LA LA LA

Page 14: Dynamical Seasonal Hurricane Hindcast Simulations

ACE Index - Atlantic Domain(units 104 kt2)

r=0.85

Page 15: Dynamical Seasonal Hurricane Hindcast Simulations

Model and Observed TC Tracks 1986-2005Control

Ensemble 1

Page 16: Dynamical Seasonal Hurricane Hindcast Simulations

500hPa Streamlines – Control Experiment

Page 17: Dynamical Seasonal Hurricane Hindcast Simulations

“HTV” Landfalls 1986-2005 - Control

“Gates” Ensemble 1 Ensemble 2 Ensemble 3 Ensemble 4 HURDAT

Texas 8 14 10 4 35

Louisiana-Miss. 3 3 2 5 12

Florida-Georgia-Al. 23 17 17 15 32

Mid-Atlantic 2 4 5 2 13

New England 2 4 5 2 13

Page 18: Dynamical Seasonal Hurricane Hindcast Simulations

Ensemble Summary

Observations Ensemble 1 Ensemble 2 Ensemble 3 Ensemble 4 Ens. Mean

Total # ofStorms 245 242 234 234 249 240

Correlation 0.76 0.51 0.71 0.62 0.78

Variance 25.25 20.2 12.96 18.01 14.89 12.55

Page 19: Dynamical Seasonal Hurricane Hindcast Simulations

Sensitivity to Convection Scheme

r=0.78r=-0.01

Page 20: Dynamical Seasonal Hurricane Hindcast Simulations

Sensitivity to Convection - continued

NRL NCAR

Control Experiment NCAR Convection Scheme

Page 21: Dynamical Seasonal Hurricane Hindcast Simulations

Sensitivity to Convection Scheme - ContinuedNCAR Convection

Page 22: Dynamical Seasonal Hurricane Hindcast Simulations

500hPa Streamlines NCAR Convection

Page 23: Dynamical Seasonal Hurricane Hindcast Simulations

“HTV” Landfalls 1986-2005 – Sensitivity to Convection Scheme

“Gates” NCAR Convection Scheme HURDAT

Texas 21 35

Louisiana-Miss. 6 12

Florida-Georgia-Al. 25 32

Mid-Atlantic 12 13

New England 12 13

Page 24: Dynamical Seasonal Hurricane Hindcast Simulations

Intensity Issue

Page 25: Dynamical Seasonal Hurricane Hindcast Simulations

Model and Observational Wind-Pressure Relationship-Atlantic Domain

Knutson et al. 2007, BAMS18km Non-Hydrostatic Model

FSU/COAPS T126 Model(All 80 Ensemble Members)

1980-2005 1986-2005

Wind Pressure Relationship

Min

slp

(hP

a)

Wind Speed (m/s)

Lowest Pressure 936hPa

Page 26: Dynamical Seasonal Hurricane Hindcast Simulations

Model Atlantic/Pacific Basin Summary

Atlantic Pacific

Avg. Duration 7.5 days 8.3 days(8.9 days) (9.6 days)

Avg. Central Pressure 995.9hPa 990.7hPa

850hPa Wind Max 60m/s 66m/s

Max Intensity 936hPa 926hPa

Avg. Number of Storms 11.7 15.9

Page 27: Dynamical Seasonal Hurricane Hindcast Simulations

Conclusions/Summary/Future Work

Page 28: Dynamical Seasonal Hurricane Hindcast Simulations

Summary/Conclusions

Ensemble hindcast results from a relatively high resolution atmospheric model (T126L27) have been presented for 20-years of the Atlantic Basin hurricane season using 2 different convection schemes.

Linear correlation of the interannual variability of the tropical storm frequency against observation was found to be high (0.78) using the Hogan and Rosmond convection, less so for the Zhang and McFarlane convection scheme.

Large sensitivity in track locations, storm numbers and interannual variability was found between the two convection schemes and choice of diffusion coefficient (not shown).

Model appears to simulate the ENSO-Atlantic covariation well.

Page 29: Dynamical Seasonal Hurricane Hindcast Simulations

Summary/Conclusions - cont.

•The model with the best interannual variability was NOT the best in simulating land falling storms along the east coast of the U.S. and Gulf of Mexico. In part due to the atmospheric large-scale response to the model's convection and the resulting large-scale steering flow.

The model's surface wind-pressure relationship was found to be similar to a 20km global model (JMA not shown) and also an 18km non-hydrostatic model (GFDL). All models fail to produce sufficient CAT3-5 level storms in terms of surface winds.

Page 30: Dynamical Seasonal Hurricane Hindcast Simulations

Present Work

Better understanding of the sensitivity of tracks and intensity to the choice of convection, diffusion coefficients and tracking algorithm.

Use selective years from the hindcast experiments and run the FSU/COAPS regional spectral model to study higher horizontal resolution impacts on hurricane seasonal statistics.

Page 31: Dynamical Seasonal Hurricane Hindcast Simulations

HOPE-OM1 Ocean Grid

Page 32: Dynamical Seasonal Hurricane Hindcast Simulations

Questions


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