Remote Sensing and Modeling of Hurricane Intensification Steve Guimond and Jon Reisner Atmospheric Dynamics EES-2 FSU.

Remote Sensing and Modeling of Hurricane

IntensificationSteve Guimond and Jon ReisnerAtmospheric Dynamics EES-2

FSU

Why study hurricanes?

• Societal– Protection of life and property

• Energy (DOE LANL)– Northern Gulf of Mexico

• Largest portion of domestic oil & gas exploration, production, processing Economy

• DOD (Navy)

Why study hurricanes?

• Societal– Protection of life and property

• Energy (DOE LANL)– Northern Gulf of Mexico

• Largest portion of domestic oil & gas exploration, production, processing Economy

• DOD (Navy)

LSU

Solutions?

• Mitigating risks relies heavily on accurate, timely forecasts– Track– Intensity– Structure

• However, problems with forecasting…

Methods for solution

• Main reason for forecasting troubles:– Incomplete understanding and observation of

hurricane physics• Understanding

• State-of-the-art atmospheric model (Reisner et al. 2005)– Compressible Navier-Stokes– Explicit cloud microphysics– Superior numerics

– Observation• Unique airborne dual-doppler radar dataset

– Rapidly intensifying Hurricane Guillermo (1997)– High resolution:

» 2 km spatial» 30 minute temporal

• LANL dual-frequency lightning array

Basic Hurricane Dynamics

• Diabatic systems– Latent heat extracted from ocean and

released in deep convection

• Forces direct and indirect circulations– Energy mass momentum

Updraft

Background Vortex

Latent Heat

Microphysics

Eddy Heat and Momentum

Fluxes

Balanced respons

e

Adjustment

Symmetric

heating

Asymmetricheating

Adjustment

Balanced respons

e

Adjustment

Intensity and

Structure Change

Hurricane Intensifica

tion Roadmap

Nolan and Grasso (2003)

Airborne Doppler Radar

• 3-D reflectivity and latent heat animations

• Latent heat retrieved following Guimond (2008)

Simulations

• Environment and boundary conditions– ECMWF re-analysis

• Stretched Grid– 2km radar domain extending to 15 km

• Quiet environment

• Constant SST (29ºC)

• Forcing from radar derived latent heat

Model Evaluation

Moist Potential Vorticity Animation

€

MPV =1

ρωa • ∇θv

Angular Momentum Budgets

• Useful for explaining mechanics of storm spin-up

• Computed in cylindrical coordinate system– dr = 2 km– 85 azimuthal points

€

∂Ma

∂t= −

1

ρr

∂[rρ uMa ]

∂r−

1

ρ

∂[ρ wMa ]

∂z−

1

ρr

∂[rρ ′ u Ma′]

∂r−

1

ρ

∂[ρ ′ w Ma′]

∂z+ rFθ

€

Ma = rv +for

2

2

Vortex Intensification

Angular Momentum Budgets: Run 1

€

∂Ma

∂t= −

1

ρr

∂[rρ uMa ]

∂r−

1

ρ

∂[ρ wMa ]

∂z−

1

ρr

∂[rρ ′ u Ma′]

∂r−

1

ρ

∂[ρ ′ w Ma′]

∂z+ rFθ

Angular Momentum Budgets: Run 1

€

∂Ma

∂t= −

1

ρr

∂[rρ uMa ]

∂r−

1

ρ

∂[ρ wMa ]

∂z−

1

ρr

∂[rρ ′ u Ma′]

∂r−

1

ρ

∂[ρ ′ w Ma′]

∂z+ rFθ

Summary/Conclusions

• Radar retrieved heating produces realistic comparison to actual storm even in “idealized” setting

• Latent heat initialization believed superior to vortex insertion methods

• Role of eddies large initially, then axisymmetrization process takes over– Hydrostatic and gradient wind adjustment

Future Work: Lightning

• Basic physics– Lightning proxy for intense convective activity,

requires…• Supercooled liquid water, ice and graupel collisions• Strong updraft and latent heating

• New LANL ground-based detection array– Operates in VLF & VHF radio bands– Precise 4D locations– Detects cloud-ground and intracloud strokes– 24/7 monitoring over large regions

• Placed in Louisiana area, offshore oil rigs

Updraft

Background Vortex

Latent Heat

Particle Growth

Lightning

Microphysics

Eddy Heat and Momentum

FluxesBalanced response

Adjustment

Symmetric heating

Asymmetricheating

Adjustment

Balanced response

Adjustment

Collisions

Intensity and

Structure Change

Hurricane Intensification

Roadmap

Nolan and Grasso (2003)

Acknowledgments

• Thanks to Jon Reisner (EES-2), Chris Jeffrey (ISR-2) and the LANL Hurricane Lightning team