hot towers and hurricane intensification
DESCRIPTION
HOT TOWERS AND HURRICANE INTENSIFICATION. Steve Guimond Florida State University. Motivation. TC intensification is a complex, non-linear process governed by physics on a multitude of scales Synoptic scale Vortex scale Convective scale Hydrometeor scale - PowerPoint PPT PresentationTRANSCRIPT
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HOT TOWERS AND HOT TOWERS AND HURRICANE HURRICANE
INTENSIFICATIONINTENSIFICATION
Steve GuimondFlorida State University
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MotivationMotivation
• TC intensification is a complex, non-linear process governed by physics on a multitude of scales– Synoptic scale – Vortex scale– Convective scale– Hydrometeor scale
• Improving TC intensification (for wide range of applications including energy) hinges on better understanding of inner-core dynamics
• In nature, the occurrence of hot towers can often be linked to TC intensification (i.e. Guimond et al. 2009)
• But not always!
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MotivationMotivation
• What are hot towers?• How are they distributed?
EYEEYE
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Latent Heat
Updraft
Background Vortex
Microphysics
Hurricane Hurricane IntensificatioIntensification n
RoadmapRoadmap
Eddy Heat and
Momentum Fluxes
Balanced responseAdjustme
nt
Symmetric heating
Asymmetricheating
Adjustment
Balanced response Adjustme
nt
Intensity and
Structure Change
Nolan and Grasso (2003)
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Latent Heat
Updraft
Background Vortex
Microphysics
Hurricane Hurricane IntensificatioIntensification n
RoadmapRoadmap
Eddy Heat and
Momentum Fluxes
Balanced responseAdjustme
nt
Symmetric heating
Asymmetricheating
Adjustment
Balanced response Adjustme
nt
Intensity and
Structure Change
Nolan and Grasso (2003)
Lightning
Collisions &
Charging
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My ContributionMy Contribution• Characterizing 4-D latent heating in RI
Hurricane– Most estimates of latent heat in TCs are crude
• Satellite – Coarse (space/time)– Not enough information (no winds)
– Airborne dual-Doppler retrieval• 2 km x 2 km x 1 km x ~30 minutes
• Understanding inner-core dynamics that is triggered by hot towers– What spatial/temporal scales of heating does the hurricane
“feel” ? – Implications for observing systems lightning
• LANL network ~ 200 m resolution for VHF– Are small scale details of lightning/heating necessary to
capture intensification or are bulk quantities sufficient?
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Latent Heating RetrievalLatent Heating Retrieval
• Based on Roux and Ju (1990)– Solve water budget with Doppler radar– Compute latent heat with vertical velocity &
lapse rate
• Improvements to algorithm– Examine assumptions (uncover sensitivities)– Reduced uncertainties with ancillary data– Uncertainty estimates on final product
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Inner-Core DynamicsInner-Core Dynamics
• Balanced adjustment of hot towers at ~100 m vs. ~2 km and feedbacks onto vortex scale
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Latent Heat
Updraft
Background Vortex
Microphysics
Hurricane Hurricane IntensificatioIntensification n
RoadmapRoadmap
Eddy Heat and
Momentum Fluxes
Balanced responseAdjustme
nt
Symmetric heating
Asymmetricheating
Adjustment
Balanced response Adjustme
nt
Intensity and
Structure Change
Nolan and Grasso (2003)
Lightning
Collisions &
Charging
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Inner-Core DynamicsInner-Core Dynamics
• Balanced adjustment of hot towers at ~100 m vs. ~ 2 km and feedbacks onto vortex scale
• Dynamics heavily motivated by observations– Basic-state vortex using Doppler data
• Made stable to all wavenumber perturbations
– Heating perturbations using EDOP data
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Peak Updrafts from Peak Updrafts from EDOPEDOP
Heymsfield et al. 2009
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• Goal: Understand fundamental impacts of hot towers (HTs) on hurricane intensification (Convective and Vortex Scales)
• New version of latent heating retrieval– 4-D distribution of heating in RI Hurricane (first
time)• Non-linear simulations addressing symmetric
and asymmetric dynamics that result from HTs – Balanced adjustment of hot towers at ~100 m vs. ~2
km and feedbacks onto vortex scale• Proxy for lightning = latent heat ?• Help prove the value of lightning data in
understanding/predicting dynamics of hurricanes– Physics on fine space/time scales are important– Role of the asymmetric mode
Summary and Summary and Ongoing Ongoing WorkWork
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Acknowledgments• Gerry Heymsfield (EDOP and dropsonde data)• Paul Reasor and Matt Eastin (Guillermo edits)• Scott Braun (MM5 output)• Robert Black (cloud particle processing)
References• Roux and Ju (1990)• Braun et al. (2006), Braun (2006)• Gamache et al. (1993)• Heymsfield et al. (1999)• Reasor et al. (2008)• Black (1990)
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Idealized Calculation