A latent heat retrieval in a rapidly intensifying hurricane
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34th Conference on Radar Meteorology. A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE. Steve Guimond and Paul Reasor Florida State University. Background/Motivation. Main driver of hurricane genesis and intensity change is latent heat release

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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

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A latent heat retrieval in a rapidly intensifying hurricane

34th Conference on Radar Meteorology

A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

Steve Guimond and Paul Reasor

Florida State University


Background motivation

Background/Motivation

  • Main driver of hurricane genesis and intensity change is latent heat release

  • Observationally derived 4-D distributions of latent heating in hurricanes are sparse

    • Most estimates are satellite based (i.e. TRMM)

      • Coarse space/time

      • No vertical velocity

    • Few Doppler radar based estimates

      • Water budget (Gamache 1993)

  • Considerable uncertainty in numerical model microphysical schemes

    • McFarquhar et al. (2006)

    • Rogers et al. (2007)


Current approach

Current Approach

  • Refined latent heating algorithm (Roux and Ju 1990)

    • Model testing:

      • Non-hydrostatic, full-physics, quasi cloud-resolving (2-km) MM5 simulation of Hurricane Bonnie (1998; Braun 2006)

    • Examine assumptions

    • Uncover sensitivities to additional data

    • Uncertainty estimates


Numerical model testing

Numerical Model Testing


Structure of latent heat

Structure of Latent Heat

  • Goal saturation using production of precipitation (Roux and Ju 1990)

    • Divergence, diffusion and offset are small and can be neglected


Magnitude of latent heat

Magnitude of Latent Heat

  • Requirements

    • Temperature and pressure (composite eyewall, high-altitude dropsonde)

    • Vertical velocity (radar)


Putting it together

Putting it Together

  • Positives…

    • Full radar swath of latent heat in various types of clouds (sometimes 4-D)

  • Uncertainties to consider…

    • Estimating tendency term

      • Steady-state ?

    • Thermo based on composite eyewall dropsonde

    • Drop size distribution uncertainty and feedback on derived parameters


A latent heat retrieval in a rapidly intensifying hurricane

Model Heating Budget Results


A latent heat retrieval in a rapidly intensifying hurricane

Examining Assumptions with Doppler radar


Impact of tendency on heating

Impact of Tendency on Heating

  • Clouds are not steady state

  • Guillermo TA  tendency term with ~34 min delta T

    • Sufficient to approximate derivative?

    • Typical value of tendency term for ∆t  0?


Impact of tendency on heating1

Impact of Tendency on Heating


Impact of tendency on heating2

All heating removed

Impact of Tendency on Heating


A latent heat retrieval in a rapidly intensifying hurricane

Impact of Tendency on Heating

R2 = 0.714

How to parameterize tendency term?

  • Using 2 minute output from Bonnie simulation

    (2) Coincident (flight level) 2 RPM LF data


Impact of tendency on heating3

Impact of Tendency on Heating

Including parameterization


P 3 doppler radar results

P-3 Doppler Radar Results

  • Rapidly intensifying Hurricane Guillermo (1997)

  • NOAA WP-3D airborne dual Doppler analysis (Reasor et al. 2009)

  • 2 km x 2 km x 1 km x ~34 min

  • 10 composite snapshots


A latent heat retrieval in a rapidly intensifying hurricane

Hurricane Guillermo (1997)


Uncertainty estimates

Uncertainty Estimates

Mean =117 K/h

  • Bootstrap (Monte Carlo method)

  • Auto-lag correlation  ~30 degrees of freedom

  • 95 % confidence interval on the mean = (101 – 133) K/h

  • Represents ~14% of mean value


Conclusions and ongoing work

Conclusions and Ongoing Work

  • New version of latent heat retrieval

    • Identified sensitivities, constrained problem with more data (e.g. numerical model)

    • Developed tendency parameterization

      • Statistics with P-3 LF data

      • Validate saturation with flight level data

    • Ability to accept some errors in water budget

      • Local tendency, radar-derived parameters, etc.

  • Monte Carlo uncertainty estimates (~14 % for w > 5)

  • Goal: Understand impact of retrieved forcings on TC dynamics

    • Simulations with radar derived vortices, heating

      • Smaller errors with retrieved heating vs. simulated heating


  • A latent heat retrieval in a rapidly intensifying hurricane

    Acknowledgments

    • Scott Braun (MM5 output)

    • Robert Black (particle processing)

    • Paul Reasor and Matt Eastin (Guillermo edits)

    • Gerry Heymsfield (dropsonde data & satellite images)

      References

    • Roux (1985), Roux and Ju (1990)

    • Braun et al. (2006), Braun (2006)

    • Gamache et al. (1993)

    • Reasor et al. (2009)

    • Black (1990)


    A latent heat retrieval in a rapidly intensifying hurricane

    Thermodynamic Sensitivity


    Testing algorithm in model how is q net related to condensation

    Testing algorithm in modelHow is Qnet related to condensation?

    • Only care about condition of saturation for heating

      • Some error OK

      • Tendency, reflectivity-derived parameters


    A latent heat retrieval in a rapidly intensifying hurricane

    Below melting level:

    Z = 402*LWC1.47 n = 7067 RMSE = 0.212 g m-3

    Above melting level (Black 1990):

    Z = 670*IWC1.79 n = 1609 r= 0.81

    Constructing Z-LWC Relationships

    Hurricane Katrina (2005) particle data from P-3

    • August 25, 27, 28 (TS,CAT3,CAT5)

    • Averaged for 6s  ~ 1km along flight path

      • Match probe and radar sampling volumes


    A latent heat retrieval in a rapidly intensifying hurricane

    Doppler Analysis Quality

    • Comparison to flight-level data at 3 and 6 km height

      • Vertical velocity (eyewall ~1200 grid points)

        • RMSE 1.56 m/s

        • Bias 0.16 m/s


    A latent heat retrieval in a rapidly intensifying hurricane

    Dropsondes

    • Composite sounding

      • DC8 and ER2 (high-altitude) total of 10 samples

      • Deep convection

        • Sat IR, AMPR, wind and humidity


    Testing algorithm in model

    Testing algorithm in model

    • Non-hydrostatic, full-physics, cloud-resolving (2-km) MM5 simulation of Hurricane Bonnie (1998; Braun 2006)


    A latent heat retrieval in a rapidly intensifying hurricane

    Testing algorithm in model


    A latent heat retrieval in a rapidly intensifying hurricane

    Testing algorithm in model


    A latent heat retrieval in a rapidly intensifying hurricane

    Testing algorithm in model


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