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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Presentation Transcript
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
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
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?
slide13
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
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
slide29
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)
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
slide32
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
slide33
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
slide34
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)
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