1. 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

2. 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)

3. Current Approach Model testing: Examine assumptions
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

4. Numerical Model Testing

5. Structure of Latent Heat
Goal saturation using production of precipitation (Roux and Ju 1990)
Divergence, diffusion and offset are small and can be neglected

6. Magnitude of Latent Heat
Requirements
Temperature and pressure (composite eyewall, high-altitude dropsonde)
Vertical velocity (radar)

7. Putting it Together Positives… Uncertainties to consider…
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

8. Model Heating Budget Results

9. Examining Assumptions with Doppler radar

10. 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 ?
Should tendency term be the same or similar regardless of delta T? This should depend on the time scale of the feature. Derivatives are scale independent, correct? Does this rule only apply for linear functions?

11. Impact of Tendency on Heating
Should tendency term be the same or similar regardless of delta T? This should depend on the time scale of the feature. Derivatives are scale independent, correct? Does this rule only apply for linear functions?

12. Impact of Tendency on Heating
All heating removed

13. 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

14. Impact of Tendency on Heating
Including parameterization 14

15. 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

16. Hurricane Guillermo (1997)

27. 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 27

28. 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

29. Acknowledgments References
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)

30. Thermodynamic Sensitivity

31. Testing algorithm in model How is Qnet related to condensation?
Only care about condition of saturation for heating
Some error OK
Tendency, reflectivity-derived parameters
Testing algorithm in model How is Qnet related to condensation?

32. Hurricane Katrina (2005) particle data from P-3
Constructing Z-LWC Relationships
Below melting level:
Z = 402*LWC n = RMSE = g m-3
Above melting level (Black 1990):
Z = 670*IWC n = r = 0.81
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

33. 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

34. Dropsondes Composite sounding
DC8 and ER2 (high-altitude) total of 10 samples
Deep convection
Sat IR, AMPR, wind and humidity

35. Testing algorithm in model
Non-hydrostatic, full-physics, cloud-resolving (2-km) MM5 simulation of Hurricane Bonnie (1998; Braun 2006)

36. Testing algorithm in model

37. Testing algorithm in model

38. Testing algorithm in model