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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

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### P-3 Doppler Radar Results

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
- 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

- 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

- Goal saturation using production of precipitation (Roux and Ju 1990)
- Divergence, diffusion and offset are small and can be neglected

Magnitude of Latent Heat

- Requirements
- Temperature and pressure (composite eyewall, high-altitude dropsonde)
- Vertical velocity (radar)

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

- 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 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 Heating

Including parameterization

- 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

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

- 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

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 modelHow is Qnet related to condensation?

- Only care about condition of saturation for heating
- Some error OK
- Tendency, reflectivity-derived parameters

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

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

Dropsondes

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

Testing algorithm in model

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

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