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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

This study presents a refined algorithm for estimating latent heat release in rapidly intensifying hurricanes using Doppler radar data. The results provide insights into the structure and magnitude of latent heating, as well as the impact of tendency on heating. Uncertainty estimates and parameterization techniques are also discussed.

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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

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  1. 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 • 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… • 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?

  11. Impact of Tendency on Heating

  12. All heating removed Impact of Tendency on Heating

  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

  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)

  17. 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

  18. 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

  19. 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)

  20. Thermodynamic Sensitivity

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

  22. 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

  23. 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

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

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

  26. Testing algorithm in model

  27. Testing algorithm in model

  28. Testing algorithm in model

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