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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE PowerPoint Presentation
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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

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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