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Methods for Introducing VHTs in Idealized Models: Retrieving Latent Heat

Methods for Introducing VHTs in Idealized Models: Retrieving Latent Heat. Steve Guimond Florida State University. Motivation. Previous theoretical work on TC intensification Many authors (i.e. Montgomery and Enagonio, 1998)

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Methods for Introducing VHTs in Idealized Models: Retrieving Latent Heat

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  1. Methods for Introducing VHTs in Idealized Models: Retrieving Latent Heat Steve Guimond Florida State University

  2. Motivation • Previous theoretical work on TC intensification • Many authors (i.e. Montgomery and Enagonio, 1998) • Initialize with 2D, balanced vorticity or PV anomalies in barotropic/QG models • Nolan and Montgomery (2002) • Dry simulations; initialize with 3D, unbalanced, weak, prescribed θ’ • Nolan et al. (2007) • Dry, “equivalent heat injection”, 4D (time evolving θ’)

  3. Attacking the problem • What is the best way to characterize and introduce remotely sensed VHTs into an idealized simulation? • Latent Heating • Divergence (Mapes and Houze 1995) • Observationally motivated simulation • Moist model • Consider evolution of latent heating into θ’

  4. Latent Heating Algorithm • Model testing… • Non-hydrostatic, full-physics, cloud-resolving (2-km) MM5 simulation of Hurricane Bonnie (1998; Braun 2006)

  5. Structure of Latent Heat • Goal: saturation using production of precipitation (Gamache et al. 1993) • Divergence, diffusion and offset are small and can be neglected

  6. Magnitude of Latent Heat • Requirements • Temperature and pressure (mean dropsonde) • Vertical velocity (radar)

  7. Combining Structure and Magnitude • Net source of precipitation • Positives • Full radar swath of latent heat in various types of clouds (sometimes 4-D) • Negatives • Estimating local tendency term • Steady-state ? • Guillermo (1997) ~34 mintue sampling • Thermo based on mean dropsonde • Drop size distribution uncertainty and feedback on derived parameters

  8. Only care about condition of saturation for heating • Some error OK • Tendency, reflectivity-derived parameters, horizontal winds (excellent in P-3)

  9. Testing algorithm in model

  10. Testing algorithm in model

  11. Testing algorithm in model

  12. P-3 Doppler Radar Results

  13. 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

  14. P-3 Doppler Radar LH in Guillermo(1997)

  15. P-3 Doppler Radar LH in Guillermo(1997)

  16. P-3 Radar LH: Thermodynamic Sensitivity

  17. Conclusions andFuture Work • New method for LH retrievals • Ability to accept some errors in water budget • Local tendency, radar-derived parameters • LH magnitude relatively insensitive to thermo • Sensitive to vertical velocity (most important) • Test mass continuity vertical velocity or use EDOP? • ~30 minute radar sampling does nothing for water budget • Local tendency à order of mag. less than Qnet • Incorporate WSR-88Ds for tendency, heating evolution • Hybrid method • Doppler radar and dropsonde

  18. LANL work • End goal: find relationship between latent heating and lightning • TCs are diabatic systems • Satellite latent heating is crude (e.g. no winds) • Doppler radar coverage sparse • To get lightning • cloud liquid water, graupel, ice collisions • Requires large updraft à latent heat • Lightning fills gap in convective monitoring • New sensors (precise 3-D lightning locations) • Lightning type, what else? • Inject latent heating coincident with lightning

  19. Taken from Richard Blakeslee TCSP ppt -8 to -15 dB large, wet, asymmetric ice to large, wet snow aggregates -13 to -17 dB  medium, wet graupel or small hail -18 to -26 dB  small, dry ice particles to dry, low density snow

  20. EYE

  21. Model Diabatic Heating Budget

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

  23. Acknowledgments • Scott Braun (MM5 output) • Robert Black (particle processing) • Paul Reasor and Matt Eastin (Guillermo edits) • Paul Reasor and Mark Bourassa (advice) References • Black (1990) • Braun et al. (2006), Braun (2006) • Gamache et al. (1993) • Heymsfield et al. (1999) • Reasor et al. (2008) • Satoh and Noda (2001)

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