1 / 28

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)

Download Presentation

Methods for Introducing VHTs in Idealized Models: Retrieving Latent Heat

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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)

More Related