1. Steve Guimond

3. Main driver of hurricane genesis and intensity change is latent heat release Main driver of hurricane genesis and intensity change is latent heat release Observationally derived 4-D distributions of latent heating in hurricanes are sparse Observationally derived 4-D distributions of latent heating in hurricanes are sparse Most estimates are satellite based (i.e. TRMM) Most estimates are satellite based (i.e. TRMM) Coarse space/time Coarse space/time No vertical velocity No vertical velocity Few Doppler radar based estimates Few Doppler radar based estimates Water budget (Gamache 1993) Water budget (Gamache 1993) Considerable uncertainty in numerical model microphysical schemes Considerable uncertainty in numerical model microphysical schemes e.g. McFarquhar et al. (2006) and Rogers et al. (2007) e.g. McFarquhar et al. (2006) and Rogers et al. (2007)

4. Refined latent heating algorithm (Roux 1985; Roux and Ju 1990) 1) Observing System Simulation Experiment (OSSE) 2) Examine assumptions/Uncover sensitivities 3) Parameterization 4) Present radar-derived retrievals 5) Uncertainty estimates 6) Impact study

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

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

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

10. Positives… 1) Full radar swath coverage in various types of clouds (3-D and 4-D) 2) Only care about condition of saturation, not magnitude Uncertainties to consider… 1) Estimating tendency term ( Steady-state ?) 2) Quality of vertical velocity 3) Thermo based on composite eyewall dropsonde 4) Drop size distribution and feedback to derived parameters

11. Aircraft (z = 1.5 – 5.5 km) Courtesy of Matt Eastin

14. NOAA WP-3D dual-Doppler radar observations of Hurricane Guillermo (1997; Reasor et al. 2009) NOAA WP-3D dual-Doppler radar observations of Hurricane Guillermo (1997; Reasor et al. 2009) Variational analysis retrieves important variables (u,v,w) Variational analysis retrieves important variables (u,v,w) 2 km x 2 km x 1 km x ~34 min (10 snapshots = ~5.5 h) 2 km x 2 km x 1 km x ~34 min (10 snapshots = ~5.5 h) 2-D particle data (Robert Black) in Katrina (2005) 2-D particle data (Robert Black) in Katrina (2005) Statistical fit between reflectivity and liquid water content Statistical fit between reflectivity and liquid water content

15. Figures from NHC report

26. Physical uncertainty Sampling uncertainty Bootstrap (Monte Carlo method) Bootstrap (Monte Carlo method) 95 % CI on mean = ~14% 95 % CI on mean = ~14%

28. NOAA WP-3D dual-Doppler radar observations of Hurricane Guillermo (1997; Reasor et al. 2009) NOAA WP-3D dual-Doppler radar observations of Hurricane Guillermo (1997; Reasor et al. 2009) Variational analysis retrieves important variables (u,v,w) Variational analysis retrieves important variables (u,v,w) 2 km x 2 km x 1 km x ~34 min (10 snapshots = ~5.5 h) 2 km x 2 km x 1 km x ~34 min (10 snapshots = ~5.5 h) Nonlinear, compressible, Navier-Stokes solver; LANL’s HIGRAD model (Reisner et al. 2005) Nonlinear, compressible, Navier-Stokes solver; LANL’s HIGRAD model (Reisner et al. 2005) Realistic setup Realistic setup 2 km radar domain stretched to boundaries (1300 km 2 ), 71 levels 2 km radar domain stretched to boundaries (1300 km 2 ), 71 levels Full Coriolis Full Coriolis Reynolds OI high-resolution SST Reynolds OI high-resolution SST ECMWF background ECMWF background State-of-the-art cloud physics (Reisner and Jeffery 2009) State-of-the-art cloud physics (Reisner and Jeffery 2009)

29. Airborne radar Environment (ECMWF)

30. Nudge momentum equations with first radar composite Thermal wind balance NHC ~ 958 hPa

31. Force energy equation with retrievals (no heat from model) t = 0 t = 10 t = 44t = 78 ∆t = 34 5.1 h

32. Initial water vapor forcing from heat retrieval conversion MICROPHYSICS t = 0 t = 10 t = 44t = 78 ∆t = 34 5.1 h

38. Black = Obs; Green = Retrieval; Red = Freemode Tangential WindVorticity

39. RetrievalFreemode

40. Improved latent heat of condensation algorithm Improved latent heat of condensation algorithm Computation of saturation (production of precipitation) performed well in numerical model setting Computation of saturation (production of precipitation) performed well in numerical model setting Need observational validation of computed saturation Need observational validation of computed saturation Precipitation storage term shown to be important for retrievals Precipitation storage term shown to be important for retrievals Developed parameterization based on advection Developed parameterization based on advection Uncertainty estimates for heating magnitudes Uncertainty estimates for heating magnitudes Insensitive to thermo, sensitive to vertical velocity Insensitive to thermo, sensitive to vertical velocity Physical and sampling uncertainty = ~ 15 – 25 % Physical and sampling uncertainty = ~ 15 – 25 % Likely first 4D view of latent heating in RI TC Likely first 4D view of latent heating in RI TC

41. Assuming saturation of entire TC inner-core inappropriate Assuming saturation of entire TC inner-core inappropriate Need to determine saturation state for |w| ≤ ~ 5 m/s Need to determine saturation state for |w| ≤ ~ 5 m/s For |w| > ~ 5 m/s saturation better assumption For |w| > ~ 5 m/s saturation better assumption Latent heat retrieval simulation vs. freemode simulation Latent heat retrieval simulation vs. freemode simulation Retrievals: Reduce RMSE and explain 20 – 25 % more variance in wind Retrievals: Reduce RMSE and explain 20 – 25 % more variance in wind Larger errors in freemode run: Larger errors in freemode run: 1)Transport of water vapor (advection & diffusion) 2)Microphysics scheme (limits on heat release)

43. Deeper understanding of dynamics responsible for TC intensification triggered by convection Deeper understanding of dynamics responsible for TC intensification triggered by convection Fundamental property = latent heat release Fundamental property = latent heat release Nolan and Montgomery (2002; NM02), Nolan and Grasso (2003; NG03) and Nolan et al. (2007) Nolan and Montgomery (2002; NM02), Nolan and Grasso (2003; NG03) and Nolan et al. (2007) Azimuthal mean heating  dominates Azimuthal mean heating  dominates Pure asymmetric heating  negligible impact Pure asymmetric heating  negligible impact Vorticity anomalies extract energy before axisymmetrizing Vorticity anomalies extract energy before axisymmetrizing

44. Nolan studies use simple, linear heating prescriptions Nolan studies use simple, linear heating prescriptions Does observational heating structure matter? Does observational heating structure matter?

45. Reproduce nonlinear results of Nolan and Grasso (2003) Reproduce nonlinear results of Nolan and Grasso (2003) Able to reproduce the symmetric heating perturbation results to a LARGE degree. Able to reproduce the symmetric heating perturbation results to a LARGE degree. Asymmetric heating case  NOT SO EASY! Asymmetric heating case  NOT SO EASY!

46. HIGRAD (Reisner et al. 2005; Reisner and Jeffery 2009) HIGRAD (Reisner et al. 2005; Reisner and Jeffery 2009) Equation set… Equation set… Discretization Discretization Temporal  semi-implicit (higher order an option) Temporal  semi-implicit (higher order an option) Space  finite volume on A-grid (non-staggered) Space  finite volume on A-grid (non-staggered)

47. Attempt to mirror settings in WRF simulations of NG03 Attempt to mirror settings in WRF simulations of NG03 Dry Dry Same domain (600 km sq. box) and grid spacing (2 km) Same domain (600 km sq. box) and grid spacing (2 km) Higher vertical resolution (71 levels) and model top (22 km) Higher vertical resolution (71 levels) and model top (22 km) Same Coriolis frequency (5.0 x 10 -5 s -1 ) Same Coriolis frequency (5.0 x 10 -5 s -1 ) Free slip of momentum and scalars on lower boundary Free slip of momentum and scalars on lower boundary Relaxation to Jordan sounding on sides and top Relaxation to Jordan sounding on sides and top Diffusion coefficients set to constant 40 m 2 /s Diffusion coefficients set to constant 40 m 2 /s Time step = 20 s, 6 h runs Time step = 20 s, 6 h runs

48. (1)Stable vorticity profile from NM02 (2)Compute u and v by solving Poisson equations (3)Baroclinic structure following NM02 CLEAN VORTEX TO START

49. How get balanced vortex at t = 0 ? How get balanced vortex at t = 0 ? Solving for fields and starting model  oscillations Solving for fields and starting model  oscillations Used nudging approach Used nudging approach thermal wind balance 1K WN3

50. symmetric -8.8 x 10 -2 hPa asymmetric -2.7 x 10 -2 hPa NG03: GR10:

51. Absolute Angular Momentum Budgets Absolute Angular Momentum Budgets Secondary Circulation

54. Discrepancy with asymmetric NG03 likely due to symmetric secondary circulation in basic-state vortex Discrepancy with asymmetric NG03 likely due to symmetric secondary circulation in basic-state vortex NG03 did not consider a secondary circulation NG03 did not consider a secondary circulation Secondary circulations are ubiquitous features of real TCs Secondary circulations are ubiquitous features of real TCs Suggests impact of asymmetric mode important for more realistic numerically simulated TCs Suggests impact of asymmetric mode important for more realistic numerically simulated TCs Diffusion (sub-grid model) in HIGRAD responsible for Diffusion (sub-grid model) in HIGRAD responsible for 1)Significant, secondary circulation in vortex 2)Variability in vorticity anomalies produced from heating Inherent uncertainty with diffusion parameterization in models at cloud resolving resolutions (e.g. 1 - 2 km) Inherent uncertainty with diffusion parameterization in models at cloud resolving resolutions (e.g. 1 - 2 km) Need turbulence resolving resolution (~ 50 m) simulations to understand role of asymmetric mode Need turbulence resolving resolution (~ 50 m) simulations to understand role of asymmetric mode

55. Thanks to PhD Committee (Paul Reasor, Mark Bourassa, Bob Hart, Michael Navon, Chris Jeffery, Gerry Heymsfield, Ming Cai and X. Zou) Thanks to PhD Committee (Paul Reasor, Mark Bourassa, Bob Hart, Michael Navon, Chris Jeffery, Gerry Heymsfield, Ming Cai and X. Zou) Jon Reisner for mentoring and modeling expertise Jon Reisner for mentoring and modeling expertise Dave Nolan for discussion, re-running WRF and figures. Dave Nolan for discussion, re-running WRF and figures. Scott Braun, Robert Black, Dave Moulton and Matt Eastin for providing data, figures and assistance. Scott Braun, Robert Black, Dave Moulton and Matt Eastin for providing data, figures and assistance. Funded by The Los Alamos National Laboratory through an LDRD project with Dr. Chris Jeffery the PI Funded by The Los Alamos National Laboratory through an LDRD project with Dr. Chris Jeffery the PI Funding also from NASA ocean vector winds and NOAA grants Funding also from NASA ocean vector winds and NOAA grants

56. Future Work Extend latent heating simulations out ~ 12 h and use Guillermo Day 2 observations for validation. Extend latent heating simulations out ~ 12 h and use Guillermo Day 2 observations for validation. How does radial inflow affect asymmetric heat perturbation? How does radial inflow affect asymmetric heat perturbation? Accelerated, more vigorous axisymmetrization? Accelerated, more vigorous axisymmetrization? Excite instability in inner-core? Excite instability in inner-core? Turbulence modeling to reduce uncertainty in diffusion parameterization, impact of asymmetric heating. Turbulence modeling to reduce uncertainty in diffusion parameterization, impact of asymmetric heating.

57.  Radial flow and associated secondary circulation driven by dissipation  Free slip along boundaries has been set correctly  Wind components are barotropic in lowest layer.  Thus, only a function of diffusive fluxes  Why does diffusion/turbulence scheme play a different role in HIGRAD than WRF?  Eddy diffusivity was set to same values as NG03 WRF runs  Stress tensor similar to WRF (also tried Laplacian in HIGRAD)  Must come down to…design of numerical solution procedure HIGRADWRF (1)Fully implicit in time(1)Time-split (2)A grid(2)C grid (3)No artificial filters(3)Divergence damping, etc.

60. Bootstrap (Monte Carlo method) Bootstrap (Monte Carlo method) Auto-lag correlation  ~30 degrees of freedom Auto-lag correlation  ~30 degrees of freedom 95 % CI on mean = 101 - 133 K/h or ~14% 95 % CI on mean = 101 - 133 K/h or ~14% Physical uncertainty  8 K/h or ~27 % of mean (30 K/h) Physical uncertainty  8 K/h or ~27 % of mean (30 K/h)

61. AAM Budget for 1K WN3: t = 10 min θ’θ’

62. AAM Budget for 1K WN3: t = 20 min

63. AAM Budget for 1K WN3: t = 30 min

65. AAM Budget for 1K WN3: t = 50 min

66. AAM Budget for 1K WN3: t = 60 min

67. Radial Flow: t = 30 min HIGRAD WRF

68. Radial Flow: t = 6 h HIGRAD WRF

69. With HIGRAD, I can reproduce these WRF results: (1)Min pressure perturbation for: Localized heat sources; Symmetric heat sources (2)Perturbation radial velocity for symmetric heat source (3)Vertical velocity for symmetric heat source (4)Perturbation tangential velocity for symmetric heat source (5)Perturbation vbar at surface for symmetric heat source