1. GFDL’s global non-hydrostatic modeling system for multi-time-scale tropical cyclone simulations and predictions
Shian-Jiann Lin
with contributions from:
M. Zhao, I. Held, and G. Vecchi
NOAA/Geophysical Fluid Dynamics Laboratory
Princeton, NJ, USA
Workshop on Retrospective Simulation and Analysis of Changing SE Asian High-Resolution Typhoon Wind and Wave Statistics
March 12, 2009

2. Outline The prototype GFDL global cloud-resolving model (aka, HiRam)
Model validation: basic climate state & simulated tropical cyclone climatology with the C180 (~50 km) HiRam (Zhao et al. 2009)
Skill of the seasonal predictions
Deterministic forecasts with the C360 (~25 km) HiRam
Initialization: NCEP analysis (large-scale) with 4D vortex-breeding (small-scale)
Preliminarily results:
5-day forecasts for HFIP (Hurricane Forecast Improvement Project )
10-day forecast

3. The GFDL High-Resolution Atmosphere Model (HiRam) is the same as the GFDL AM2.1 used in the IPCC AR4
Except the following:
Non-hydrostatic Cubed-sphere finite-volume dynamical core.
6-category bulk cloud microphysics (based mostly on Lin et al. 1984)
The deep convective parameterization scheme (Relaxed Arakawa-Schubert) is replaced by a non-precipitating shallow convection scheme (based on Bretherton et al )
Surface fluxes modified for high-wind situation over ocean (Moon et al. 2007)

4. Climate Model inter-comparisons:
GFDL finite-volume models vs. other IPCC AR4 models

5. Observed cyclone tracks: 1981-2005
Simulated tracks: (C180 model)
One realization

6. Seasonal cycle of hurricanes (1981-2005)
(red: 4-member ensemble)

7. Inter-annual cycle/trend (1981-2005)
Number of West Pacific Typhoons
Number of East Pacific hurricanes
Number of North Atlantic hurricanes
Reds: observed
Blue: model (4 realizations)
Model-obs correlation ~ 0.83

8. Skill of CSU (statistical) seasonal hurricane forecasts
Suzana J. Camargo, Anthony G. Barnston, Philip J. Klotzbach and
Christopher W. Landsea, 2007

9. GFDL Seasonal (Jul-Dec) hurricane predictions 1985-2005
5-member C180 (~50 km) model ensemble
SST: climatology plus persistent anomaly from June of the forecast year
N. Atlantic
Correlation ~ 0.66
E. Pacific
Correlation ~ 0.63

10. GFDL global models vs. CSU (statistical) hurricane predictions for 1999-2007

11. Hurricanes: intensity vs
Hurricanes: intensity vs. resolution Central pressure – surface wind correlation ( )
C90
C180
Cat 4-5
C360

12. NCEP/GFS TC initialization
“Remove” (by filters) the vortex from the first-guess
“Relocate” the vortex to the observed position
Main issue:
Initial vortex (if existed) location is correct but the intensity is typically very weak

13. Regional GFDL hurricane model initialization
“Remove” (by filters) the vortex from the NCEP analysis
“Grow” a balanced vortex offline by an axis-symmetrical model (to achieve internal dynamical, thermodynamic, and micro-physics balance).
“Insert” the fully developed and balanced vortex into the NCEP background
Main issues:
The large-scale “background” is not in dynamical or thermodynamic balance with the vortex – possibly causing degraded track forecasts
Multiple vortices do not interact with themselves nor the environment
Being 3D (time discontinuous), vortex information does not propagate to the next forecast time.

14. A simple 4D data assimilation for tropical cyclone prediction:
Large-scale nudging (using NCEP T382L64 gridded analysis) +
storm-scale 4D (time continuous) vortex-breeding
00Z
06Z
12Z
18Z
Assimilation
forecast

15. Re-Assimilation of hurricane Katrina into NCEP analysis
GFDL c360 model is capable of reproducing all storms in the IBTrac data with the re-assimilation procedure

16. Katrina forecasts 2005 operational models GFDL C360 HiRam
00Z 08/25
GFDL C360 HiRam
00Z 08/26

17. Intensity forecast:
NCEP/GFS forecasts
GFDL HiRam forecasts

18. Summary:
For seasonal hurricane prediction to be skillful, the model must be able to simulate a credible tropical-cyclone climatology, including inter-annual variability & seasonal cycle
A 25-km global model can be skillful in hurricane intensity forecasts.

19. A simple 4D assimilation for hurricane prediction (continued):
“storm-scale vortex breeding”
Interpolation in time the NHC “best tracks” (latitude, longitude; slp)
Construction of two radial (Gaussian) SLP distributions based on the observed center pressure and model environment pressure (blue area); radial distance determined iteratively.
If the model’s SLP is outside the two bounds, dry air mass is instantly “transported” from(into) the inner area (red) to the outer ring (green area)
Large-scale nudging towards NCEP analysis is gradually masked out (from green to the red region)

20. The Finite-Volume (FV) Cubed Sphere dynamical core
Key features in the Cubed-Sphere dynamical core
Quasi-uniform resolution over the globe; self-consistent global-regional nesting & Adaptive Mesh Refinement capability (to be implemented)
Vertically Lagrangian control-volume discretization (for both hydrostatic & non-hydrostatic)
A Lagrangian (stable for large CFL number) Riemann Solver for sound waves; non-reflective upper boundary condition
Tracer transport is strictly 2D and uses a vertically dependent time stepping, which dramatically enhanced the computational efficiency if many tracers are required (e.g., chemistry, carbon cycle, and cloud microphysics)
Highly scalable: super linear scaling to ~10,000 CPUs at global cloud-resolving resolutions.

21. SLP: Katrina 1st US landfall (~ 4 days before 2nd landfall)

22. Hurricane Ike (2008)
10-day forecast: 00Z