GFDL’s global non-hydrostatic modeling system for multi-time-scale tropical cyclone simulations and predictions

Shian-Jiann Lin

NOAA/Geophysical Fluid Dynamics LaboratoryPrinceton, NJ, USA

with contributions from:

M. Zhao, I. Held, and G. Vecchi

Workshop on Retrospective Simulation and Analysis of Changing SE Asian High-Resolution Typhoon Wind and Wave Statistics

March 12, 2009

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) HiRamInitialization: NCEP analysis (large-scale) with 4D vortex-breeding (small-scale)

Preliminarily results:– 5-day forecasts for HFIP (Hurricane Forecast Improvement Project )– 10-day forecast

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. 2004)

Surface fluxes modified for high-wind situation over ocean (Moon et al. 2007)

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

Observed cyclone tracks: 1981-2005

Simulated tracks: 1981-2005 (C180 model)

One realization

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

Number of North Atlantic hurricanes

Number of East Pacific hurricanesNumber of West Pacific Typhoons

Reds: observedBlue: model (4 realizations)

Model-obs correlation ~ 0.83

Inter-annual cycle/trend (1981-2005)

Suzana J. Camargo, Anthony G. Barnston, Philip J. Klotzbach andChristopher W. Landsea, 2007

Skill of CSU (statistical) seasonal hurricane forecasts

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

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

Hurricanes: intensity vs. resolutionCentral pressure – surface wind correlation (1981-2005)

C90

C180

C360

Cat 4-5

NCEP/GFS TC initialization

1. “Remove” (by filters) the vortex from the first-guess

2. “Relocate” the vortex to the observed position

Main issue:

Initial vortex (if existed) location is correct but the intensity is typically very weak

Regional GFDL hurricane model initialization

1. “Remove” (by filters) the vortex from the NCEP analysis

2. “Grow” a balanced vortex offline by an axis-symmetrical model (to achieve internal dynamical, thermodynamic, and micro-physics balance).

3. “Insert” the fully developed and balanced vortex into the NCEP background

Main issues:1. The large-scale “background” is not in dynamical or thermodynamic

balance with the vortex – possibly causing degraded track forecasts

2. Multiple vortices do not interact with themselves nor the environment

3. Being 3D (time discontinuous), vortex information does not propagate to the next forecast time.

A simple 4D data assimilation fortropical cyclone prediction:

Large-scale nudging (using NCEP T382L64 gridded analysis) +storm-scale 4D (time continuous) vortex-breeding

00Z 06Z 12Z 18ZAssimilation

forecast

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

00Z 08/25

00Z 08/26

www.weatherundergroud.com

GFDL C360 HiRam

2005 operational models

Katrina forecasts

NCEP/GFS forecastsGFDL HiRam forecasts

Intensity forecast:

Summary:1. 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

2. A 25-km global model can be skillful in hurricane intensity forecasts.

A simple 4D assimilation for hurricane prediction (continued):

“storm-scale vortex breeding”

1. Interpolation in time the NHC “best tracks” (latitude, longitude; slp)

2. Construction of two radial (Gaussian) SLP distributions based on the observed center pressure and model environment pressure (blue area); radial distance determined iteratively.

3. 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)

4. Large-scale nudging towards NCEP analysis is gradually masked out (from green to the red region)

• 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.

The Finite-Volume (FV) Cubed Sphere dynamical core

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

Hurricane Ike (2008)10-day forecast: 00Z 20080906-20080915