1. An Applied Example Alexander Gohm IMGI, University of Innsbruck
Atmospheric Modeling
An Applied Example
Alexander Gohm
IMGI, University of Innsbruck
17 March 2005
HPC Seminar, A. Gohm, IMGI

2. Outline The concept of atmospheric modeling
The basic equations of an atmospheric model
Modeling at IMGI:
Past research topics
History of numerical modeling
The RAMS model – an example
Application of RAMS to bora winds
RAMS – upcoming studies
17 March 2005
HPC Seminar, A. Gohm, IMGI

3. Yes, but it will soon clear up!
The basic concept of atmospheric modeling or how to make a weather forecast?
Observing the atmosphere
Running numerical models on powerful computers
Simulations
Yes, but it will soon clear up!
Observations
Oh, it’s raining!
Initialization
Verification
We solve a set of nonlinear equations which describe the motion of the atmospheric fluid as well as process such as precipitation
The nonlinearity requires a numerical treatment on “big” computers
Observations are needed to initialize and verify the model forecast
17 March 2005
HPC Seminar, A. Gohm, IMGI

4. The basic concept of atmospheric modeling or how to make a weather forecast?
Deriving the initial conditions (current state of the atmosphere) from observations is a computationally expensive process
The European Center for Medium Range Weather Forecast (ECMWF) located in Reading (UK) uses a so-called 4DVAR Analysis technique in order to derive the initial state (analysis)
The total computing time at ECMWF for a whole forecast cycle (~6 hours) is subdivided into
~30% for 4DVAR Analysis
~20% for one deterministic forecast
~50% for 50 ensemble forecasts
The usual approach in limited area modeling (LAM):
Using the analysis and/or forecast of a global model (GM) as the initial and boundary conditions of the LAM
LAM GM
17 March 2005
HPC Seminar, A. Gohm, IMGI

5. The equations of an atmospheric model
An example: RAMS model
Three equations of motion
One thermodynamic equation
Several continuity equations for water species
One mass continuity equation
Local tendencies are derived base on
Advection
Pressure gradient
Coriolis force
Gravit. acceleration
Turbulent diffusion
Radiation
Divergence
17 March 2005
HPC Seminar, A. Gohm, IMGI

6. Past modeling research topics at IMGI
Flow through mountain gaps:
Föhn in valleys
flow y x
Flow around mountains:
formation
of lee vortices
flow y x
Flow over mountains:
Formation of downslope windstorms
flow z x
Heavy rain due to
orographic lifting
flow z x
17 March 2005
HPC Seminar, A. Gohm, IMGI

7. History of numerical modeling at IMGI
Million
model
grid
points
Model
setup
nonlinear
linear 3D 2D
idealized flow
realistic flow
1 grid
2 grid
3 grid
6 grids
1 CPU
8-24 CPU
CDC3300
SGI XL/o2000
SGI o2000
zid-cc
17 March 2005
HPC Seminar, A. Gohm, IMGI

8. The RAMS model – an example
Regional Atmospheric Modeling System (RAMS)
Developed at Colorado State University (CSU), currently maintained by the US spin-off company ATMET, and released under the GNU public license
RAMS is a limited area model designed for the mesoscale (~1–100 km horizontal mesh size)
It is mostly used as a research model to study various atmospheric phenomena, for example:
Atmospheric flows over complex terrain (e.g., Föhn winds, downslope windstorms)
Convection and the formation of clouds
Orographically induced heavy precipitation
Fronts, thunderstorms, and hurricanes
Air pollution applications (atmospheric dispersion)
Large Eddy Simulation (LES)
17 March 2005
HPC Seminar, A. Gohm, IMGI

9. The RAMS model – an example
Finite-difference method:
The nonlinear partial differential equations are discretized on a spatial grid (see next slide)
The time differencing is based on a hybrid scheme:
Forward time differencing for thermodynamic variables
Leapfrog differencing for the velocity and pressure variables
A “time-split” scheme is used:
A smaller time step for terms in the model equations that are responsible for the propagation of fast wave modes (acoustic and gravity waves)
A longer time step for other terms (e.g., horizontal advection and Coriolis force)
17 March 2005
HPC Seminar, A. Gohm, IMGI

10. The RAMS model – an example
u, v
T, r y x x
Grid structure:
Staggered grid (Arakawa C grid)
Rotated polar-stereographic projection
Terrain-following coordinate
Vertically stretched grid
Several nested grids (two-way communication) z x z x z x y
17 March 2005
HPC Seminar, A. Gohm, IMGI

11. The RAMS model – an example
Parameterization of sub-grid scale processes:
Turbulent mixing/diffusion:
Several schemes depending on model resolution (e.g., prognostic equation for TKE)
Surface layer parameterization (LEAF-2 submodel):
Solves heat and water balance equations for several ground layers
Evaluates the exchange of these quantities with the atmosphere
Convective parameterization
Radiation parameterization
Parameterization of cloud microphysics
Sub-grid scale
turbulent motion
The LEAF-2 submodel
17 March 2005
HPC Seminar, A. Gohm, IMGI

12. Application of RAMS to bora winds
Bora is severe downslope windstorm which occurs on the leeward side of the Dinaric Alps
Downslope winds represent a potential hazard for aircrafts
There is a need for an improvement of the physical understanding of such winds (improvement of forecasts)
The goal of the combined observational/numerical study by Gohm and Mayr (2005)* was to
investigate the small-scale structure (~1 km) of bora winds
elucidate the role of boundary layer effects (surface friction and convective mixing) in the development of the bora flow
* Gohm, A. and G.J. Mayr, 2005: Numerical and observational case-study of a deep Adriatic bora, Q. J. R. Meteorol. Soc., in press
17 March 2005
HPC Seminar, A. Gohm, IMGI

13. Application of RAMS to bora winds
The event: bora winds on 28 March 2002
The model setup:
5 (6) nested grids with a horizontal mesh size as low as 800 m (267 m)
56 vertical levels (0-21 km AGL) with a vertical mesh size as low as 30 m
model grid 5
Senj
5 nested grids
gap
17 March 2005
HPC Seminar, A. Gohm, IMGI

14. Application of RAMS to bora winds
zid-cc linux cluster
Fortran 90 PGI compiler
MPICH for parallel computing
8 processors
Master-slave configuration (1 master, 7 slaves)
Domain decomposition
6,443,024 grid points
1440 time steps on the coarsest grid for a 1-day forecast
Computing time:
~180 seconds for a 60-second time step
74 hours for a 24-hour simulation (factor ~3)
Comparison with global forecast of ECMWF (2001): 15 min for a 24-hour simulation (factor ~0.01)
17 March 2005
HPC Seminar, A. Gohm, IMGI

15. Application of RAMS to bora winds
Model
x=800 m
Flow
Observation
Flow       
Agreement: mixed boundary layer, flow separation, low-level jet flow, primary and secondary hydrostatic gravity wave
Disagreement: nonhydrostatic lee waves („trapped lee waves“)
17 March 2005
HPC Seminar, A. Gohm, IMGI

16. Application of RAMS to bora winds
The increase of horizontal model resolution leads to a slight improvement in capturing nonhydrostatic lee waves
Model x=267 m
Observation
17 March 2005
HPC Seminar, A. Gohm, IMGI

17. Application of RAMS to bora winds
Orography
Wind speed at 500 m MSL
Model
x=800 m
gap
gap
approach corridor
gap
gap
Several narrow bands of strong winds downstream of mountain gaps
The boundaries of the jet flow represent strong shear lines
Shear lines are potential regions of strong turbulence (hazard for aircrafts)
17 March 2005
HPC Seminar, A. Gohm, IMGI

18. Application of RAMS to bora winds
Summarizing the results:
We could show that gravity wave dynamics are responsible for the development of bora winds
We could clarify the role of boundary layer effects in the generation of gap flows, flow separation, and shear lines
We could demonstrate the great value of combining high-resolution numerical simulations with airborne remote sensing observations
Ongoing study:
Clarify the breakthrough (onset) phase of bora winds
Investigate rotor formation (vortices with horizontal rotation axis; reversed flow)
17 March 2005
HPC Seminar, A. Gohm, IMGI

19. RAMS – upcoming/ongoing projects
zid-cc linux cluster:
Continuation of bora study
Lecture on RAMS modeling, SS2005
Opteron cluster:
Air pollution study: transport and diffusion of traffic-induced pollutants in the Inn Valley
Climate-glacier interactions in the tropics
AGRID:
Precipitation in the Alpine region
17 March 2005
HPC Seminar, A. Gohm, IMGI

20. And finally … “… and last, not least,
how to choose among the great many parameters to be varied and tested,
how to look at three-dimensional flow configurations with our restricted cinemascopic abilities,
how to help being swamped by the mass of data?”
Vergeiner (1975)
17 March 2005
HPC Seminar, A. Gohm, IMGI