1. New Capabilities for the Regional Atmospheric Modeling System (RAMS)
Implementing Very-High Resolution Capabilities into a Mesoscale Atmospheric Model
New Capabilities for the Regional Atmospheric Modeling System (RAMS)
Craig J. Tremback, ATMET Robert L. Walko, ATMET/Duke Silvia Trini-Castelli, CNR, Torino, Italy R. Ohba/colleagues, MHI, Nagasaki, Japan

2. Regional Atmospheric Modeling System (RAMS)
Two very-high resolution applications with special model versions in the past
Nicholls (CSU) – building simulation (Δx ~ 5 m)
Ying (NASA) – direct wind tunnel simulation (Δx ~ 1 cm)

3. Regional Atmospheric Modeling System (RAMS)
Driving reasons for implementation of very-high resolution capabilities
Use of RAMS for local dispersion applications (scale of individual plant)
Urban dispersion of chemical/biological agents
Treatment of “steep” topography on all scales

4. Regional Atmospheric Modeling System (RAMS)
Considerations to convert an atmospheric mesoscale model for very-high resolution capabilities
Vertical coordinate – almost all mesoscale models use terrain-following coordinate
Sub-grid diffusion schemes
Surface fluxes (vertical walls, different materials)
Radiation – all mesoscale models treat long/short- wave radiation as vertical process

5. Problems with Terrain-Following () Coordinate Systems
All terrain-following coordinate models have difficulty in handling “steep” topography
In z systems, dependent on ratio ztopo/z
Problems arise in taking horizontal gradients
ETA coordinate model (Mesinger/Janjic) developed to address this problem for p coordinate systems
In z systems, what we really want is a true Cartesian horizontal gradient…

6. Terrain-Following Horizontal Gradient Computation

7. The Main Culprit… Horizontal Diffusion
Horizontal diffusion was causing the majority of the “bad” effects in RAMS when topography became too steep.
New method: interpolate model fields from the z levels to true Cartesian surface, then take gradient.
Allowable Δz improved by a factor of 3-5 in most cases.
Disadvantages:
About 15% slower than standard diffusion. There are coding strategies to fix this…
Problems due to horizontal gradients also enter through advection, Coriolis, and pressure gradient terms.
Not flux conservative
The lower boundary condition is problematic.

8. A Permanent Solution ?
ETA-type step-coordinate model used successfully:
Simple Cartesian grid easy to implement
Eliminates all coordinate-induced, numerical truncation errors
Runs faster per gridpoint, almost 2x faster for basic numerics
Allows arbitrarily steep topography (cliffs, buildings, etc.
However…

9. ETA Disadvantages However, ETA-type coordinate has drawbacks:
As implemented at NCEP, topography must jump in steps of Δz/Δ P, even along smooth slopes.
Need to numerically deal with “corners”. It has been shown that the ETA model generates noise.
Usually needs more gridpoints (and memory/disk), hence slower physics.
Gridpoints underground can be blocked from computation relatively easily; not as easy to block from memory.

10. ETA Disadvantages Important drawback to ETA-type coordinate:
Computational issues when desiring high vertical resolution all along a slope
2000 m
0 m

11. Toward a Better Solution…
ADAP (ADaptive APerature) coordinate
Mostly following work of Adcroft, et al for oceanographic model
“Shaved” ETA-type coordinate
Standard ETA coordinate
ADAP coordinate

12. ADAP Features Grid structure is a true Cartesian grid.
The apertures of the grid cell faces are adapted to topography that would block the flow.
Implemented as an option in RAMS; z still supported because high-resolution along slopes is still an issue. Vertically- nested grids can help…
Same technique can be applied to other types of obstacles: buildings, vegetative canopy, etc.
Allows very complex shapes (overhangs, tunnels, etc.)
All components (topography, buildings, vegetation, etc.) can be present in simulation at same time.

13. RAMS Vertical Nesting Can add vertically-nested grid along slope
Nest can have same horizontal resolution as “coarse” grid
Nest is not required to extend to model top
Not ideal solution, but can help…
2000 m
0 m

14. ADAP and Vegetation

15. RAMS/ADAP Modifications
Subgrid, isotropic turbulence options:
Deardorff TKE available for a long time, used in LES runs
Recently implemented E- and E-l (S. Trini Castelli, CNR, Torino, Italy)
All model terms must account for partially-closed apertures
Complete transition of RAMS from finite-difference model to finite-volume model

16. ADAP Numerical Differencing for Advective Terms
Finite-difference form
Finite-difference flux form
Finite-volume flux form

17. Subgrid Scheme Testing
MHI 3D-hill. Horizontal cross-section at the second σ-coordinate vertical level (z = 8 m). Isolines of the u horizontal wind component field. E-l, E-, Mellor-Yamada and Deardoff closures. BC2 boundary condition.
 Turbulence closure models and their application in RAMS
Trini Castelli S., Ferrero E., Anfossi D., Ohba R., Environmental Fluid Mechanics, Special Issue RAMS5th Workshop

18. Subgrid Scheme Testing
EPA V8 2D Valley
Speed: observations
Speed: Mellor-Yamada closure
Speed: E-l closure
 Atmospheric dispersion in non-homogeneous conditions – simulation of a wind tunnel tracer experiment
Trini Castelli S., Ferrero E., Anfossi D.
Proceeding del International workshop on Physical Modelling of flow and dispersion phenomena, Prato FI, Italy) 3-5 September 2003

19. Subgrid Scheme Testing
EPA V8 2D Valley
u: observations
u : Mellor-Yamada closure
u : E-l closure
 Atmospheric dispersion in non-homogeneous conditions – simulation of a wind tunnel tracer experiment
Trini Castelli S., Ferrero E., Anfossi D.
Proceeding del International workshop on Physical Modelling of flow and dispersion phenomena, Prato FI, Italy) 3-5 September 2003

20. RAMS/ADAP Very High-Resolution Simulation Examples
Flow around a single rectangular building
(CEDVAL A1-1, Re = 32750)
Flow through an array of buildings
(CEDVAL B1-1, Re = 56390)
Flow through an array of buildings on a slope
RAMS configuration
Two grids: x = 10 m & 2 m; z = 2 m, stretched
Neutral, horizontally homogeneous initialization
5 m/s initial flow; Re  100
Deardorff isotropic TKE subgrid scheme

21. Flow around a single building
Building size:
x=20m y=30m z=25m
Cedval: 1:200

22. Flow around a single building
Z = 0.28H

23. Flow through a building array
Z = 1 m

24. Flow through a building array

25. Flow through buildings on a slope
5 m/s

26. Flow through buildings on a slope

27. RAMS/ADAP Very High-Resolution Simulation Examples
Flow through an idealized downtown region
RAMS configuration
Two grids: x = 16 m & 4 m; z = 4 m
Stable, horizontally homogeneous initialization
10 m/s initial flow from the west
Deardorff TKE subgrid scheme
4x4 array of city “blocks”, about 130x130 m
Each block divided into 4 buildings, randomly set to m in height.
Streets 36m wide

28. Flow through downtown region
Arrangement of city blocks
130x130 m each
Streets 36m wide

29. Flow through downtown region
Typical vertical section through region

30. Flow through downtown region
100 m AGL
150 m AGL

31. Flow through downtown region
After 30 minutes of simulation
100m AGL

32. Flow through downtown region
150m AGL

33. Flow through downtown region

34. Flow through downtown region

35. MHI MEASURES (Multiple Radiological Emergency Assistance System for Urgent Response)
Accident Identification and Processing System (AIPS)
Accident Course Inference System (ACIS)
Accident Simulation Analysis System (ASAS)
We developed 4 kinds of new technique shown here; one is a simulation technique of building with RAMS code. Second is an improvement of turbulence model by full 3D equation of k/l model. Third is a development of emergency response system of nuclear accident in Japan. The last one is a high-speed computing system by using PC cluster of 128 CPU.
Environmental Dose Projection System (EDPS)
RAMS/HYPACT

36. Environmental Dose Projection System (EDPS)
Meteorological data (20km mesh)
Topographical data (50m mesh)
Building data (10m mesh)
RAMS code
Constant release rate
Gravitational depletion
Dry & wet deposition
HYPACT code
Conversion by using a traveling time of each particle
Last, I introduce the new emergency response system for nuclear accident in Japan which we developed for the Ministry of Economy, Trade and Insustries in Japan. This system combines three kinds of codes; first one is RAMS code for the simulation of meteorology. Second one is HYPACT code for gas diffusion. Last one is our original radiation code which was named TEDOP. We applied a new function of building simulation into original RAMS code with 10m mesh size. Next, we made two functions of gravitational depletion and dry & wet deposition into original HYPACT code. Last, we developed our original radiation code named TEDOP, which can calculate unsteady diffusion with non-constant release rate from the result of HYPACT that assumes constant release rate, by using a traveling time of each particle.
Non-constant release rate
130 kinds of radioactive materials
3D integration by Particle model
Original Radiaiton code (TEDOP)

37. MEASURES Environmental Dose Projection System (EDPS)
Flow simulation
Radiation simulation
We developed 4 kinds of new technique shown here; one is a simulation technique of building with RAMS code. Second is an improvement of turbulence model by full 3D equation of k/l model. Third is a development of emergency response system of nuclear accident in Japan. The last one is a high-speed computing system by using PC cluster of 128 CPU.
3-D radioactive cloud
animation

38. RAMS Very-High Resolution Summary
We have implemented the ADAP coordinate as an alternative to the z coordinate in RAMS.
Still has disadvantage of ETA-type coordinate in situations where high vertical resolution along a slope is desired. RAMS vertically-nested grids can help.
Simulations show good qualitative agreement with wind tunnel measurements

39. RAMS Very-high Resolution Summary
Things to do:
Momentum/heat fluxes on vertical walls
Implement other physical processes, e.g. 3-D radiation effects (overhangs, shading)
Further investigation of differencing at convex corners
Further investigation of TKE communication issues across nested grid boundaries. Probably not as serious as between RANS and LES resolution grids.
Quantitative validation