The Ocean-Land-Atmosphere Model (OLAM)
Robert L. Walko
Roni Avissar
Rosenstiel School of Marine and Atmospheric Science
University of Miami, Miami, FL
Martin Otte
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
David Medvigy
Department of Geosciences and Program in Atmospheric and
Oceanic Sciences, Princeton University, Princeton, NJ
Motivation for OLAM originated in our work with the
Regional Atmospheric Modeling System (RAMS)
RAMS, begun in 1986, is a limited-area model similar to WRF and MM5
Features include 2-way interactive grid nesting, microphysics and other physics
parameterizations designed for mesoscale & microscale simulations
But, there are significant disadvantages to limited-area models
External GCM domain
RAMS domain
Numerical noise at
lateral boundary
Information
flow
OLAM global lower resolution domain
OLAM local high
resolution region
Information
flow
Well behaved transition region
So, OLAM was originally planned as a global version of RAMS.
OLAM began with all of RAMS’ physics parameterizations in place.
Global RAMS 1997: “Chimera Grid” approach
Lateral boundary values interpolated from interior of opposite grid
Not flux conservative
OLAM dynamic core is a complete replacement from RAMS
Based on icosahedral grid
Seamless local mesh refinement
OLAM: Relationship between triangular and hexagonal cells
(either choice uses Arakawa-C grid stagger)
OLAM:
Hexagonal grid
cells
Downscaling Regional Climate
Model Simulations to the
Spatial Scale of the Observations
Terrain-following coordinates
used in most models
OLAM uses cut cell method
One reason to avoid terrain-following grids:
Error in horizontal gradient computation (especially for pressure)
P
P
V
V
P
P
Another reason:
Anomalous vertical dispersion
Wind
Thin cloud layer
Terrain-following coordinate levels
Terrain
Continuous equations in conservation form

 

 

Vi
   viV  p  i  2    v i   g i  Fi
t
Momentum conservation
(component i)


   V  M
t
Total mass conservation

 (  )
   V  H
t

 
p   d Rd   v Rv  
CP
CV
 1 
 
 p0 

(  s)
   s V  Q
t
 
conservation
Rd
CV
Equation of State
Scalar conservation
(e.g. sv  v /  )
  d  v  c
Total density


V v
Momentum density


qlat
   1 

C
max(
T
,
253
)


p
 = potential temperature
 = ice-liquid potential temperature
Finite-volume formulation:
Integrate over finite volumes and
apply Gauss Divergence Theorem



    d      d



d

Discretized equations:
 

 



p
Vi d     viV  d  
d    2   v

t
xi



 d     V  d   Fm d 
t 
 





d




V

d

  F d 


t
 




s
d



s
V

d

  Fs d 


t

i
d     gi d    Fi d 
Conservation equations in discretized finite-volume form
(SGS = “subgrid-scale eddy correlation”)


 
Vi
p
1
    vi j V j  SGS{vi j ,V j }  j 
 2   v i   gi  Fi
t
xi
j


cell volume

  1  V j j 
t
j
cell face area

d
 
 
 1  jV j  SGS{ j ,V j } j  H
t
j
 
 s
  1  s j V j  SGS{s j ,V j } j   Q
t
j
p  sd Rd  sv Rv    
CP
CV
 1
 
 p0 
Rd
CV

Discretized momentum
density is consistent between
all conservation equations
Grid cells A and B have reduced volume and surface area
Fully-underground cells have zero surface area
A
B
Land cells are defined such that each one interacts with
only a single atmospheric level
Land grid cells
Cut cells vs. terrain-following coordinates
High vertical resolution near ground
Direction of atmospheric isolines
C-staggered momentum advection method of Perot (JCP 2002)
3D wind vector
diagnosed
Normal wind
prognosed
Neighbors of W point on hexagonal mesh
itab_w(iw)%im(1:7)
itab_w(iw)%iv(1:7)
itab_w(iw)%iw(1:7)
iw5
iw4
iv5
im4
iw6
im5
iv4
iv6
im3
im6
iw
im2
iv7
iw7
iv3
im7
im1
iv2
iv1
iw1
iw2
iw3
Neighbors of V point on hexagonal mesh
iw4
iv10
itab_v(iv)%im(1:6)
itab_v(iv)%iv(1:16)
itab_v(iv)%iw(1:4)
iv11
im6
im5
iv12
iv9
iv3
iv4
im2
iv15
iv16
iv
iw1
iw2
iu
iv14
im1
iv13
iv5
iv2
iv1
iv8
im4
im3
iv6
iw3
iv7
Neighbors of M point on hexagonal mesh
iw2
iv3
iw1
im
iv2
iv1
iw3
itab_m(im)%iv(1:3)
itab_m(im)%iw(1:3)
RAMS/OLAM Bulk Microphysics
Parameterization
• Physics based scheme – emphasizes individual
microphysical processes rather than the statistical
end result of atmospheric systems
• Intended to apply universally to any atmospheric
system (e.g., convective or stratiform clouds,
tropical or arctic clouds, etc.)
• Represents microphysical processes that are
considered most important for most modeling
applications
• Designed to be computationally efficient
Physical Processes Represented
•
•
•
•
•
•
•
•
•
•
Cloud droplet nucleation
Ice nucleation
Vapor diffusional growth
Evaporation/sublimation
Heat diffusion
Freezing/melting
Shedding
Sedimentation
Collisions between hydrometeors
Secondary ice production
Hydrometeor Types
C
1. Cloud droplets
D
2. Drizzle
R
3. Rain
P
4. Pristine ice (crystals)
S
5. Snow
A
6. Aggregates
G
7. Graupel
H
8. Hail
Stochastic Collection Equation
drx N tx N tyF

dt
4a


0
0
f f
mDxDx  Dy  Vtx Dx   Vty D y 
2
f mgx Dx  f mgy D y E  x, y dDx dDy
Table Lookup Form of Collection Equation
rx 
N tx N tyF E  x, y t
4a
J  x, y , Dnx , Dny 
A
wca
V
fluxes
water
rvc
C
wvc
hvc
wvs
V
wgc hgc rgv
G2
wgvc1
G1
hgg wgg
LANDCELL 1
rav
rsv
has
hca
C
hvs
wsc
hsc
rsa
S2
wss
rga
wgvc2
was
wca
wvc
LEAF–4
sensible
heat
hav
hca
hvc
longwave
radiation
wav
wgs
wgvc2
wgg wgvc1
S1
hss
G2
hgs
G1
hgg
LANDCELL 2
Water flux between soil layers
Fwgg
 y  z 
  wh
z
Hydraulic conductivity(m/s)
h 
h   s  
h s 
2b 3
Soil water potential (m)
b
h s 
y y s   ;
h 
Ks = saturation hydraulic conductivity
ys = saturation water potential
w = density of water
[h / hs ] = soil moisture fraction
b = 4.05, 5.39, 11.4 for sand, loam, clay
ys  0
How should models represent convection
at different grid resolutions?
Conventional thinking is to resolve convection where
possible and to parameterize it otherwise.
Deep
Convection
Shallow
Convection
resolve
resolve
0.1
?
?
parameterize
parameterize
1
10
Horizontal grid spacing (km)
100