the ocean-land-atmosphere model (olam) robert l. walko roni avissar rosenstiel school of marine and...
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The Ocean-Land-Atmosphere Model (OLAM)
Robert L. Walko
Roni Avissar
Rosenstiel School of Marine and Atmospheric ScienceUniversity of Miami, Miami, FL
Martin Otte
U.S. Environmental Protection AgencyResearch 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 Informationflow
Numerical noise at lateral boundary
OLAM global lower resolution domain
OLAM local highresolution region
Well behaved transition region
Informationflow
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” approachLateral 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 coordinatesused in most models
OLAM uses cut cell method
One reason to avoid terrain-following grids:Error in horizontal gradient computation (especially for pressure)
P
V
P
P
PV
Wind
Terrain-following coordinate levels
Terrain
Another reason:Anomalous vertical dispersion
Thin cloud layer
iiiiii FgvpVvt
V
2
HVt
)(
QVst
s
)(
V
d
V
P C
R
C
C
vvdd pRRp
0
1
Continuous equations in conservation form
vV
Momentum conservation(component i)
Total mass conservation
conservation
Scalar conservation(e.g. )
Equation of State
Momentum density
)253,max(1
TC
q
p
lat = potential temperature = ice-liquid potential temperature
cvd Total density
/vvs
MVt
dd
dFdVdt m
dFdVdt
dFdVsdst s
.
Discretized equations:
Finite-volume formulation:Integrate over finite volumes andapply Gauss Divergence Theorem
dFdgdvdx
pdVvdV
t iiii
ii 2
d
iiiij
jjjijjii Fgv
x
pVvSGSVv
t
V
2},{1
jjjV
t 1
HVSGSVt j
jjjjj
},{1
QVsSGSVst
s
jjjjjj
},{1
Conservation equations in discretized finite-volume form
V
d
V
P C
R
C
C
vvdd pRsRsp
0
1
cell face area
cell volume
d
(SGS = “subgrid-scale eddy correlation”)
Discretized momentum density is consistent between all conservation equations
Grid cells A and B have reduced volume and surface areaFully-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 coordinatesHigh vertical resolution near groundDirection of atmospheric isolines
C-staggered momentum advection method of Perot (JCP 2002)
3D wind vectordiagnosed
Normal wind prognosed
iw
iw1
iw6
iw5
iw4
iw3
iw2
iw7
iv1
iv7
iv6
iv5
iv4
iv3
iv2im7im1
im2
im3
im4im5
im6
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)
iv
iu
im1
im2
im3
im5 im6
im4
iv1
iv3iv9
iv15
iv13
iv5
iv12
iv16
iv14
iv8
iv4
iv2
iv11iv10
iv7iv6
iw1iw2
iw3
iw4
Neighbors of V point on hexagonal meshitab_v(iv)%im(1:6) itab_v(iv)%iv(1:16)itab_v(iv)%iw(1:4)
im
iw1iw2
iw3
iv3
iv1iv2
Neighbors of M point on hexagonal mesh 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
1. Cloud droplets
2. Drizzle
3. Rain
4. Pristine ice (crystals)
5. Snow
6. Aggregates
7. Graupel
8. HailH
G
S
A
C
P
R
D
Stochastic Collection Equation
yxymgyxmgx
ytyxtxa
tytx
t
x
dDdDyxEDfDf
DVDVDyDxDxmFNN
d
dr ff
,
42
00
Table Lookup Form of Collection Equation
nynxa
tytxx DDyxJ
tyxEFNNr ,,,
4
,
LANDCELL 1 LANDCELL 2
LEAF–4
fluxes
wgg
wca hca
rvchvc wvc wvc hvc
wgvc1
A wav
C
VV
G2
G1
hav
ravC
G2
S2
S1
rsahscwsc
hca
haswas
wca
wss
wgs
wgg hgg
hgs
hss
rsv
hvswvs
wgvc2hgcwgcrga
hgg
rgv wgvc2
wgvc1G1
longwave radiation
sensible heat
water
Ks = saturation hydraulic conductivity
ys = saturation water potential
rw = density of water
[h / hs ] = soil moisture fraction
b = 4.05, 5.39, 11.4 for sand, loam, clay
z
zF wwgg
32
b
ss
0;
s
b
ss
Water flux between soil layers
Hydraulic conductivity(m/s) Soil water potential (m)
How should models represent convection at different grid resolutions?
Conventional thinking is to resolve convection wherepossible and to parameterize it otherwise.
0.1 1 10 100 Horizontal grid spacing (km)
Deep Convection
ShallowConvection
resolve
resolve
parameterize
parameterize
?
?
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