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Development and applications ofbenchmarking aerosol models on
the
regional scale using a stochasticparticle-resolved approach
Jeffrey H. Curtis, Nicole Riemer and Matthew West
University of Illinois at Urbana-Champaign
International Aerosol Modeling Algorithms ConferenceDecember 5,
2019
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 1/15
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Atmospheric modeling: A multiscale challenge
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0.01 0.10 1.00
d in mmp
ifjfic
sjc
total
µm
n(log d p)
1
Global scale
Regional scale
Mesoscale
Microscale
Par7cle scale
Molecular scale
Li#et#al.,#Atmospheric#Environment,#45,#248892495,#2011#
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 2/15
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Atmospheric modeling: A multiscale challenge
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0.01 0.10 1.00
d in mmp
ifjfic
sjc
total
µm
n(log d p)
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Global scale
Regional scale
Mesoscale
Microscale
Par7cle scale
Molecular scale
Li#et#al.,#Atmospheric#Environment,#45,#248892495,#2011#
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d in mmp
ifjfic
sjc
total
µm
n(log d p)
1
Global scale
Regional scale
Mesoscale
Microscale
Par7cle scale
Molecular scale
Li#et#al.,#Atmospheric#Environment,#45,#248892495,#2011#
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 2/15
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How do models represent aerosol composition?
amount
amount
radiusradius
Modalradiusradius
Sectional
I Simplifying assumptions regarding the aerosol compositionI
Sectional model: aerosols in a bin are fully internally mixed.I
Modal model: aerosols in a mode are fully internally mixed.
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 3/15
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How do models represent aerosol composition?
amount
amount
radiusradius
Modalradiusradius
Sectional
I Simplifying assumptions regarding the aerosol compositionI
Sectional model: aerosols in a bin are fully internally mixed.I
Modal model: aerosols in a mode are fully internally mixed.
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 3/15
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Alternative representation: Particle-resolved
Particle-resolved
I Use a discrete representation of particlesI Representation of
processes are straight-forward to modelI No bins or modesI No
assumption made regarding how particles are mixed
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 4/15
N. Riemer, M. West, R. A. Zaveri, and R. C. Easter, Journal of
Geophysical Research, 2009
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Model verification of aerosol representation
We need approximations at the regional and global scales.But
approximations cause error and uncertainties.
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n(logd)p
0.01 0.10 1.00
d in mmp
ifjfic
sjc
total
µm
n(log d p)
1
Global scale
Regional scale
Mesoscale
Microscale
Par7cle scale
Molecular scale
amount
amount
radiusradius radiusradius Howwell is this complexity
captured?
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 5/15
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Particle-resolved modeling technique
What is composition space? Each particle is uniquely represented
as anA-dimensional vector with mass composition components {µi1,
µi2, . . . , µiA}
Particle 1
Particle 2
Particle 3
BC 3 10 1 SO4 12 3 4 OC 5 8 2
BC
SO4
OC
Sulfate (SO4)
Black carbon (BC)
Organic carbon (OC)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 6/15
N. Riemer, M. West, R. A. Zaveri, and R. C. Easter, Journal of
Geophysical Research, 2009
-
Particle-resolved modeling technique
What is composition space? Each particle is uniquely represented
as anA-dimensional vector with mass composition components {µi1,
µi2, . . . , µiA}
Particle 1
Particle 2
Particle 3
BC 3 10 1 SO4 12 3 4 OC 5 8 2
BC
SO4
OC
Sulfate (SO4)
Black carbon (BC)
Organic carbon (OC)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 6/15
N. Riemer, M. West, R. A. Zaveri, and R. C. Easter, Journal of
Geophysical Research, 2009
-
Particle-resolved modeling technique
What is composition space? Each particle is uniquely represented
as anA-dimensional vector with mass composition components {µi1,
µi2, . . . , µiA}
Particle 1
Particle 2
Particle 3
BC 3 10 1 SO4 12 3 4 OC 5 8 2
BC
SO4
OC
Sulfate (SO4)
Black carbon (BC)
Organic carbon (OC)
Composition space A ≈ 20
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 6/15
N. Riemer, M. West, R. A. Zaveri, and R. C. Easter, Journal of
Geophysical Research, 2009
-
Benefits of particle-resolved models
I No approximation need for representing mixing stateI Coarse
graining tool: deriving parameters for more approximate modelsI
Benchmark and error quantification for more approximate modelsI
Detailed studies on the particle scale and experimental
intercomparison.
I Scales efficiently for high-dimensional data (number of
aerosol species)I Avoids curse of dimensionality
I Efficient algorithms make particle-resolved modeling feasibleI
Accelerated binned coagulation (Riemer et al. 2009, Michelotti et
al. 2013)I Particle weighting methods to reduce statistical error
(DeVille et al. 2011, 2019)I Accelerated particle removal
algorithms (Curtis et al. 2016)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 7/15
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Benchmarking approximate models
Simulation inputs and processes should be as similar as
possible
I Same meteorological model
I Same chemical mechanisms
I Consistency in emissions
I Identical particle removal processes
I Identical transport algorithms
Only change the aerosol microphysics
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 8/15
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Particle-resolved modeling on the regional scale
I PartMC coupled with WRF allows regionalsimulations with
highly-detailed mixing state.
I Each grid cell simulates 10 000 computationalparticles -
billions of particles for the domain.
I Many levels of detail from the large-scale topopulation level
to single-particle details ofcomposition and emission source.
I Computational expense: 300 000 core hours for2 day simulation
from the domain to right
106 107 108 109 1010 1011
number concentration # / m3
pt natgasonroad hddiesel
nonroad gas4strk
np other pt other
nonroad diesel
10�9 10�8 10�7 10�6
dry diameter (m)
0.0
0.5
1.0
OC
mas
sfr
action
101 102 103 104 105
number concentration #/cm3
SO4
NO 3
OIN O
C BC
0
1
2
3
mas
s(k
g)
⇥10�19
aircraftegu natgas
nonroad diesel
nonroad gas2strk
nonroad gas4strknp natgas
np other
onroad ldgaspt natgas pt other
Population level
Single-particle level
Source contributions in grid cell
Source contributions in single particle
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 9/15
Curtis, Riemer and West, Geoscientific Model Development,
2017
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How do we move vectors of particle composition?
Transport PDE→ Discretize in space, time, and particles→
Determine probabilities→ Sample particle sets
qt+1i,j − qti,j = ∆tF ti+1/2,j
−F ti−1/2,j
∆x + ∆tF ti,j+1/2
−F ti,j−1/2
∆y
(a) (b) (c)
Replicates deterministic finite volume method to isolate
importance of representation
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 10/15
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Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
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Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
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Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
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Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
-
Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
-
Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
-
Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
-
Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
-
Simulating stochastic aerosol transport
Testcase: 1D constant positive u advection (third order)
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 11/15
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Results: Simulating stochastic aerosol transport
0.0 0.5 1.0x
0.00
0.25
0.50
0.75
1.00
1.25
q
spatial order = 1
0.0 0.5 1.0x
spatial order = 2
0.0 0.5 1.0x
spatial order = 3
0.0 0.5 1.0x
spatial order = 4
0.0 0.5 1.0x
spatial order = 5
0.0 0.5 1.0x
spatial order = 6
100 101
Frequency (1/m)
103
Pow
er
1st
2nd
3rd
4th
5th
6th
Odd orders perform better (implicit diffusion)
102 105 108 1011 1014
Number of computational particles npart
10−8
10−6
10−4
10−2
100
Mea
nre
lati
veer
rorē
Converges to FV method in particle number
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 12/15
Curtis, Riemer and West, Geoscientific Model Development (in
prep)
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Transport performance in real-world case
Complex terrain, complex and evolving wind field
t = 0 hr t = 6 hr t = 12 hr
0 10 20 30 40
Wind speed m s−110−1 101
q
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 13/15
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Transport performance in real-world case
Stochastic algorithm applied to third order monotonic advection
scheme in WRF
Npart = 10 Npart = 100 Npart = 1000 FV
10−1 100 101 102
q
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 13/15
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Transport performance in real-world case
Stochastic algorithm applied to third order monotonic advection
scheme in WRF
Npart = 10 Npart = 100 Npart = 1000 FV
10−1 100 101 102
q
101 102 103
Number of particles Npart
10−2
10−1
RM
SE
Par
t-F
V
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 13/15
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First step: CCN error quantification for a sectional
projection
0 20 40 60 80 100
Mixing state χccn (%)
−100
0
100
200
Per
cent
erro
rin
CC
Nco
nce
ntr
ati
on
(%)
100
101
102
103
104
105
grid
cell
cou
nt
0 2
0
1Externallymixed
0 2
0
1 Internallymixed
0 2
0
1
Underestimated
Overestimated
Complex mixing stateresults in larger error
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 14/15
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Concluding thoughts
Future work: Model benchmarking
Use particle-resolved modeling andmixing state metrics to
benchmarkaerosol models that use varying levelsof mixing state
amount
amount
radiusradius
Modalradiusradius
Sectional
Code availability
https://github.com/compdyn/partmc
Funding
DE-SC0011771DE-SC0019192
Curtis (University of Illinois) Benchmarking aerosol models on
the regional scale December 5, 2019 15/15
https://github.com/compdyn/partmc
TransportExampleConclusion