modeling and computational issues for air/water quality problems g. giunta, r. montella, a. murli,...
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Modeling and computational issues for air/water quality
problems
G. Giunta, R. Montella,A. Murli, A. Riccio
Dept. of Applied Sciences , University of Naples “Parthenope”
Dept. of Mathematics, University of Naples “Federico II”
ICAR-CNR
Past experiencesPast experiences• Coupling of a meteorological model (MM5v3), an Coupling of a meteorological model (MM5v3), an
emission model (STdEM) and an air quality model emission model (STdEM) and an air quality model (PNAM or CAMx) to simulate the transport and (PNAM or CAMx) to simulate the transport and chemical reactivity of atmospheric pollutants in urban chemical reactivity of atmospheric pollutants in urban areas from anthropogenic and natural sourcesareas from anthropogenic and natural sources
• Use of an oceanographic circulation model (POM) to Use of an oceanographic circulation model (POM) to study the circulation patterns and biogeochemical study the circulation patterns and biogeochemical processes in the Gulf of Naples, and mixed layer processes in the Gulf of Naples, and mixed layer temperature in the Mediterranean area.temperature in the Mediterranean area.
• Grid flow/Grid aware computational experiences in Grid flow/Grid aware computational experiences in water/air quality simulationswater/air quality simulations
– Simulation and forecasting of:Simulation and forecasting of:
• Meteorological circulation (MM5-MPP)Meteorological circulation (MM5-MPP)
• Dispersion and reactivity of atmospheric Dispersion and reactivity of atmospheric pollutants (PNAM-CAMx)pollutants (PNAM-CAMx)
• Oceanographic circulation near coastal areas Oceanographic circulation near coastal areas (POM-pPOM)(POM-pPOM)
• Wave propagation (WW3)Wave propagation (WW3)
Primary goalsPrimary goals
1.1. Forecasting and MonitoringForecasting and Monitoring
2.2. Scenario simulationScenario simulation (“What if …?” (“What if …?” analysis) analysis)
3.3. Coupling with GIS systemsCoupling with GIS systems
Expected usesExpected uses
The meteorological model (MM5v3)
• Limited-area, non hydrostatic, terrain following sigma-coordinate Limited-area, non hydrostatic, terrain following sigma-coordinate modelmodel
• Based on the integration of continuity, energy and momentum Based on the integration of continuity, energy and momentum balance equationsbalance equations
• Includes nesting capabilities, FDDA, and many kind of physical Includes nesting capabilities, FDDA, and many kind of physical parameterization (more boundary layer, radiative and atmosphere-parameterization (more boundary layer, radiative and atmosphere-surface exchange submodels)surface exchange submodels)
• Multitasking capability on shared- and distributed-memory machines Multitasking capability on shared- and distributed-memory machines
• Initially developed in the late 1970's at the PENN State University, Initially developed in the late 1970's at the PENN State University, but continuously updated at the NCAR Microscale and Mesoscale but continuously updated at the NCAR Microscale and Mesoscale DivisionDivision
• One of the most used meteorological model for real-time forecastingOne of the most used meteorological model for real-time forecastingTo know morehttp://www.mmm.ucar.edu/mm5/mm5-home.html
G. Grell, J. Dudhia and D. R. Stauffer, A Description of the Fifth-Generation Penn-State/NCAR Mesoscale Model (MM5), NCAR Technical Note, NCAR/TN-398+STR
Weather forecasting on the Mediterranean region using the MM5
http://imfa.uniparthenope.it
Weather forecasting on the Mediterranean region using the MM5
http://imfa.uniparthenope.it
z
wc
y
vc
x
uc
t
c iiii
Tendency= Advection
Turbulent diffusion+ Ri + Si + Li
Chemistry EmissionsDry/wet deposition
z
cK
zy
cK
yx
cK
xi
Vi
Hi
H
The air quality model (CAMx)
Example of ozone dynamics (CAMx)
Example of ozone dynamics (CAMx)
FuelConsumption
CORINAIR METHODOLOGY
COPERT III
Number ofVehicles
ClimateData
(temperature)
Urban emission
sNOx
CO
CO2
VOC
SO2
Rural emission
sNOx
CO
CO2
VOC
SO2
Highwaysemission NOx
CO
CO2
VOC
SO2
(age, speed, engine technology)
(mileage, mean speed)
Emission time trends
Spatial disaggregation
•Population density•Kind of land use
•Population density•Kind of land use•Main road net
• Highways net
Time disaggregation
• NOx and VOC speciation• Regridding
Urban emission
sNOx
CO
CO2
VOC
SO2
Rural emission
sNOx
CO
CO2
VOC
SO2
Highwaysemission NOx
CO
CO2
VOC
SO2
Emission factors from urban road traffic
Emission factors from rural road traffic
Spatial disaggregation
Product yieldEmissionfactors
Ei,j / year = (Ai / year) * Fei,j
Simple Methodology
Industrialemissions NOx
CO
CO2
VOC
SO2
Number of employers
NOx emissions from industrial processes
VOC emissions from industrial processes
SOx emissions from industrial processes
CO emissions from industrial processes
CO2 emissions from industrial processes
z
wc
y
vc
x
uc
t
c iiii
Tendency= Advection
Turbulent diffusion
+ Ri + Si + LiChemistry Emissions Dry deposition
z
cK
zi
V
The air quality model (PNAM)The air quality model (PNAM)
Implementation tools Implementation tools
• Fortran 90 (dynamic memory allocation,
modules, pointers, recursion,…)
• Run-time System Library (RSL), an MPI-
based parallel software package for finite
difference regular-grid problems
http://www-unix.mcs.anl.gov/~michalak/rsl/
• domain decomposition and refinement
• local address space computation
• local/global index translation
• specialized communication for updating ghost regions
• inter- and intra-domain communication for nesting
• dynamic remapping for load balancing
RSL provides facilities for RSL provides facilities for
in each domain combine simple tasks in a coarse task subdomains not necessarily of the same
shape subdomains not necessarily of regular
shape
map coarse tasks to processors
Domain decomposition
one-way nesting
mother domainforcing
nested domains
forcing = provide Dirichlet boundary conditions on the child domain
recursive descent
parallelism at domain level, iteration over domains
recursive descent
parallelism at domain level, iteration over domains
recursive descent
parallelism at domain level, iteration over domains
recursive descent
parallelism at domain level, iteration over domains
Workload per task: CPU time each vertical grid
column
the stiffness of the diffusion + chemical kinetics ODE systems
varies in time and space
variable time step feature of the ODE solver causes different workloads in
vertical columns
load imbalance = inefficiencyG. Barone, P. D'Ambra, D. di Serafino, G. Giunta, A. Riccio, A. Murli, “PNAM: Parallel software for air
quality simulations in Naples area”, J. Environ. Health and Manag., 1999, 10, 209-215
G. Barone, P. D'Ambra, D. di Serafino, G. Giunta, A. Riccio, A. Murli, “Application of a parallel Air Quality model to the Campania region”, Environ. Modelling and Software”, 2000, 15, 503-511
dynamic load balancing strategy
run-time agglomeration and remapping of simple tasks
For each domain
do assess processors’ work loads
determine the average workload
greedy algorithm for local work flow scheduling
done
Dynamic load balancing: remapping of tasks over processors (4x4 processors mesh,24 h
simulation)
cpu time (vertical diffusion + chemistry) per processor
16 processors24 h simulation
24 h simulationone nest
efficiency
speedup
beowulf 16 procs
IBM SP 16 procs
24 h simulationone nest
efficiency
speedup
The Princeton Ocean Model (POM)
• Sigma coordinate, free surface, primitive equation ocean model, which includes a turbulence sub-model (Mellor-Yamada level 2.5).
• Developed in the late 1970's by Blumberg and Mellor, with subsequent contributions from other people. (Blumberger and Mellor 1987)
• Used for modeling of estuaries, coastal regions and open oceans
• The most used ocean model in the coastal applications.
To know morehttp://www.aos.princeton.edu/WWWPUBLIC/htdocs.pom/
Blumberg, A. F. and G. L. Mellor, A description of a three-dimensional coastal ocean circulation model. Three-Dimensional Coastal ocean Models, edited by N. Heaps, 208 pp., American Geophysical Union., 1987
POM’s applications• Used in many hindcast/nowcast/forecast systems projects……
http://splash.princeton.edu/WWWPUBLIC/PROFS/ http://chartmaker.ncd.noaa.gov/csdl/op/
http://www.jamstec.go.jp/frsgc/jcope/
http://www.mar.dfo-mpo.gc.ca/
http://superior.eng.ohio-state.edu
• … and in many process studies.
Example of applications• Gulf of Naples climatology
• A marine ecological study for the Campania Region
References
Mariani P., Esposito S., Ribera M., Interaction between seasonal cycle and terrestrial runoff in determining the time course of plankton biomass in a coastal embayment: a numerical study. Chemistry and Ecology. Submitted
Example of applications
• The study of the mixed layer depth in the Mediterranean
Example of applications
POMpn validation
SEAMOUNT test
east west z = 4500 m h = 450 m 21 σ vertical levels stretched horiz. grid ρ (z) V0 = 0.2 m/s
Simulation timeSimulation time
8,42 h
4,36 h
2 domains vs 1 domain
1,14 h
POMpn performance
POMpn performance
EfficiencyEfficiency E(p) =T(1)
T(p)
p
Efficiency loss < 10%
Speed-upSpeed-up
0
1
2
3
4
5
6
7
8
9
0 2 4 6 8 10
numero di processori
spee
d-u
p
Speed-up misurato Speed-up ideale
S(p) =T(1)
T(p)
POMpn performance
Thinking in… grid
Computational resource sharing;
Distributed storage;
Security infrastructure;
Complex problem solving environment.
Ubiquitous (high performance) computing
The Application Workflow
Offline model coupling
Weather driven simulations
Operational/On Demand modes
User Request
Initial and boundary conditiondata file collecting
MM5 weather simulation
Other Environmental Model
Visualization Tools
NOAA
ECMWF
POM ocean circulation model PNAM air quality model
ScheduledRequest
Cache trigger
Full modular framework
High performance computing tools
Some trivial parallel process
Our grid approachComputational unit decomposition:
MM5 Modules POM Pre & Post Processing STdEM / PNAM coupling
Parallel process takes care of:
Nested domain pre-processing Loosely coupled models
Optimize inter-process communications:
Limiting time and data dependences
Optimize data exchanging
Only node/node data transfers No buffer node usage
MM5 module decomposition
Grid middleware wraps around each model and model component
both master managed job factory service (mmjfs)
and ad hoc grid service components
Grid parallelism achieved in nested domains preprocessing
The MM5 job consumes other jobs produced data
TERRAIN
REGRIDdomain 1
INTERPFdomain 1
Mm5
INTERPFdomain 2
INTERPFdomain 3
INTERPFdomain 4
REGRIDdomain 2
REGRIDdomain 3
REGRIDdomain 4
The Application Job flow (1/2)
The MM5 job produces data for dependent jobs
No coupling between MM5 coupled models
Grid parallelism achieved by weather driven model concurrent runs
Asynchronous results publishing
Synchronized metadata update to ensure stored data coherence
Mm5
INTERPPOM
PUBLPOM
PUBLPNAM
PUBLMm5
UPDATEdb-metadata
POM
STdEM
PNAM
The Application Job flow (2/2)
Grid EnablingEnvironmental Models
• Environmental models as computational resources
• Developing a wrap around the resource using a high level programming language;
• Developing a grid wrap using a middleware grid toolkit as Globus GT3;
• Exposing common interface through the job submitting system or an ad hoc developed grid service.
HardwareOperating System
ResourcesJVMGlobus ToolkitGrid Services
interfaces
Sta
ndar
d
Cus
tom
grid
wra
p
Developing the Grid Application
Remote resource virtualization with grid service consumer;
The grid application as glue between remote services and the local machine;
Nothing than application setup could be executed on the user interface (thin client possibility).
HardwareOperating System
Java Virtual MachineGlobus Toolkit
Grid Application
Grid ServiceConsumer
interfaces
HardwareOperating System
Java Virtual MachineGlobus Toolkit
Grid Application
Grid ServiceConsumer
interfaces
the Internet
grid middleware
HardwareOperating System
ResourcesJVMGlobus ToolkitGrid Services
interfaces
Sta
nda
rd
Cu
sto
m
grid
wra
p
HardwareOperating System
ResourcesJVMGlobus ToolkitGrid Services
interfaces
Sta
nda
rd
Cus
tom
grid
wra
p
HardwareOperating System
ResourcesJVMGlobus ToolkitGrid Services
interfaces
Sta
nda
rd
Cus
tom
grid
wra
p
HardwareOperating System
ResourcesJVMGlobus ToolkitGrid Services
interfaces
Sta
ndar
d
Cus
tom
grid
wra
p
MesoscaleModel 5v3
PrincetonOcean Model
Parallel NaplesAirquality Model
S-T DistributionEmission Model
MM 5 POM PNAM STdEM
Grid Application Building Blocks
Up and running
http://imfa.uniparthenope.it
Operational Web Portal 1/3
Weather Forecast
Surface sea current forecast
Operational Web Portal 2/3
Interactive time series graph
Operational Web Portal 3/3
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