Multiscale numerical simulation of pollution transport in near - - PowerPoint PPT Presentation

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Multiscale numerical simulation

• f pollution transport in near

surface air

Alexander Starchenko, Tomsk State University,

starch@math.tsu.ru

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NWP & AQ simulation Nowadays a broad range of problems of atmospheric physics, climate and environment protection is solved with application of mathematical modelling approach. Modelling systems, developed at large centres of atmospheric research, are applied for scenario analysis, weather prediction, air quality investigation. For example, CMAQ, Community Multiscale Air Quality Chemical Transport Modelling System; EURAD, EURopean Acid Deposition model, EZM, European Zooming Model. Dynamic core of such systems are or well-known models (e.g. MM5, WRF) either original models.

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Nesting technology

Т о м с к Т о м с к Ю р г а Ю р г а К е м е р о в о К е м е р о в о А н ж е р о - С у д ж е н с к А н ж е р о - С у д ж е н с к

Tomsk Region Tomsk City

Domain 200x200km2 Domain 50x50 km2

One-nested Two-nested

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MM5 (Mesoscale Model 5)

The PSU/NCAR mesoscale model is a limited-area, nonhydrostatic or hydrostatic, terrain-following sigma- coordinate model designed to simulate or predict mesoscale and regional-scale atmospheric circulation. It has been developed at Penn State and NCAR as a community mesoscale model. The Fifth-Generation NCAR / Penn State Mesoscale Model (MM5) includes a multiple-nest capability, nonhydrostatic dynamics, which allows the model to be used at a few-kilometer scale, multitasking capability on shared- and distributed-memory machines, a four-dimensional data-assimilation capability, more physics options.

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Mesoscale Model 5 MM5 generates meteorological fields:

• horizontal and vertical wind components,
• pressure,
• temperature,
• air humidity,
• cloudiness and precipitation parameters,
• heat, moisture and momentum fluxes,
• short-wave and long-wave radiation.

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The Weather Research and Forecast Model is a next-generation mesocale numerical weather prediction system designed to serve both operational forecasting and atmospheric research needs. It features multiple dynamical cores, a 3-dimensional variational (3DVAR) data assimilation system, and a software architecture allowing for computational parallelism and system extensibility. The WRF model is a fully compressible, nonhydrostatic model. Its vertical coordinate is a terrain-following hydrostatic pressure coordinate. Model uses the Runge-Kutta 2nd and 3rd order time integration schemes, and 2nd to 6th order advection schemes in both horizontal and vertical directions. The dynamics conserves scalar variables.

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The WRF model is designed to be a flexible, state-of-the-art atmospheric simulation system that is portable and efficient on available parallel computing platforms. WRF is suitable for use in a broad range of applications across scales ranging from meters to thousands of kilometres, including:

• Idealized simulations (e.g. LES, convection, baroclinic waves)
• Parameterization research
• Data assimilation research
• Forecast research
• Real-time NWP
• Coupled-model applications
• Teaching

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MM5 & WRF

Since the MM5&WRF modeling system are primarily designed for real-data studies/simulations, it requires the following datasets to run:

• Topography, landuse and vegetation (in categories);

(1o - 30’’ resolution)

• Gridded atmospheric data that have at least these variables:

sea-level pressure, wind, temperature, relative humidity and geopotential height; and at these pressure levels: surface, 1000, 850, 700, 500, 400, 300, 250, 200, 150, 100 mb;

• Observation data that contains soundings and surface reports

(final analysis data NCEP or ECMWF, global data NCEP)

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Simulation cases

Two temporal periods: 16-17 May 2004; 20-21 October 2004; Three local nested domains with horizontal sizes 450х450, 150х150 и 50х50km2. South of Western Siberia, Tomsk (56,5o N, 85o E) is in the centre of domains; Initial state of atmosphere and lateral boundary conditions were set up on the basis of NCEP final analysis data

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Tomsk (56,6N; 85,0E)

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Simulation conditions

D1 D2 D3

Novosibirsk Tomsk Kemerovo

D1 D2 D3

Novosibirsk Kemerovo

Three nested domains D1, D2, D3 and distribution of landuse categories

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Geographic map and land use distribution

Novosibirsk Tomsk Kemerovo Novosibirsk Kemerovo

Color table of land use categories in domain 450x450km: Color table of land use categories in domain 450x450km: blue blue-

• water,

water, violet violet-

• few vegetation,

few vegetation, yellow yellow-

• farmland,

farmland, light green light green-

• deciduous forest,

deciduous forest, brown brown-

• mixed forest,

mixed forest, green green-

• evergreen forest,

evergreen forest, red red-

• urban area

urban area

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Terrain elevation map

450km x 450km x Tomsk

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Land use categories for the research domain

Water Water Water Few vegetation Few vegetation Farmland Deciduous forest Mixed forest Evergreen forest Urban area Tomsk city 50x50 km2

• r. Tom

Tomsk

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Surface elevation above sea level and pollution emission distribution in Tomsk

• 25
• 20
• 15
• 10
• 5

5 10 15 20 25

• 25
• 20
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5 10 15 20 25 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200

˜ IAO TOR-station

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SLIDE 16

Simulation options

Grids 52х52х31 for domains D1, D2, D3 Horizontal resolution: 9; 3; 1 km for D1, D2, D3 Temporal step: 27; 9; 3 sec for D1, D2, D3 Vertical size of domains: 17km Cluster IAO SB RAS Grids 52х52х31 for domains D1, D2, D3 Horizontal resolution: 9; 3; 1 km for D1, D2, D3 Temporal step: 60; 30; 10 sec for D1, D2, D3 Vertical size of domains: 17 km Cluster IAO SB RAS

MM5 MM5 WRF WRF

SLIDE 17

Simulation options

Mixed phase microphysics by Reisner RRTM scheme for long wave radiation Similarity theory for surface layer Thermal diffusion for soil Blackadar scheme for PBL None cumulus parameterization Eta Grid-Scale Cloud and Precipitation scheme by Ferrier RRTM scheme for long wave radiation Dudhia scheme for short wave radiation Similarity theory for surface layer Thermal diffusion for soil MYJ scheme for PBL

MM5 MM5 WRF WRF

SLIDE 18

Comparison of the models

• 20-18-16-14-12-10 -8 -6 -4 -2 0

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 time, hrs 1 2 3 4 5 Wind velocity, m/s

• 20-18-16-14-12-10 -8 -6 -4 -2 0

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• 20-18-16-14-12-10 -8 -6 -4 -2 0

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 time, hrs 10 15 20 25 30 35 40 Temperature, C Legend Hydromet observations WRF model TSU-IAO model MM5 model TOR station IAO

16-17 May 2004

Time=-20…0: 16 May 2004; Time= 0…24: 17 May 2004 MM5 MM5 WRF WRF

Wind velocity and direction at 10m Air temperature at 2m in Tomsk

SLIDE 19

Wind at 10m for the domain D1

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SLIDE 20

Wind at 10m for the domain D3

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SLIDE 21

Comparison of the models

• 20-18-16-14-12-10 -8 -6 -4 -2 0

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30

time, hrs

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Wind velocity, m/s

• 20-18-16-14-12-10 -8 -6 -4 -2 0

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time, hrs

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Wind direction, deg

• 20-18-16-14-12-10 -8 -6 -4 -2 0

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Temperature, C Legend

Hydromet observations WRF model MM5 model

20-22 October 2004

Wind velocity and direction at 10m Air temperature at 2m in Tomsk

Time=-20…0: 20 October 2004; Time= 0…24: 21 October 2004 MM5 MM5 WRF WRF

SLIDE 22

Parallel realization of the models

Linux cluster IAO: 10 nodes, each with 2 processors Pentium III 1GHz and RAM 1Gb Communication net 1Gbs Ethernet, “star” topology 11Gflops on the LINPACK test

2 4 6 8 10 12 14 16 18 20

Number of processors

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Speed up

Models

Mesoscale Model of the Fifth Generation Weather Research and Forecast

2 4 6 8 10 12 14 16 18 20

Number of processors

10 20 30 40 50 60

Time of calculation, min

Temporal period of simulation 1hour

MM5 80Mb, WRF 210Mb

SLIDE 23

CAMx

The Comprehensive Air quality Model with extensions (CAMx) is an Eulerian photochemical dispersion model that allows for an integrated “one-atmosphere” assessment

• f gaseous and particulate air pollution (ozone, PM2.5,

PM10, air toxic, mercury) over many scales ranging from sub-urban to continental. CAMx simulates the emission, dispersion, chemical reaction, and removal of pollutants in the troposphere by solving the pollutant continuity equation for each chemical species on a system of nested three-dimensional grids.

Four versions of the Carbon Bond IV (CB-IV) chemical mechanism SAPRC99 mechanism

SLIDE 24

A computer modeling system TSU-IAO was created within the project Integrated System for Intelligent Regional Environmental Monitoring & Management in a city/ region (on the example of Tomsk region) of the European Community Framework 5 Program to assist in the analysis of the distribution of meteorological parameters and the concentration

• f admixtures in the atmospheric boundary layer

above a rough inhomogeneous underlying surface. The nonhydrostatic prognostic mesoscale model and the model of pollution transformation and transport make the core of this system.

SLIDE 25

Components of the MS TSU-IAO

Model initialization block Model initialization block (terrestrial data, ground-based observations, data of vertical distributions of meteorological parameters, data base of point, area and mobile sources

• f air pollution)

Nonhydrostatic Nonhydrostatic meteorological model meteorological model Pollution transport photochemical model Pollution transport photochemical model Data visualization block Data visualization block

SLIDE 26

Model initialization block

Terrestrial data: topography, land use topography, land use categories categories (albedo, soil thermal conductivity, heat capacity, density, evaporation, surface roughness, emissivity, deep soil temperature) Ground Ground-

• based and vertical observations of

based and vertical observations of wind velocity and wind direction, air wind velocity and wind direction, air temperature and humidity, atmospheric temperature and humidity, atmospheric pressure pressure

SLIDE 27

Numerical nonhydrostatic model

Terrain following (zeta) coordinate system Nonhydrostatic hydrodynamic 3D equations 3D equations of heat and humidity exchange Two-equation “k-l” turbulence model 2D equation for surface temperature Assimilation of observed data Nesting technology

SLIDE 28

Pollution transport model

Eulerian 3D equations for basic anthropogenic pollutants of near surface layer (dust, CO, SO2, NO2) Dry deposition (resistance model) Photochemical reactions of Hurley’s GRS- mechanism of troposphere ozone and PM10 generation (CSIRO) Data base of distributed point, area, mobile (linear) sources

SLIDE 29

Nesting technology

Т о м с к Т о м с к Ю р г а Ю р г а К е м е р о в о К е м е р о в о А н ж е р о - С у д ж е н с к А н ж е р о - С у д ж е н с к

Tomsk Region Tomsk City

Domain 200x200km2 Domain 50x50 km2

SLIDE 30

Comparison of the predictions and the observed data

Meteodata of the IOA TOR-station and the Hydrometeorological Center o

SLIDE 31

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June 30 2000 00:00

Reference Vectors 1 m/s 5 m/s 10 m/s

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Reference Vectors 1 m/s 5 m/s 10 m/s

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Reference Vectors 1 m/s 5 m/s 10 m/s

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20000 40000 60000 80000 100000

June 30 2000 12:00

Reference Vectors 1 m/s 5 m/s 10 m/s

• 100000
• 80000
• 60000
• 40000
• 20000

20000 40000 60000 80000 100000

• 100000
• 80000
• 60000
• 40000
• 20000

20000 40000 60000 80000 100000

June 30 2000 16:00

Reference Vectors 1 m/s 5 m/s 10 m/s

• 100000
• 80000
• 60000
• 40000
• 20000

20000 40000 60000 80000

• 100000
• 80000
• 60000
• 40000
• 20000

20000 40000 60000 80000 100000

June 30 2000 20:00

Reference Vectors 1 m/s 5 m/s 10 m/s

Comparison of MEMO and MS TSU-IOA predictions

MEMO

SLIDE 32

Prediction of pollutant concentrations in Tomsk

3 inert pollutants: CO, SO2, NO2 Point and area sources Emission rate of line sources:

Q(h)=Qave*(0.1+1.9*sin(π(h- 6)/ 18), 6<h<24 hours

Computation grid: 100x100x60

• 20
• 10

10 20

часы

0.001 0.01 0.1 1

NO2, мг/м3

• 20
• 10

10 20

часы

0.001 0.01 0.1 1

NO2,мг/м3

• 20
• 10

10 20

часы

0.001 0.01 0.1 1

NO2, мг/м3 Пост №2 Пост №5 Пост №14

10-11 J anuary 2000

SLIDE 33

Ozone concentration, observed in TOR-station IAO near Tomsk on 16 May 2004

O3, mkg/m3

SLIDE 34

Generic Reaction Set kinetic scheme

• f ozone formation (Hurley,1999)

Rsmog + hv => RP + Rsmog + ηAPM RP + NO => NO2 NO2 + hv => NO + O3 NO + O3 => NO2 RP + RP => RP + αH2O2 RP + NO2 => SGN RP + NO2 => APM RP + SO2 => APM H2O2 + SO2 => APM O3 + SO2 => APM

SLIDE 35

Air pollution in Tomsk

• 20 -18 -16 -14 -12 -10 -8 -6 -4 -2

2 4 6 8 10 12 14 16 18 20 22 24

time, hrs

40 80 120

O3,ppb

• 20 -18 -16 -14 -12 -10 -8 -6 -4 -2

2 4 6 8 10 12 14 16 18 20 22 24

time, hrs

10 20 30 40 50

NO2,ppb

• 20 -18 -16 -14 -12 -10 -8 -6 -4 -2

2 4 6 8 10 12 14 16 18 20 22 24

time, hrs

400 800 1200 1600 2000

CO,ppb Legend

TOR-station Prediction

16-17 May 2004

Time=-20…0: 16 May 2004; Time= 0…24: 17 May 2004

SLIDE 36

CAMx vs TSU-IAO MS

• 20 -16 -12 -8
• 4

4 8 12 16 20 24

Time, h

0.01 0.02 0.03 0.04 0.05 0.06

O3, ppm

• 20 -16 -12 -8
• 4

4 8 12 16 20 24

Time, h

200 400 600 800 1000 1200

Solar radiation, Wt/m2

• 20 -16 -12 -8
• 4

4 8 12 16 20 24

Time, h

200 250 300 350 400 450 500

Wind direction, deg

• 20 -16 -12 -8
• 4

4 8 12 16 20 24

Time, h

0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

NO2, ppm

• 20 -16 -12 -8
• 4

4 8 12 16 20 24

Time, h

1 2 3 4 5

Wind speed, m/s

• 20 -16 -12 -8
• 4

4 8 12 16 20

Time, h

0.1 0.2 0.3 0.4 0.5 0.6 0.7

CO, ppm

a b d e c f

• presented model with mechanism GRS,
• CAMx,
• MM5,
• TOR-station mesurements

26-27 May 2004 in Tomsk

SLIDE 37

84.94 84.96 84.98 85 85.02 85.04 85.06 56.44 56.46 56.48 56.5 56.52

84.92 84.94 84.96 84.98 85.00 85.02 85.04 85.06 56.46 56.48 56.50 56.52

50 40 40

10 20 30 40 50 60 70 80 90

IAO Measurements TSU-IAO MS Predictions Near surface concentration of nitrogen dioxide at 16:00 on 11 July 2005 in Tomsk

Результаты расчетов

SLIDE 38

Results of AQ predictions in Tomsk

84.94 84.96 84.98 85 85.02 85.04 85.06 56.46 56.48 56.5 56.52

CO NO2

11 July 2005

SLIDE 39

Acknowledgements

This research is funded by RFBR,

grants N04-07-90219, N05-05-98010r_ob

SLIDE 40

Fire in toxic waste area

• 10 January 2003 fire

was happened in the Tomsk area of toxic waste, located in the north of Tomsk.

• Conflagration

duration was from 10.00 to 24.00.

• Inhabitants of

Seversk, Svetly and Tomsk felt foxy smell.

SLIDE 41

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 14:00

Toxic waste area

Tomsk

SLIDE 42

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 15:00

Toxic waste area

Tomsk

SLIDE 43

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 16:00

Toxic waste area

Tomsk

SLIDE 44

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 18:00

Toxic waste area

Tomsk

SLIDE 45

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 19:00

Toxic waste area

Tomsk

SLIDE 46

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 20:00

Toxic waste area

Tomsk

SLIDE 47

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 22:00

Toxic waste area

Tomsk

SLIDE 48

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 10 2003 23:00

Toxic waste area

Tomsk

SLIDE 49

• 30000
• 20000
• 10000

10000 20000 30000

• 30000
• 20000
• 10000

10000 20000 30000

0.05 0.13 0.32 0.8 2 5

January 11 2003 00:00

Toxic waste area

Tomsk