AQAST Tiger Team Project*:
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AQAST Tiger Team Project*: Chemical data assimilation tested for national air quality forecasting and SIP modeling. Pius Lee 1 , Ted Russell 2 , Yongtao Hu 2 , Tianfeng Chai 1 and Talat Odman 2 1 Air Resources Laboratory Headquarters (ARL) Office of Oceanic and Atmospheric Research (OAR)

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AQAST Tiger Team Project*:

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Aqast tiger team project

AQAST Tiger Team Project*:

Chemical data assimilation tested for national air quality forecasting and SIP modeling

Pius Lee1, Ted Russell2, Yongtao Hu2, Tianfeng Chai1 and Talat Odman2

1 Air Resources Laboratory Headquarters (ARL)

Office of Oceanic and Atmospheric Research (OAR)

National Oceanic & Atmospheric Administration (NOAA)

2 Environmental Engineeing

Georgia Institute of Technology

*Management contacts: Ivanka Stajner, NWS; Local Environmental Agencies


2 objectives a to improve aq forecasting b provide ic and or bc for sip modeling

2 Objectives: (A) To improve AQ forecasting (B) Provide IC and/or BC for SIP modeling

BW

SJV

HOU


Moderate resolution imaging spectroradiometer modis

Moderate Resolution Imaging Spectroradiometer (MODIS)

http://terra.nasa.gov/About/


Data assimilation methods

Data Assimilation Methods

  • Optimal interpolation (OI)

    • Easy to apply, computationally efficient

  • 3D-Var

    • Adjusts all variables in the whole domain simultaneously. Currently, GSI is being developed at NOAA/NASA/NCAR

  • 4D-Var

    • Provides more flexibility, requires adjoint model

  • Kalman Filter

Sandu and Chai, Atmosphere 2011


Optimal interpolation oi

Optimal Interpolation (OI)

  • OI is a sequential data assimilation method. At each time step, we solve an analysis problem

  • We assume observations far away (beyond background error correlation length scale) have no effect in the analysis

  • In the current study, the data injection takes place at 1700Z daily

Chai et al. JGR 2006


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Objective (A): Improve PM forecast

Methodology of OI: Take account for

background input; Obs; and

physical processes from model

Observation Input

OI

Background Input

Analysis output


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Use AOD Analysis/Background as Scaling Factors

CMAQ 471: 49 Adjusted species

  • ASO4I, ANO3I, ANH4I, AORGPAI, AECI, ACLI (6)

  • ASO4J, ANO3J, ANH4J, AORGPAJ, AECJ, ANAJ, ACLJ, A25J (8)

  • AORGAT: AXYL1J, AXYL2J, AXYL3J, ATOL1J, ATOL2J, ATOL3J, ABNZ1J, ABNZ2J, ABNZ3J, AALKJ, AOLGAJ (11)

  • AORGBT: AISO1J, AISO2J, AISO3J, ATRP1J, ATRP2J, ASQTJ, AOLGBJ (7)

  • AORGCT: AORGCJ (1)

  • ASO4K, ANO3K, ANH4K, ANAK, ACLK, ACORS, ASOIL (7)

  • NUMATKN, NUMACC, NUMCOR (3)

  • SRFATKN, SRFACC, SRFCOR (3)

  • AH2OJ, AH2OI, AH2OK (3)

Tong et al. ACP 2012

for CMAQ5.0 dust module


Model modis aod on 7 4 11

Model & MODIS AOD on 7/4/11

Base: R=0.25

Y=0.21+0.09 X

OI: R=0.34

Y=0.19+0.15 X


7 4 11

HMS fire detect 7/4

MODIS AOD 17 UTC 7/3

7/4/11

AOD 17 UTC 7/4 after OI

OI

Base Case AOD 17 UTC 7/4

MODIS AOD 17UTC 7/4

OI minus Base Case


7 5 11

MODIS AOD 17 UTC 7/4

7/5/11

AOD 17 UTC 7/5 after OI

OI

Base Case AOD 17 UTC 7/5

MODIS AOD 17UTC 7/5

OI minus Base Case


7 6 11

MODIS AOD 17 UTC 7/5

7/6/11

AOD 17 UTC 7/6 after OI

OI

Base Case AOD 17 UTC 7/6

MODIS AOD 17UTC 7/6

OI minus Base Case


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Most postive impact for SE


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MODIS AOD and AIRNow PM2.5 Correlation

Chai et al. JGR 2006

http://www.star.nesdis.noaa.gov/smcd/spb/aq/


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Objective (B): Provide D.A. dynamic BC for SIP modeling

  • WRF 3.2.1 for meteorological fields

    • NCEP North American Regional Reanalysis (NARR) 32-km resolution inputs

    • NCEP ADP surface and soundings observational data

    • MODIS landuse data for most recent land cover status

    • 3-D and surface nudging, Noah land-surface model

  • SMOKE 2.6 for CMAQ ready gridded emissions

    • NEI inventory projected to 2011 using EGAS growth and existing control strategies

    • BEIS3 biogenic emissions based on BELD3 database

    • GOES biomass burning emissions: ftp://satepsanone.nesdis.noaa.gov/EPA/GBBEP/

  • CMAQ 4.6 revised to simulate gaseous & PM species

    • SAPRC99 mechanism, AERO4, ISORROPIA thermodynamic, Mass conservation,

    • Updated SOA module (Baek et. al. JGR 2011) for multi-generational oxidation of semi-volatile organic carbons


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Tests with data assimilated IC/BC

Simulate the period of 12Z on July 1, 2011 through 12Z on July 12, 2011 for testing assimilated PM fields as IC/BC. The tests are conducted on the 12- and 4-km grids with IC/BC modified for the 12-km grid. IC/BC of base and fdda cases are prepared using the NOAA provided data with the following 25 model species modified from the IC/BC that the original hindcast used .

Model Species that replaced in IC/BC with NOAA data

25 model species

ASO4J ASO4I

ANH4J ANH4I

ANO3J ANO3I

AORGPAJ AORGPAI

AECJ AECI A25J

ACORS ASOIL

NUMATKN NUMACC NUMCOR

SRFATKN SRFACC

AH2OJ AH2OI

ANAJ ACLJ

ANAK ACLK ASO4K


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Surface O3 bias over 4 km BW domain; where fdda applied for BC

Surface PM25 bias


Summary and future work

Summary and future work

  • Assimilating MODIS AOD using OI method is able to improve AOD and PM2.5 predictions in selected regions. The improvement is not “yet” significant.

  • Dynamic BC from archived best chemical fields generated by this project can support SIP modeling. E.g. The SIP-type limited-domain modeling result over Baltimore-Washington presented was based on ingesting assimilated AOD through dynamic LBCs.

  • Assimilating both MODIS AOD and AIRNow PM2.5 is expected to have better results and will be tested.


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On 4km SJV real-time AQ forecast

for DISCOVER-AQ Jan-Feb 2013


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Tentative flight routes for DISCOVER-AQ Central Valley, Jan-Feb 2013


Objective provide ic and or bc for sip modeling

Objective: Provide IC and/or BC for SIP modeling

  • 110 x 240;

  • Centlon= -97.00

  • Centlat=40.0

  • Truelat1=33.00

  • Truelat2= 45.00

SJV


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NDAS Assimilates the following important variables


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Surface pressure and wind barbs; Both verified reasonably well

NMMB launcher run for CalNex period


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BACKUP SLIDES


Estimate model error statistics w hollingsworth lonnberg method

Estimate Model Error Statistics w/ Hollingsworth-Lonnberg Method

  • At each data point, calculate differences between forecasts (B) and observations (O)

  • Pair up data points, and calculate the correlation coefficients between the two time series

  • Plot the correlation as a function of the distance between the two stations,


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Horizontal Error Statistics

Rz:

~ 0.9

EB2/ Eo2:

~ 9

Correlation length:

~ 160 km


Stats for daily maximum 8 hr o 3 at all aqs sites within 4km d omain

Statistical metrics for high resolution AQ model evaluation -- New paradigm

Stats for Daily Maximum 8-hr O3 at All AQS Sites within 4km Domain

The performance measures over the 4 km resolution may not be necessarily better than over the coarser (12 km) resolution; it may be even worse if it is evaluated using the traditional evaluation metrics based on paired obs-mod data

Courtesy: Daiwen Kang, CMAS 2011

Air Resources Laboratory/NOAA and Georgia Tech for AQAST, Madison, WI, June 13 2012


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