slide1
Download
Skip this Video
Download Presentation
AQAST Tiger Team Project*:

Loading in 2 Seconds...

play fullscreen
1 / 26

AQAST Tiger Team Project*: - PowerPoint PPT Presentation


  • 106 Views
  • Uploaded on

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)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'AQAST Tiger Team Project*:' - mandy


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1
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

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

slide6
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

slide7
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 7/47/5/11

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 7/57/6/11

AOD 17 UTC 7/6 after OI

OI

Base Case AOD 17 UTC 7/6

MODIS AOD 17UTC 7/6

OI minus Base Case

slide13
MODIS AOD and AIRNow PM2.5 Correlation

Chai et al. JGR 2006

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

slide14
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
slide15
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

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.
slide18
On 4km SJV real-time AQ forecast

for DISCOVER-AQ 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

slide22
Surface pressure and wind barbs; Both verified reasonably well

NMMB launcher run for CalNex period

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,
slide25
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 paradigmStats 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

ad