aqast tiger team project*: chemical data assimilation...

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

Air Resources Laboratory/NOAA and Georgia Tech for AQAST, Madison, WI, June 13 2012 1

PresenterPresentation NotesCollaboration between ARL and Georgia Tech.

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

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

SJV

BW

HOU

2

PresenterPresentation NotesCo-incident of the 3 decided domains for the first 3 DISCOVER-AQ campaigns

Moderate Resolution Imaging Spectroradiometer (MODIS)

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

Orbit: 705 km, 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua), sun-synchronous, near-polar, circular

Swath Dimensions: 2330 km (cross track) by 10 km (along track at nadir)

Spatial Resolution: 250 m (bands 1-2) 500 m (bands 3-7) 1000 m (bands 8-36)


PresenterPresentation Notes550 nanometer channel

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

PresenterPresentation NotesNCEP: Hybrid GSI-EnKF data assimilation system seemed on track to be implemented in a few months

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

)()( 1 HXYOHBHBHXX TTba −++= −


Chai et al. JGR 2006

PresenterPresentation NotesOI: applied to AOD:a: Analysis; b: background state;B: background error covariance; O: Observation error; H: transformation from model extinction to AOD as Y (observed AOD).

C

R

50 100 150 200 250 300 350 400

50

100

150

200

250

MODIS_FINE: 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Observation Input

Background Input

Analysis output

OI


Methodology of OI: Take account for background input; Obs; and physical processes from model

Objective (A): Improve PM forecast

6

PresenterPresentation NotesOptimization problem solved by OI: B, Y, and H as in the formula in previous slide

Use AOD Analysis/Background as Scaling Factors

C

R

0 50 100 150 200 250 300 350 4000

50

100

150

200

250Factor: 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8


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)

CMAQ 471: 49 Adjusted species

7

Tong et al. ACP 2012 for CMAQ5.0 dust module

PresenterPresentation NotesScaled using the same ratio at each grid point, but applied same factor for all these species and all 22 vertical layers

Model & MODIS AOD on 7/4/11

MODIS AOD

AO

D_O

I

0 0.5 1 1.50

0.5

1

MODIS AOD

AO

D_B

ase

0 0.5 1 1.50

0.5

1

MODIS AOD

AO

D_M

odel

0 0.5 1 1.50

0.5

1AOD_BaseAOD_OI

Base: R=0.25 Y=0.21+0.09 X OI: R=0.34 Y=0.19+0.15 X 8

PresenterPresentation NotesRayleigh scattering due to gas molecules was not discounted as done in satellite

7/4/11

OI

MODIS AOD 17 UTC 7/3

Base Case AOD 17 UTC 7/4

AOD 17 UTC 7/4 after OI

MODIS AOD 17UTC 7/4

OI minus Base Case

HMS fire detect 7/4

9

PresenterPresentation NotesSignificant improvement but not uniformly spatially

7/5/11

OI




OI minus Base Case

MODIS AOD 17UTC 7/5

10

PresenterPresentation NotesEspecially helpful in SE

7/6/11

OI




OI minus Base Case

MODIS AOD 17UTC 7/6

11

PresenterPresentation NotesAgain SE due probably to more responsive processes involved in SE e.g rh, HCHO proxy for biomass burning as well as SOA

Upper Midwest Northeast US

Rock Mountain Region Lower Midwest

Pacific Coast Region Southeast US


Most postive impact for SE 12

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

MODIS AOD and AIRNow PM2.5 Correlation


Chai et al. JGR 2006

PresenterPresentation NotesNext step may include sfc pm25 obs in D.A.


12-km (123x138)

12-km (123x138)

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

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

14

PresenterPresentation NotesSIP modeling is initialize once initialize subsequent days with forecast result from previous day

Tests with data assimilated IC/BC

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

Model Species that replaced in IC/BC with NOAA data

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 .

15

PresenterPresentation NotesMapping from cmaq471 to cmaq46 for aerosol species


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

Surface PM25 bias

16

PresenterPresentation NotesHardly any impact for this case as Mid Atlantic is not benefit the 12km parent domain run significantly by ingesting MODIS AOD

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.



On 4km SJV real-time AQ forecast for DISCOVER-AQ Jan-Feb 2013

18


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

19

Objective: Provide IC and/or BC for SIP modeling


SJV

110 x 240; Centlon= -97.00 Centlat=40.0 Truelat1=33.00 Truelat2= 45.00

20

obs data stream Data acquisition platforms

Wind profiles Doppler radar, Satellites (QuikScat, ENVIRSAT, ADM, AVHRR,OMI,.…), TDWR

Cloud top temperature & Z

AQUA, AIRS

Precipitable water METNET GPS-based instrument, Satellites (EUMETSAT, IFLOWS..)

Snow cover MIRS Skin temperature Mesonet, MADIS, MIRS Temperature profile

TAMDAR, ACARS,

Surface emissivity MIRS,… radiance Satellites (ENVIRSAT, METEOSAT,..) PBL ACARS, Satellites (GPS-RO) O3 – column and profile

Satellites (AVHRR, AURA POES, TES, OMI)

NDAS Assimilates the following important variables


Surface pressure and wind barbs; Both verified reasonably well

NMMB launcher run for CalNex period

22


BACKUP SLIDES

23

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,


24

PresenterPresentation NotesCalculate differences between forecasts (B) and observations (O) at each AIRNOW station as a time series Pair up AIRNOW stations, and calculate the correlation coefficients between the two time series at the paired stations Plot the correlation as a function of the distance between the two stations, Intercept Rz can be used to calculate the ratio between model error (EB) and and observational error (including representative error) Horizontal length scale can be inferred as well

Horizontal Error Statistics

Separation Distance (km)

Cor

rela

tion

coef

ficie

nt

0 200 400 600 800 10000

0.2

0.4

0.6

0.8

1

Rz:

~ 0.9

EB2/ Eo2:

~ 9

Correlation length:

~ 160 km


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

Metrics Paired (4km) Paired

(12km)

RMSE (ppb) 15.65 14.94

NME (%) 25.36 24.29

MB (ppb) 1.86 2.43

NMB (%) 3.92 5.13

R 0.55 0.61

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

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


Air Resources Laboratory and Georgia Tech for AQAST, Madison, WI, June 13 2012 26

Slide Number 12 Objectives: (A) To improve AQ forecasting� (B) Provide IC and/or BC for SIP modeling Moderate Resolution Imaging Spectroradiometer� (MODIS) Data Assimilation MethodsOptimal Interpolation (OI)Slide Number 6Slide Number 7Model & MODIS AOD on 7/4/117/4/117/5/117/6/11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Summary and future workSlide Number 18Slide Number 19Objective: Provide IC and/or BC for SIP modeling Slide Number 21Slide Number 22Slide Number 23Estimate Model Error Statistics w/ Hollingsworth-Lonnberg MethodSlide Number 25Stats for Daily Maximum 8-hr O3 at All AQS Sites within 4km Domain�

aqast tiger team project*: chemical data assimilation...

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