Assimilating AIRNOW Ozone Assimilating AIRNOW Ozone Observations into CMAQ Model to Observations into CMAQ Model to

Improve Ozone ForecastsImprove Ozone Forecasts

Tianfeng ChaiTianfeng Chai11, Rohit Mathur, Rohit Mathur22, David Wong, David Wong22, , Daiwen KangDaiwen Kang11, Hsin-mu Lin, Hsin-mu Lin11, and Daniel Tong, and Daniel Tong11

1. Science and Technology Corporation, 10 Basil 1. Science and Technology Corporation, 10 Basil Sawyer Drive, Hampton, VA 23666, USA Sawyer Drive, Hampton, VA 23666, USA

2. U.S. Environmental Protection Agency, Research 2. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USATriangle Park, NC 27711, USA

This research is funded by NOAA, under collaboration between NOAA and US EPA (agreement number DW13921548).

Background Background

• In meteorology, assimilating real-time observations is essential in all weather forecasting systems

• AIRNOW ozone measurements are available in near real time, and can be used to improve ozone forecasts

• Optimal Interpolation has potential to be applied operationally for air quality forecasting

Optimal Interpolation (OI)Optimal Interpolation (OI)• In a sequential assimilation, at each time

step, we try to solve the following analysis problem

• In OI, we assume only a limited number of observations are important in determining the model variable analysis increment.

)()( 1 HXYOHBHBHXX TTba

Domain, Grid, and AIRNOW StationsDomain, Grid, and AIRNOW Stations

Estimate Model Error Statistics w/ Estimate Model Error Statistics w/ Hollingsworth-Lonnberg MethodHollingsworth-Lonnberg Method

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

• 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,

Error StatisticsError Statistics

EB ~

14.2 ppbv

EO ~

3.3 ppbv

Correlation length:

60 km

Distance(km)

Co

rre

latio

n

Pa

irD

en

sity

(1/k

m)

0 200 400 600 800 1000 1200

-0.2

0

0.2

0.4

0.6

0.8

1

0

10

20

30

40

50

60Pair Density (1/km)Correlation

Setup of OI Assimilation TestsSetup of OI Assimilation Tests

• Model starts at 1200 GMT, 8/5/07

• Hourly AIRNOW observations assimilated in first 24 hours

• Model continues to run another day without observations

1200 GMT, 8/7/07

1300 1500 1400

Hourly Ozone Observations

…

…

1200 UT, 8/5/07

Observation-Prediction (in ppbv)Observation-Prediction (in ppbv)

Day 2

Day 1

1300 - 2400 Z

R=0.78

R=0.56

R=0.59

R=0.68

Surface OSurface O33 at 1800Z, 8/5/07 at 1800Z, 8/5/07

Base Case OI (Analysis)

Surface OSurface O33 at 1800Z, 8/6/07 at 1800Z, 8/6/07

Base Case OI (Forecast)

Ozone Bias and RMS errorOzone Bias and RMS error

Bias RMS error

4D-Var Data Assimilation4D-Var Data Assimilation1. CMAQ v4.5 Adjoint was

developed at Virginia Tech. by A. Sandu et al.

2. Adjoint available for:Transport, Chemistry

3. Assimilation time window is 15 hours

4. Only initial O3 are adjusted to minimize the cost functional,

OI vs. 4D-VarOI vs. 4D-Var

Bias RMS Error

SummarySummary• CMAQ model error statistics has been

estimated using Hollingsworth-Lonnberg method

• The model error covariance is used in optimal interpolation to assimilate AIRNOW observations

• Assimilating AIRNOW observations into CMAQ model using Optimal Interpolation proves to be beneficial for the next-day ozone forecasting

• The positive effect of assimilation is throughout the second day, but the effect on the night-time ozone forecasts is minimal

• A 4D-Var data assimilation test shows similar effect as OI

1hr obs?1hr obs?

Bias correctionBias correction