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Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve Ozone Forecasts

Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve Ozone Forecasts. Tianfeng Chai 1 , Rohit Mathur 2 , David Wong 2 , Daiwen Kang 1 , Hsin-mu Lin 1 , and Daniel Tong 1 1. Science and Technology Corporation, 10 Basil Sawyer Drive, Hampton, VA 23666, USA

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Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve Ozone Forecasts

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  1. Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve Ozone Forecasts Tianfeng Chai1, Rohit Mathur2, David Wong2, Daiwen Kang1, Hsin-mu Lin1, and Daniel Tong1 1. Science and Technology Corporation, 10 Basil Sawyer Drive, Hampton, VA 23666, USA 2. U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA This research is funded by NOAA, under collaboration between NOAA and US EPA (agreement number DW13921548).

  2. 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

  3. 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.

  4. Domain, Grid, and AIRNOW Stations

  5. Estimate Model Error Statistics w/ Hollingsworth-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,

  6. Error Statistics EB ~ 14.2 ppbv EO ~ 3.3 ppbv Correlation length: 60 km

  7. 1200 UT, 8/5/07 1300 1400 1500 … Hourly Ozone Observations 1200 GMT, 8/7/07 … Setup 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

  8. Observation-Prediction (in ppbv) Day 1 R=0.59 R=0.78 1300 - 2400 Z R=0.56 R=0.68 Day 2

  9. Surface O3 at 1800Z, 8/5/07 Base Case OI(Analysis)

  10. Surface O3 at 1800Z, 8/6/07 Base Case OI (Forecast)

  11. Ozone Bias and RMS error Bias RMS error

  12. 4D-Var Data Assimilation • CMAQ v4.5 Adjoint was developed at Virginia Tech. by A. Sandu et al. • Adjoint available for: Transport, Chemistry • Assimilation time window is 15 hours • Only initial O3 are adjusted to minimize the cost functional,

  13. OI vs. 4D-Var Bias RMS Error

  14. Summary • 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

  15. 1hr obs?

  16. Bias correction

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