Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve Ozone Forecasts

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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).
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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

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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.

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Domain, Grid, and AIRNOW Stations
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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,

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Error Statistics
EB 14.2 ppbv EO 3.3 ppbv Correlation
length 60 km
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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

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Observation-Prediction (in ppbv)
Day 1
R0.78
R0.59
1300 - 2400 Z
R0.56
R0.68
Day 2
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Surface O3 at 1800Z, 8/5/07
Base Case
OI (Analysis)
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Surface O3 at 1800Z, 8/6/07
Base Case
OI (Forecast)
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Ozone Bias and RMS error
Bias
RMS error
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4D-Var Data Assimilation

1. 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,

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OI vs. 4D-Var
Bias
RMS Error
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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

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1hr obs?
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Bias correction