Assimilating AIRNOW Ozone Observations into CMAQ Model to Improve by hvz13267

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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).
            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)
• In a sequential assimilation, at each time
  step, we try to solve the following analysis
  problem
                                1
    X  X  BH ( HBH  O) (Y  HX )
      a     b     T       T




• In OI, we assume only a limited number of
  observations are important in determining
  the model variable analysis increment.
Domain, Grid, and AIRNOW Stations
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,
                                     Error Statistics
               1                                                          60
                                                    Pair Density (1/km)                              EB ~
                                                    Correlation
              0.8                                                         50                           14.2 ppbv
                                                                                                     EO ~




                                                                               Pair Density (1/km)
              0.6                                                         40
                                                                                                       3.3 ppbv
Correlation




              0.4
                                                                          30                         Correlation
                                                                                                      length:
              0.2
                                                                          20
                                                                                                       60 km
               0
                                                                          10


          -0.2
                                                                        0
                    0   200   400       600        800     1000      1200
                                    Distance(km)
    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

 1200 UT, 8/5/07
                   1300           1400         1500
                                                         …

                   Hourly Ozone Observations
                                                      1200 GMT,
  …                                                   8/7/07
Observation-Prediction (in ppbv)
                             Day 1



                         R=0.59   R=0.78




                          1300 - 2400 Z




                         R=0.56   R=0.68




                             Day 2
Surface O3 at 1800Z, 8/5/07

Base Case           OI (Analysis)
Surface O3 at 1800Z, 8/6/07

Base Case
                  OI (Forecast)
Ozone Bias and RMS error
    Bias        RMS error
         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,
  OI vs. 4D-Var
Bias         RMS Error
               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
1hr obs?
Bias correction

								
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