1. CMAQ PM 2.5 Forecasts Adjusted to Errors in Model Wind Fields Eun-Su Yang 1, Sundar A. Christopher 2 1 Earth System Science Center, UAHuntsville 2 Department of Atmospheric Science, UAHuntsville Shobha Kondragunta 3, and Xiaoyang Zhang 4 3 NOAA/NESDIS/STAR 4 Earth Resources Technology Inc. at NOAA/NESDIS/STAR Presentation to the 9 th CMAS Conference October 11, 2010

2. OUTLINE Motivation - Georgia fires in 2007 - large discrepancy between simulated and observed PM 2.5 Smoke position error due to model wind error - wind error with a random component - wind error with random & autoregressive (AR) components first attempt to estimate transport error amplified by AR processes Suggestion to improve PM 2.5 forecasts Summary

3. MOTIVATION: Georgia fires in 2007 (Left) Fire spots and fire plumes viewed from the MODIS Terra on May 22, 2007. Taken from http://rapidfire.sci.gsfc.nasa.gov/realtime. Fire location and emission data from the GOES biomass burning emission product (ftp://satepsanone.nesdis.noaa.gov/EPA/GBBEP) Transport of fire plume from MM5 and CMAQ

4. MODIS Aqua AOT CMAQ FIRE Surface PM 2.5 AIRS Total column CO Overall Agreement The CMAQ PM 2.5 simulations (middle) reproduced well the observed pattern of smoke transport (see the map of AIRS total column CO).

5. MOTIVATION: Discrepancy CMAQ missed the high PM 2.5 episode May 26, 2007 Uncertainties in fire emission estimates and plume injection height were not the primary cause.

6. Select the worst MM5 case example during May 20-31. 24-hour forward trajectories, starting at 925 hPa, 00Z May 26, 2007 Position error might be too underestimated

7. MM5 Wind Evaluation (1) Comparison with the Automated Surface Observing System (ASOS) observations at 10 m for April 1 – May 31, 2007

8. MM5 Wind Evaluation (2) Bias (m/sec) (u / v) RMSE (m/sec) (u / v) Birmingham-0.1 / 0.41.5 / 1.5 Atlanta-0.2 / 0.61.4 / 1.7 Tallahassee0.0 / 0.11.4 / 1.4 Bias: -0.2 ~ 0.6 and RMSE: < 2 m/sec agree well with the previous works [e.g., Emery et al., 2001; Olerud and Sims, 2004] similar errors by comparing with the Rapid Update Cycle (RUC) analysis data at various pressure levels

9. MM5 Winds versus RUC Analysis Winds along Trajectory Path If the RUC wind is assumed true:

10. First-Order Autoregressive Process, AR(1) 0.75 < AR(1) < 0.95 for (MM5 – RUC) and (MM5 – ASOS) winds

11. The Statistical Perspective Variance of position at time t = wind variance at time 0 * time interval + wind variance at time 1 * time interval … + wind variance at time t-1 * time interval VAR{D t } = VAR{U 0 } + VAR{U 1 } + … + VAR{U t-1 } = VAR{ (ε 0 + φ ε -1 + φ 2 ε -2 + φ 3 ε -3 + φ 4 ε -4 + φ 5 ε -5 + …) + (ε 1 + φ ε 0 + φ 2 ε -1 + φ 3 ε -2 + φ 4 ε -3 + φ 5 ε -4 + …) + (ε 2 + φ ε 1 + φ 2 ε 0 + φ 3 ε -1 + φ 4 ε -2 + φ 5 ε -3 + …) … + (ε t + φ ε t-1 + φ 2 ε t-2 + φ 3 ε t-3 + φ 4 ε -4 + φ 5 ε -5 + …) } where σ ε 2 /(1 - φ 2 ) = σ 2, ε i and ε j are independent if i ≠ j [see Yang et al., 2006, JGR] σ ε 2 /(1-φ 2 ) = σ 2 σ 2 (1+φ)/(1-φ) φ σ 2 (1+φ)/(1-φ)

12. Variance of Position Error without AR(1) t = 1, VAR = 1 ∙ σ 2 t = 2, VAR = (2 + 2 φ ) ∙ σ 2 t = 3, VAR = (3 + 4 φ + 2 φ 2 ) ∙ σ 2 t = 4, VAR = (4 + 6 φ + 4 φ 2 + 2 φ 3 ) ∙ σ 2 t = 5, VAR = (5 + 8 φ + 6 φ 2 + 4 φ 3 + 2 φ 4 ) ∙ σ 2 … where φ is the AR(1) coefficient (-1< φ < 1). If φ = 0, VAR ~ t & standard deviation ~ t 1/2. If φ  1, VAR  t 2, standard deviation  t

13. t = 1, VAR = 1 ∙ σ 2 t = 2, VAR = (2 + 2 φ ) ∙ σ 2 t = 3, VAR = (3 + 4 φ + 2 φ 2 ) ∙ σ 2 t = 4, VAR = (4 + 6 φ + 4 φ 2 + 2 φ 3 ) ∙ σ 2 t = 5, VAR = (5 + 8 φ + 6 φ 2 + 4 φ 3 + 2 φ 4 ) ∙ σ 2 … where φ is the AR(1) coefficient (-1< φ < 1). If φ = 0, VAR ~ t & standard deviation ~ t 1/2. If φ  1, VAR  t 2, standard deviation  t Variance of Position Error with AR(1)

14. Position error with AR(1) in model wind error t 1/2 t1t1

15. The Physical Perspective Map from http://www.hpc.ncep.noaa.gov Surface map at 18Z May 26, 2007 Model wind errors have short-term memory. Modeled or analyzed winds are subject to have the AR behavior due to the use of incomplete modeling of wind that is aroused from dynamical simplifications, incorrect physical parameterizations, incorrect initial and boundary conditions, among others. 17Z18Z19Z…days later RUC??? MM5???

16. Allow smoke plume in all grids within the range of (1-sigma) position error to not miss the observed-but-not-predicted case Not Observed Predictedoccasionallyrarely Not Predicted most oftenoccasionally When smoke position error >> smoke width: site

17. Think about Ensemble Approach Initial perturbations (σ) are typically assumed Gaussian (normal) Magnitude of the perturbations is given empirically Our approach is analytical and needs model run only once

18. Comparison with AirNow PM 2.5 Observations Local emissionsLocal + fire emissions with increased range of smoke CMAQ: local emissions from the emission inventory model (SMOKE) fire emissions from the GOES biomass burning emission product Observations: daily surface-level PM 2.5 observations at 17 AirNow sites for April - May, 2007: 8 in Alabama, 6 in Georgia, and 3 in northern Florida. missed alarm  versus false alarm  EPA daily PM 2.5 limit

19. SUMMARY Model winds are a key factor in predicting smoke position Model wind errors are positively autocorrelated Smoke position error tends to be underestimated without consideration of AR(1) in model wind error AR(1) term is included to capture high PM 2.5 episodes and reduces the number of missed-alarm cases Ensemble approach could be improved by adding AR processes