CMAQ PM 2.5 Forecasts Adjusted to Errors in Model Wind Fields

1. CMAQ PM2.5 Forecasts Adjusted to Errors in Model Wind Fields Eun-Su Yang1, Sundar A. Christopher2 1Earth System Science Center, UAHuntsville 2Department of Atmospheric Science, UAHuntsville Shobha Kondragunta3, and Xiaoyang Zhang4 3NOAA/NESDIS/STAR 4Earth Resources Technology Inc. at NOAA/NESDIS/STAR Presentation to the 9th CMAS Conference October 11, 2010

2. OUTLINE • Motivation • - Georgia fires in 2007 • - large discrepancy between simulated and observed PM2.5 • 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 PM2.5 forecasts • Summary

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

4. Overall Agreement MODIS Aqua AOT CMAQ FIRE Surface PM2.5 AIRS Total column CO The CMAQ PM2.5 simulations (middle) reproduced well the observed pattern of smoke transport (see the map of AIRS total column CO).

5. MOTIVATION: Discrepancy May 26, 2007 CMAQ missed the high PM2.5 episode Uncertainties in fire emission estimates and plume injection height were not the primary cause.

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

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: -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{Dt} = VAR{U0} + VAR{U1} + … + VAR{Ut-1} = VAR{ (ε0 + φ ε-1 + φ2 ε-2 + φ3 ε-3 + φ4 ε-4 + φ5 ε-5 +…) + (ε1 + φ ε0 + φ2 ε-1 + φ3 ε-2 + φ4 ε-3 + φ5 ε-4 +…) + (ε2 + φ ε1 + φ2 ε0 + φ3 ε-1 + φ4 ε-2 + φ5 ε-3 +…) … + (εt + φ εt-1 + φ2 εt-2 + φ3 εt-3 + φ4 ε-4 + φ5 ε-5 +…) } where σε2/(1 - φ2) = σ2, εiand εj are independent if i ≠ j [see Yang et al., 2006, JGR] σε2/(1-φ2) = σ2 σε2/(1-φ2) = σ2 σ2 (1+φ)/(1-φ) φσ2 (1+φ)/(1-φ)

12. Variance of Position Error without AR(1) t = 1, VAR = 1 ∙ σ2 t = 2, VAR = (2 + 2φ) ∙ σ2 t = 3, VAR = (3+ 4φ + 2φ2) ∙ σ2 t = 4, VAR = (4+ 6φ + 4φ2 + 2φ3) ∙ σ2 t = 5, VAR = (5+ 8φ + 6φ2 + 4φ3 + 2φ4) ∙ σ2 … where φ is the AR(1) coefficient (-1< φ < 1). If φ = 0, VAR ~ t & standard deviation ~ t1/2. If φ 1, VAR  t2, standard deviation  t

13. Variance of Position Error with AR(1) t = 1, VAR = 1 ∙ σ2 t = 2, VAR = (2 + 2φ) ∙ σ2 t = 3, VAR = (3+ 4φ + 2φ2) ∙ σ2 t = 4, VAR = (4+ 6φ + 4φ2 + 2φ3) ∙ σ2 t = 5, VAR = (5+ 8φ + 6φ2 + 4φ3 + 2φ4) ∙ σ2 … where φ is the AR(1) coefficient (-1< φ < 1). If φ = 0, VAR ~ t & standard deviation ~ t1/2. If φ 1, VAR  t2, standard deviation  t

14. Position error with AR(1) in model wind error t1 t1/2

15. The Physical Perspective 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. Map from http://www.hpc.ncep.noaa.gov

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

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 PM2.5 Observations CMAQ: local emissions from the emission inventory model (SMOKE) fire emissions from the GOES biomass burning emission product Observations: daily surface-level PM2.5 observations at 17 AirNow sites for April - May, 2007: 8 in Alabama, 6 in Georgia, and 3 in northern Florida. Local + fire emissions with increased range of smoke Local emissions Local + fire emissions EPA daily PM2.5 limit missed alarm  versus false alarm 

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 PM2.5 episodes and reduces the number of missed-alarm cases • Ensemble approach could be improved by adding AR processes