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/STAR4Earth Resources Technology Inc. at NOAA/NESDIS/STAR

Presentation to the 9th CMAS Conference

October 11, 2010

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

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

MODIS AquaAOT

CMAQ FIRESurface PM2.5

AIRSTotal column CO

Overall Agreement

The CMAQ PM2.5 simulations (middle) reproduced well the observed pattern of smoke transport (see the map of AIRS total column CO).

MOTIVATION: Discrepancy

CMAQ missed the high PM2.5 episode

May 26, 2007

Uncertainties in fire emission estimates and plume injection height were not the primary cause.

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

MM5 Wind Evaluation (1)

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

MM5 Wind Evaluation (2)

Bias (m/sec)

(u / v)

RMSE (m/sec)

(u / v)

Birmingham -0.1 / 0.4 1.5 / 1.5

Atlanta -0.2 / 0.6 1.4 / 1.7

Tallahassee 0.0 / 0.1 1.4 / 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

MM5 Winds versus RUC Analysis Windsalong Trajectory Path

If the RUC wind is assumed true:

First-Order Autoregressive Process, AR(1)

0.75 < AR(1) < 0.95 for (MM5 – RUC) and (MM5 – ASOS) winds

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, εi and 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)φ σ2 (1+φ)/(1-φ)

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

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

Variance of Position Error with AR(1)

Position error with AR(1) in model wind error

t1/2

t1

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.

17Z 18Z 19Z … days later

RUC ???

MM5 ???

Allow smoke plume in all grids within the range of (1-sigma) position error to not miss the observed-but-not-predicted case

NotObserved Observed

Predicted occasionally rarely

NotPredicted

most often occasionally

When smoke position error >> smoke width:

site

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

Comparison with AirNow PM2.5 Observations

Local emissions Local + fire 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 productObservations: 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.

missed alarm versus false alarm

EPA daily PM2.5 limit

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