Using combined Lagrangian and Eulerian modeling approaches to

improve particulate matter estimations in the Eastern US.

Ariel F. Stein1, Rohit Mathur2, Daiwen Kang3 and Roland R. Draxler4

1Earth Resources & Technology (ERT) on assignment to the Air Resources Laboratory (ARL) at NOAA

2Atmospheric Science Modeling Division (ASMD), ARL-NOAA3 Science and Technology Corporation on assignment to ASMD/ARL

4Air Resources Laboratory (ARL) at NOAA

Motivation

• Underestimations in PM: CMAQ domain is not big enough to include long range transport.

• Example: Forest fires in Alaska. July 14th to 23rd of 2004. – Summer 2004: One of the strongest fire seasons on record for

Alaska and Western Canada– Smoke plume from Alaska transported into continental US– PM2.5 grossly under-predicted by ETA-CMAQ forecast system– Model picks up spatial signatures ahead of the front – Simulation under predicts behind the front

System description

• Forest fires emissions

• HYSPLIT

• HYSPLIT-CMAQ interface

• CMAQ

Emissions• Fire locations from Hazard Mapping System Fire and

Smoke Product (http://www.ssd.noaa.gov/PS/FIRE/hms.html)

• The fire position data representing individual pixel hot-spots that correspond to visible smoke are aggregated on a 20 km resolution grid.

• Each fire location pixel is assumed to represent one km2 and 10% of that area is assumed to be burning at any one time.

• PM2.5 emission rate is estimated from the USFS Blue Sky (http://www.airfire.org/bluesky) emission algorithm, which includes a fuel type data base and consumption and emissions models

HMS map for July 13th 2004• The smoke outlines

are produced manually, primarily utilizing animated visible band satellite imagery.

• The locations of fires that are producing smoke emissions that can be detected in the satellite imagery are incorporated into a special HMS file that only denotes fires that are producing smoke emissions.

• These fire locations are used as input to the HYSPLIT model.

HYSPLIT• Same settings as in the Interim Smoke Forecast Tool• Mass distribution:

– Horizontal: Top hat – Vertical: 3D Particle

• Number of lagrangian particles per hour: 500• Release height: 100 m• Meteorology: NCEP Global Data Assimilation System

(GDAS, horizontal resolution 1x1 deg)• Run as in forecast mode: Each calculation is started with all the

pollutant particles that are on the domain at the model's initialization time as computed from the previous day's simulation (yesterday's 24 h forecast).

• Smoke particles are assumed to have a diameter of 0.8 m with a density of 2 g/cc

• Wet removal is much more effective than dry deposition and smoke particles in grid cells that have reported precipitation may deposit as much as 90% of their mass within a few hours

Advection and Dispersion

• P(t+t) = P(t) + 0.5 [V(P,t) + V(P’,t+t)] th

• P’(t+t) = P(t) + V(P,t) th

• Umax(grid units min-1) th(min) < 0.5 (grid units)

Xfinal(t+t) = Xmean(t+t) + U’(t+t) t

Zfinal(t+t) = Zmean(t+t) + W’(t+t) t

HYSPLIT-CMAQ preprocessor• This processor reads the location of each lagrangian particle as

calculated by HYSPLIT and determines the concentration of the pollutant at the boundaries of the CMAQ domain.

• The concentration of each chemical species within a boundary cell is calculated by:– In the vertical: dividing the sum of the particle masses of a particular

chemical compound by the height of the corresponding concentration grid cell in which the particles reside

– In the horizontal: the concentration grid is considered as a matrix of sampling points, such that the puff only contributes to the concentration as it passes over the sampling point

C = q ( r2 zp)-1

• A speciation profile was applied to obtain the chemical species compatible with CMAQ’s chemical mechanism. It was assumed that the composition of PM2.5 was 77% organic carbon , 16% elemental carbon, 2% sulfate, 0.2% nitrate and 4.8% other PM.

CMAQ v4.5

• 259 Columns x 268 Rows• 12x12 km horizontal resolution covering Eastern

US• 22 Vertical layers• Meteorology driven by ETA• Emissions: SMOKE• Chemistry: EBI CB4 • Aerosols: Isorropia AERO3• Advection: YAMO New global mass-conserving

scheme (Robert Yamartino) • Clouds: Asymmetrical Convective Model (ACM)

HYSPLIT vs TOMS

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 0.004 0.015 0.026

MODIS AOD

DIFF HYSPLIT-CMAQ to CMAQ AOD

7/17 7/18 7/19

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

CMAQ NO BC AOD

AOD under estimation

• Transport and dispersion? Not likely. Timing and geographical extension of smoke plume is very good compared with satellite images

• Dry deposition? Not likely. Sensitivity shows no substantial variation in output.

• Emission’s initial height? No. Sensitivity run with 2000m release height shows no substantial difference with base case.

• Emission’s strength? Very uncertain. Could be off by a factor of 10.

Emissions sensitivity

Pfister, G, Hess P.G., Emmons L.K., Lamarque J.-F., Wiedinmyer C., Edwards D.P., Petron G., Gille J.C., and Sachse G.W., 2005. Quantifying CO emissions from 2004 Alaskan wildfires using MOPITT CO data. Geophysical Research Letters, Vol. 32, L11809.

Emissions x 10 Emissions scaled to daily total Pfister’s emissions

LIDAR vs CMAQ at Madison WI

July 18th 12 UTC July 19th 0 UTC July 19th 12 UTC

PBL heightPBL height

PBL height

Statistics

Conclusions and future activities

• Coupled models capture the main features of PM long range transport

• Magnitude of PM emissions are an issue• Advantage of using HYSPLIT: vertical

distribution of PM• Integrate operational HYSPLIT interim

forecast system with operational CMAQ forecast system?

• How about dust?

HYSPLIT-CMAQ GUI