Using combined Lagrangian and Eulerian modeling approaches to improve particulate matter estimations in the Eastern US. Ariel F. Stein 1, Rohit Mathur 2, Daiwen Kang 3 and Roland R. Draxler 4 1 Earth Resources & Technology (ERT) on assignment to the Air Resources Laboratory (ARL) at NOAA 2 Atmospheric Science Modeling Division (ASMD), ARL-NOAA 3 Science and Technology Corporation on assignment to ASMD/ARL 4 Air 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 14 th to 23 rd of –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 ( 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 km 2 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 ( emission algorithm, which includes a fuel type data base and consumption and emissions models

HMS map for July 13 th 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) [V(P,t) + V(P’,t+  t)]  t h P’(t+  t) = P(t) + V(P,t)  t h U max (grid units min -1 )  t h (min) < 0.5 (grid units) X final (t+  t) = X mean (t+  t) + U’(t+  t)  t Z final (t+  t) = Z mean (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 (  r 2 z p ) -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 v 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

MODIS AOD DIFF HYSPLIT-CMAQ to CMAQ AOD 7/17 7/187/ 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., Quantifying CO emissions from 2004 Alaskan wildfires using MOPITT CO data. Geophysical Research Letters, Vol. 32, L Emissions x 10 Emissions scaled to daily total Pfister’s emissions

LIDAR vs CMAQ at Madison WI July 18 th 12 UTC July 19 th 0 UTCJuly 19 th 12 UTC 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