Adaptation and Application of the CMAQ Modeling System for Real-time Air Quality Forecasting During the Summer of 2004 R. Mathur, J. Pleim, T. Otte, K.

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Presentation transcript:

Adaptation and Application of the CMAQ Modeling System for Real-time Air Quality Forecasting During the Summer of 2004 R. Mathur, J. Pleim, T. Otte, K. Schere, J. Young, G. Pouliot, B. Eder Atmospheric Sciences Modeling Division, ARL/NOAA, NERL/U.S. EPA D. Kang, S. Yu, H.-M. Lin Science and Technology Corporation J. McQueen National Centers for Environmental Prediction P. Lee, M. Tsidulko Science Applications International Corporation D. Wong Lockheed Martin Information Technology Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy

Eta-CMAQ AQF System Eta-12 CMAQ Eta Post PRDGEN PREMAQ AQF Post Verification Tools Vertical interpolation from eta to sigma Horizontal interpolation to Lambert grid CMAQ-ready meteorology and emissions Gridded ozone files for users Chemistry model Meteorology model Performance feedback for users and developers

CMAQ Configuration Structural –Netcdf replaced with binary IOAPI Advection –Horizontal: Piecewise Parabolic Method –Vertical: Upstream with rediagnosed vertical velocity to satisfy mass conservation Turbulent Mixing –K-theory; PBL height from Eta –New scheme for specification of minimum K z

CMAQ Configuration (contd.) Gas phase chemistry –CB4 mechanism with EBI solver Cloud Processes –Mixing and aqueous chemistry: following the scheme in RADM Deposition –Dry : M3dry modified to use Eta land surface parameters –Wet Aerosols –2004 release version

grid cells 259 grid cells Northeast “1x” Domain East “3x” Domain CMAQ Modeling Domains Ozone forecasts on 3x and 1x Experimental PM forecasts on 3x

Lateral Boundary Condition Specification A key uncertainty in long term modeling over limited area domains –Determines “model background” Default profiles –“Clean” tropospheric background values –Used in 1x Seasonal Profiles –Derived from continental CMAQ simulations for 2001 –Used in 3x

Lateral Boundary Conditions (contd.) Ozone profiles from NCEP’s Global Forecast System (GFS) –O 3 is a 3-d prognostic variable –Initialized with Solar Backscatter Ultra-Violet (SBUV- 2) satellite observations –Motivation Simulating varying dynamical conditions Improve model representation of O 3 in the free troposphere –Effects associated with intrusions –Study FT-BL exchange mechanisms

Specification of Minimum K z Minimum value of K z allowed to vary spatially depending on urban fraction (f urban ) K z = 0.1 m 2 /s, f urban = 0 K z = 2.0 m 2 /s, f urban = 1 –allows min. K z in rural areas to fall off to lower values than urban regions during night-time; mimics urban heat island effects –prevents precursor concentrations (e.g., CO, NOx) in urban areas from becoming too large at night –lower K z (and reduced mixing intensity) in non-urban areas results in increased night-time O3 titration Helps reduce night time over predictions of ozone “regionally”

Summer 2004: Atypical Ozone Season July 21 Aug. 12 Source: EPA AIRNOW

Model Performance Characteristics: Summer 2004

Bias Effects of GFS Ozone and Cloud Mixing O3O3 Vertical profile Cloud top

GFS Sensitivity Simulations Limit GFS use to above a specified altitude –6 km –10 km Motivation: to limit the use of GFS derived O 3 profiles to the upper levels of the model, where there is greatest confidence in GFS predictions and to avoid abnormally high O 3 within the boundary layer (noticed in early parts of May) Default BC (without GFS)

Target Day Stats: May 18, 2004

Impact of GFS 6km and min. K z change 1x domain Solid lines-with changes Dashed lines-without changes Implemented in 1x domain on 7/20/04 Hourly1 Hr. Max8 Hr. Max

Comparison of 3x and 1x Performance At sites within the 1x domain

Comparison of 3x and 1x Lateral Boundary Conditions Solid lines (3x); filled circles and dash line (1x) CMAQ 2001 Performance at 3x Western Boundary Solid lines: seasonal BCs Dash/dots: Default profile

“Switch-Off” top-down cloud mixing Tropopause limit on cloud top Diagnostic Tests: Cloud Mixing

Diagnostic Tests: Cloud Mixing Target Day Stats: May 18, 2004 FSIDE: switch-off top-down mixing TROPLIM: tropopause limit on cloud top

Diagnostic Tests: Cloud effects on Photolysis O 3 (ppb) Cloud Fraction: Current (average) Cloud Fraction: Modified (max)

Scale by Radiation reaching the surface 1-(J/Jclear) Photolysis Attenuation August 12, 2004

Effects of Cloud Process Modifications Maximum Reductions in O 3 August 12, 2004 Below cloud attenuation based on radiation Below cloud attenuation based on Radiation + switch-off top-down mixing+ New CFRAC

Effects of Cloud Process Modifications August 12, 2004 rad_atten: cloud mixing + photolysis attenuation modifications

Comparison with Previous Day Persistence Max. 1 Hr. Ozone Persistence Eta-CMAQ Correlation coefficient plots from S. McKeen

PM 2.5 Forecast Comparisons with AIRNOW : Preliminary Daily Average August 15August 16 August 17

Summary Lateral boundary conditions play a dominant role in regulating modeled O 3 background levels –Critical when ozone levels are relatively low as in the past two summers –Higher O 3 BC led to a systematic higher bias in the 3x simulation Careful consideration needs to be given in deriving LBCs from larger scale models –Are conditions representative? Bias propagation –Consistent coupling Over-predictions at low O 3 range related to representation of cloud processes –Top-down mixing –Photolysis attenuation

Looking ahead …. Methods to improve coupling between models –Boundary conditions GFS, Eta, CMAQ Layer structure and model top to improve representation of tropopause dynamics Comparison of model (GFS, CMAQ) and observed free- tropospheric O 3 values –Radiation Photolysis attenuation –Boundary layer mixing Revisit Eta-K h –Transition to WRF Advection on E-grid –Minimize interpolations

Looking ahead …. Continue testing alternate formulations over a wider range of conditions –Mixing –Below cloud washout (low observed O 3 during precipitation events) Assessment of experimental PM forecasts results –Surface data: AIRNOW, IMPROVE, CASTNET –Satellites AOD