Development of

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Development of Alternative Methods For
Estimating Dry Deposition Velocity In CMAQ
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Kiran Alapaty University of North Carolina at
Chapel Hill
Dev Niyogi North Carolina State University
Sarav Arunachalam Andrew Holland Kimberly
Hanisak University of North Carolina at Chapel
Hill
Marvin Wesely (Posthumous) Argonne National
Laboratory
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INTRODUCTION
Dry Deposition Velocity estimation
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Time Series of Dom Avg Resistances
Log Scale
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Relation of Rc to Stomatal Resistance

• Rc ? sum of several resistance for the
• Soil-vegetation Continuum.
• One of them is the Stomatal Resistance
• for a gas (Rsg)
• Rsg is proportional to Rsw
• Rsw Plays an important role in Land
• surface Modeling.

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• Stomatal Resistance
• A key Parameter in
• Land surface Modeling

• Why ?
• Stomata Controls Water Vapor Exchange

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Stoma (pore) through which CO2 enters for use in
Photosynthesis releases O2 H2O
Depending on the applications, Rs is modeled
using a variety of forcings. For environmental
Applications - Wesely scheme - Jarvis scheme
- BallBerry scheme
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• JARVIS method is used in many LSMs
• (traditional in Met Models)
• WESELY method is used many AQMs
• Micro-Met and GCMs use
• Photosynthesis/CO2 assimilation

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Stomatal Resistance Formulations
WESELY
JARVIS
Ball-Berry (GEM)
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• JARVIS WESELY methods
• Based on Minimum Stom. Resist.
• Ball Berry method
• Based on Photosynthesis approach
• (e.g., Farquhar, Collatz, Niyogi et al. ,
• Wu et al.)

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WESELY
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JARVIS
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GEM
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OBJECTIVES Introduce and
evaluate a Photosynthesis-based Vegetation
Model for estimating stomatal resistance in
MM5 and deposition velocity in CMAQ Intercompare
results from Jarvis-, Wesely-, GEM
(photosynthesis) type methods
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• Methodology
• Photosynthesis Model Development
• Testing in 1D mode
• Integrate GEM, Wesely, and Jarvis
• within a LSM
• Couple Unified LSM (with three schemes)
• to MM5
• Develop 3D model simulations using MM5
• Use Vd estimates from the three schemes
• in CMAQ

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GEM development results 1-D Model Results
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MM5 Simulation Details

• 28 Layers
• MRF ABL
• Noah LSM
• Grell
• RRTM
• FDDA
• 5.5 days
• 23 Aug 2000
• TDL hourly Data

Simulation Domain 36 km grids for Texas Air
Quality Study
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Will Present

• Discussion of MM5 / Unified Noah
• (with three Rs schemes) model Results
• Model performance statistics with surface
  observations
• Model diagnostics for the 3 schemes (surface
  parameters energy fluxes, temperature, and
  estimated Rs values,.)

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Surface Observations used in STATS
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Time Series for Temp1.5
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Temperature Bias (Model Obs)
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(No Transcript)
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Mod. Lowest Vs Obs. Surface Level Qv
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(No Transcript)
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Diagnostic Other Parameters
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Land Domain Avg. ABL Depths (m)
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Land Domain Avg. TRF (cm/h)
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Canopy Conductance
Sfc. Latent Heat Flux
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Sfc. Sensible Heat Flux
Sfc. Latent Heat Flux
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Agriculture Land (26)
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RANGE Land (34)
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Land Use Patterns
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Coniferous (14)
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URBAN Land (0.13)
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ABL Depths at 20 UTC
GEM
WES
JAR
(Acquire Lidar other ABL obs)
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TRF per hour
GEM
WES
JAR
(Acquire Stage IV Radar)
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Cloud Fraction
GEM
WES
JAR
(Acquire GOES)
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MCIP ? was modified to generate Dep Vel
fields using M3-DryDep for CMAQ
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Dep. Vel. for Ozone at 22 UTC
GEM
WES
JAR
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Dep. Vel. for NO2 at 22 UTC
GEM
WES
JAR
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Domain Averaged Vd for O3
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We are still doing analysis of MET fields Once
completed, we will perform CMAQ simulations by
keeping all MET fields identical except Dep Vel
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• These Schemes are also being
• tested in WRF model
• WRF-CMAQ driver is also
• Under construction