Development of Alternative Methods For Estimating Dry Deposition Velocity In CMAQ.

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Presentation transcript:

Development of Alternative Methods For Estimating Dry Deposition Velocity In CMAQ

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

Dry Deposition Velocity estimation INTRODUCTION

Time Series of Dom Avg Resistances Log Scale

Rc  sum of several resistance for the Soil-vegetation Continuum. One of them is the Stomatal Resistance for a gas (R sg ) R sg is proportional to R sw R sw Plays an important role in Land surface Modeling. Relation of Rc to Stomatal Resistance

Stomatal Resistance: A key Parameter in Land surface Modeling Why ? Stomata Controls Water Vapor Exchange

Stoma (pore) through which CO 2 enters for use in Photosynthesis; releases O 2 & H 2 O Depending on the applications, Rs is modeled using a variety of forcings. For environmental Applications: - Wesely scheme - Jarvis scheme - Ball–Berry scheme

JARVIS method is used in many LSMs (traditional in Met Models) WESELY method is used many AQMs Micro-Met and GCMs use Photosynthesis/CO 2 assimilation

Stomatal Resistance Formulations WESELY JARVIS Ball-Berry (GEM)

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.)

WESELY

JARVIS

GEM

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

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 V d estimates from the three schemes in CMAQ

GEM development results 1-D Model Results

MM5 Simulation Details Simulation Domain – 36 km grids for Texas Air Quality Study 28 Layers MRF ABL Noah LSM Grell RRTM FDDA 5.5 days 23 Aug 2000 TDL hourly Data

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

Surface Observations used in STATS

Time Series for Temp1.5

Temperature Bias (Model – Obs)

Mod. Lowest Vs Obs. Surface Level Qv

Diagnostic & Other Parameters

Land Domain Avg. ABL Depths (m)

Land Domain Avg. TRF (cm/h)

Canopy Conductance Sfc. Latent Heat Flux

Sfc. Sensible Heat Flux

Agriculture Land (26%)

RANGE Land (34%)

Land Use Patterns

Coniferous (14%)

URBAN Land (0.13%)

ABL Depths at 20 UTC WES JAR GEM (Acquire Lidar & other ABL obs)

TRF per hour WES JAR GEM (Acquire Stage IV Radar)

Cloud Fraction WES JAR GEM (Acquire GOES)

MCIP  was modified to generate Dep Vel fields using M3-DryDep for CMAQ

WES JAR GEM Dep. Vel. for Ozone at 22 UTC

WES JAR GEM Dep. Vel. for NO 2 at 22 UTC

Domain Averaged V d for O 3

We are still doing analysis of MET fields Once completed, we will perform CMAQ simulations by keeping all MET fields identical except Dep Vel

These Schemes are also being tested in WRF model WRF-CMAQ driver is also Under construction