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

2. 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

3. Dry Deposition Velocity estimation INTRODUCTION

4. Time Series of Dom Avg Resistances Log Scale

5. 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

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

7. 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

8. 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

9. Stomatal Resistance Formulations WESELY JARVIS Ball-Berry (GEM)

10. 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.)

11. WESELY

12. JARVIS

13. GEM

14. 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

15. 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

16. GEM development results 1-D Model Results

17. 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

18. 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:

19. Surface Observations used in STATS

20. Time Series for Temp1.5

21. Temperature Bias (Model – Obs)

23. Mod. Lowest Vs Obs. Surface Level Qv

25. Diagnostic & Other Parameters

26. Land Domain Avg. ABL Depths (m)

27. Land Domain Avg. TRF (cm/h)

28. Canopy Conductance Sfc. Latent Heat Flux

29. Sfc. Sensible Heat Flux

30. Agriculture Land (26%)

31. RANGE Land (34%)

32. Land Use Patterns

33. Coniferous (14%)

34. URBAN Land (0.13%)

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

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

37. Cloud Fraction WES JAR GEM (Acquire GOES)

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

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

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

41. Domain Averaged V d for O 3

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

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