1. Adel Hanna Director, CMAS
A Community Approach to the Development and Applications of Environmental Decision Support Systems
Adel Hanna
Director, CMAS

2. Center for Environmental Modeling for Policy Development
A group of 22 scientists at the University of North Carolina at Chapel Hill
Started at CSC (Mid 1980’s)
Moved to MCNC/NCSC (1992)
Moved to UNC/CEP (2003)
Important products:
I/O API, MAQSIP, SMOKE, PAVE, MIMS
Host of Community Modeling and Analysis Center (CMAS)

3. CMAS www.cmascenter.org
Centralized organization to fill the gaps through technology transfer and establish the links in communications between community members.
Serve as a bridge between various segments of the air quality modeling community
Foster the growth of the developer and user communities
Serve as a clearinghouse of information
Become a hub for education and training about modeling

4. Community Issues and Needs
One Atmosphere
Atmospheric gases
Particulate Matter and Mercury
Air Toxics
Models and Tools
Knowledge
Education and Training
Attributes
Availability of Technical Resources and Support
Technology Transfer

5. Models-3 Approach One Atmosphere Modeling Systems Attributes
Description of the Atmospheric System
Multi-scale
Multi-pollutant
Modeling Systems
Flexible and expandable
Modular
Analysis tools and Data handling
Attributes
Open Source
Multi-user
Platform for multiple contributions

6. Supported Products Community Multiscale Air Quality (CMAQ)
Sparse Matrix Operator Kernel Emissions (SMOKE)
Meteorology Chemistry Interface Processor (MCIP)
Package for Analysis and Visualization for Environmental (PAVE) Data
Input/Output Application Programming Interface (IOAPI)
Multimedia Integrated Modeling System (MIMS)

7. Model Releases (2005) CMAQ SMOKE MCIP (version 3.0) (9/21/2005)
CMAQ version 4.5 released (9/21/2005)
CMAQ-MADRID , CMAQ-MADRID- MERCURY and CMAQ-APT
CMAQ release with Mercury (Planned January 2006)
SMOKE
SMOKE version 2.2 released; more streamline of processing for CAMx, REMSAD, and UAM
SMOKE with emission processing capabilities for the dispersions models AERMOD, ISCST3, and ASPEN
MCIP (version 3.0) (9/21/2005)
PAVE (version 2.3) (9/21/2005)

8. Training At least Four training sessions per year
CMAQ
SMOKE
MIMS
Upgrading training material to match latest released versions of CMAQ and SMOKE
Advanced Training on CMAQ and SMOKE
Process Analysis
Surrogate generation for Spatial Allocator
Advanced Quality Assurance topics with Smkreport
Web-based training modules

9. Support and Analysis Tools
On-line CMAQ operational guidance document
CMAQ Graphical User Interface (GUI)
help you to download, compile, and run the various components of CMAQ
Run on Linux
Other Operating Systems will be possibly supported
MIMS Spatial Allocator
Processing of surrogates for emissions
create the inputs for biogenic emissions processing that are required by the SMOKE modeling system
PAVE
Several updates in PAVE 2.3

10. CMAS
Online Help Desk
Model Clearinghouse

11. Community Participation
New Modules and Enhancements
Model Development and Evaluation
Feedbacks, Sharing of Analysis, results and data
Participate in developing the strategy for CMAS
Outreach
Annual Conference
Newsletters
Specialty Workshops
Online Seminars
Reviews and Publications
Second CMAQ Review
Special Issue (Atmospheric Environment) (2004 Conference)
Special Issue (Journal of Applied Meteorology) (2005 Conference)

12. CMAS over the Years Average 50 visits per day to the CMAS web site
More than 800 help desk tickets
On site and off site training
2005 conference 110 papers and 205 participants

13. Emissions Modeling Framework
System to manage data input to SMOKE in a relational database
Supports data versioning, metadata input and tracking, quality assurance
Eventually also run SMOKE
Extensible to other types of data
Open source, free software
QA done in part using EmisView

14. EmisView
Open source tool for quality assurance and visualization of emissions data
Goal: Create plots and tables of emission summaries at various spatial and temporal resolutions
Supports emissions inventory and modeling staff at states, RPOs, EPA, and industry
Supports SMOKE and CONCEPT
Quality Assurance Tool for emission inventory data

15. EmisView Main GUI

16. Analysis Results
Source Classifications Code
From the results table you can sort, filter, show/hide cols, format, create plots, show a description, load/save configuration, export

17. Examples of Plots
Available Plot types: Bar Plot, Box Plot, CDF Plot, Histogram, Discrete Category Plot, Rank Order Plot, Tornado Plot, XY

18. Spatial Allocator
Spatial Allocator developed as part of the Multimedia Integrated Modeling System
Free open-source software
Targeted software for spatial functions but does not require a GIS
Shapefiles are primary input format
Also I/O API files and ASCII “PointFiles”
Controlled using environment variables
Driven from scripts

19. Phase 2 Components Posted July 9, 2005
allocator: performs several types of spatial allocation
beld3smk: creates biogenic inputs to SMOKE based on BELD3 land use
diffioapi: differences I/O API files
dbf2asc: creates a .csv file from a .dbf file
srgcreate: creates spatial surrogates
srgmerge: merges and gapfills surrogates
diffsurr: differences surrogates
libspatial: library shared by applications

20. Modes of Allocator Program
CONVERT_SHAPE: create a copy of a Shapefile with a new map projection
FILTER_SHAPE: Apply a filter to a Shapefile to create a new Shapefile that is a subset of the original
OVERLAY: Print data values of an input file that are overlap a shape (e.g., a grid)
ALLOCATE: Allocate data from one geospatial unit (e.g., grid cells) to another (e.g., counties or another grid)

21. The Value of CMAS Provide linkage between users and developer
Offer training on air quality modeling and analysis
Provide analysis tools to understand model results;
Report on case studies of interest;
Evaluate the models to insure the sound science in analyzing various problems;
Provide a platform for presentations, publications and exchange of information;
Establish communications with the international community;
Provide direct guidance to community users and model developers.

22. CMAS Team Applications and Training
Software Development and Analysis Tools
Modeling Research
Outreach
Registration Coordinator
Technical Editing
Zac Adelman, Andy Holland
Alison Eyth, Limei Ran, BH Beak
Frank Binkowski, Uma Shankar, Aijun Xiu, Sarav Arunachalam
Ken Galluppi
Brian Naess
Jeanne Eichinger

23. Modeling Research Updates on the CMAQ Land surface modeling
Inline radiative transfer calculations for photolysis in CMAQ
PM calculations including incorporation of Sea Salt aerosols
Cloud effects on photolysis rates
Introduction to coarse mode chemistry
Satellite Analysis
Integrated Meteorology-Chemistry (Example INDOEX)
Inter Continental and Northern Hemisphere domains
Variable Grid CMAQ (36km to 500 m)
Four Dimensional Data Assimilation
Land surface modeling
PX scheme in WRF

24. Integrated System Solution Chart
Figure 3. Integrated System Solution chart

25. GOES/AERONET AOD
Figure 1: Correlation between GOES Aerosol and Smoke Product (GASP) and AERONET AOD measurements for COVE site in the eastern U.S. Match-ups were performed if measure­ments were within 15 minutes; 517 match-ups were obtained for July and August 2004.
AOD is obtained from a vertical integral of aerosol extinction from PM2.5 at a wavelength commensurate with that observed by sateelite

26. CMAQ/GOES AOD
Figure 2: 17 July Z, CMAQ forecast (top left) and GOES observations (bottom left). 23 July Z, CMAQ forecast (top right) and GOES observations (bottom right). The color bar indicates AOD ranging from 0.0 (blue) to 1.4 (red).

27. MODIS AOD/PM 2.5
Figure 4. Sample air quality analysis product from the University of Wisconsin-Madison Space Science & Engineering Center's IDEA (Infusing satellite Data into Environmental Applications) project. The 60-day running correlation is based on coincident MODIS AOD pixel values and 1‑h in situ PM2.5 concentrations measured at surface stations. The values of the correlation coefficient range from -1 (perfectly anticorrelated) to zero (two measurements vary independent­ly of each other) to 1 (perfectly correlated). The size of the point plotted indicates the number of coincidences between MODIS AOD pixels and 1-h PM2.5 concentrations for a given time period. Generally, the significance of the correlation increases as the number of coincidences increases.

28. PM 2.5 and MODIS AOD
Figure 5. Site-specific 60-day time-series and correlations of MODIS AOD and surface PM2.5 concentrations plotted using the UW-Madison SSEC IDEA project’s web interface ( In situ PM2.5 concentrations from continuous monitors are displayed for both 1-h (solid line) and running 24-h (dashed line) averages. Coincident correlation values are represented by solid red circular symbols for MODIS AOD, black asterisks for 1-h average PM2.5 mass concentrations, and hollow black triangles for 24-h average PM2.5 mass concentra­tions. Higher correlations suggest the MODIS AOD pixel value is reflective of in situ surface PM2.5 mass concentrations at the monitor location. The vertical distance between coincident points indicates whether the aerosol viewed by MODIS is well mixed and at or near the surface (small separation distance), or whether the aerosol is aloft (large separation distance)

29. Integrated Meteorology-Chemistry Model (METCHEM)
Radiative Feedback of Aerosols
H & V Transport, Cloud Physics & Chemistry, Gas/Particulate Chemistry, PM Microphysics (Modal), Dry & Wet Removal
(MAQSIP CTM)
Meteorology
(MM5)
Met. Couple
(MCPL)
Emissions Processing
(SMOKE)

30. INDOEX Overview
Transport of polluted continental air across Bay of Bengal, Arabian Sea during Indian winter monsoon to remote Indian Ocean, thought to exert significant forcing on climate in the region
Trace gas, sulfate and carbonaceous aerosol loadings, and aerosol radiative properties measured during the Indian Ocean Experiment from a variety of platforms to understand relative contributions to aerosol radiative forcing
Intensive field phase Jan–April 1999 (IFP99) showed unique aerosol signature, higher BC loadings than in other parts of the world
The Indian Ocean Experiment that was conducted from , culminating in the intensive field phase of 1999 aimed to assess the importance of continental aerosols to the radiative forcing . During the winter monsoon, ~ Dec – March, polluted air masses are carried across the subcontinent from north and transported to the pristine areas of the So. Indian Ocean. This region provided a natural laboratory for studying the flow patterns in the ITCZ and their influences on the observed concentrations of trace gases, sulfate and carbonaceous aerosol. Shipboard and land-based measurements were made for 4 months from Jan – Apr 99.

31. Modeling Domains 108-km 36-km
Special thanks to Shekar Reddy and Chandra Venkataraman for the LMD GCM results, and consultation on the use of the sectoral emissions data

32. INDOEX Region K = Kaashidoo Climate Observatory (AERONET)
G = Goa (AERONET)
D = Dharwar (AERONET)
M = Mumbai
Dotted lines: Sagar Kanya cruise track
Solid lines: Ronald Brown cruise track

33. Emissions Data
Coarse domain uses GEIA/EDGAR/WEBDAB databases for Europe, Africa and Middle East; includes sulfur species, BC, OM and PM2.5 from power plants and other industry, transportation, domestic biofuels and biomass burning, volcanoes and vegetation, and wind-blown dust; sea salt being added
Asian emissions from TRACE-P (D. Streets)
T. Bond inventory for BC and OC
36-km emissions will be aggregated up from the 25-km res. Indian inventory of Reddy & Venkataraman for SO2, BC, OM, and PM2.5

34. Initial and Boundary Conditions
Initial conditions assumed to be uniform, background values for each species
Lateral boundary conditions for coarse grid derived from available seasonally averaged data from GEOS-CHEM (1998) for
9 gas-phase species: PAN, CO, isoprene, HNO3, HCHO, N2O5, HNO4, O3, and SO2
5 aerosol species: SO4, NH4, NO3, EC, and OM
Static BCs reduced due to very large values seen for most PM species on the eastern boundary

35. Results for 108-km Simulations
Results are preliminary, and somewhat qualitative due to short period simulated thus far, and the lack of observations with sufficient temporal frequency for the period
Used mainly to check model input data quality, and initial model configuration
Compared event-average (Jan 1-5, 1999) concentrations and AOD with results from LMD GCM simulation by Reddy et al., 2004, at LOA, U. of Lille

36. LMD GCM Results vs. Ronald Brown Cruise Data

37. LMD GCM and METCHEM Spatial Patterns – SO4 & BC (mg/m3)

38. LMD GCM vs. METCHEM Spatial Patterns – Fly ash & Dust (mg/m3)
BC max for the domain was ~ 43 ug/m3 in Africa
SO4 max was 88 over same location although China also had high values
METCHEM

39. Aerosol Optical Depth

40. METCHEM AOD vs. AERONET

41. Variable Grid Resolution: Emissions
36 Km Uniform Grids
36 to 4 Km Variable Grids

42. Variable Grid Resolution
Surface Ozone
Vgrid – 36 to 4 km
Ugrid – 36 km

43. Satellite Derived SST Satellite Sea Surface Temperature (Top)
NCEP Sea Surface Temperature (Bottom)
Notice the large difference in magnitude and spatial distribution

44. MM5 Wind Simulations
Case September 1, 2000 (2:00 UTC (8:00pm Local (top)); 15:00 UTC (9:00am Local (Bottom)))
Differences in wind vector simulations using satellite and NCEP SST
Diurnal variation of the wind pattern
Potential enhancement of land-sea breeze
Possible changes due to effects on cloud convection

45. Four Dimensional Data Assimilation for Air Quality Model
WRAL
This slide shows the effect of data assmilation with O3 observations aloft. Without FDDA, the model overpredicted
the O3 concentration at night. With FDDA, the modeled O3 concentration at night is much better comparing to observations
around the assimilation area. You may want to show the surface FDDA case as well.
Assimilation of aloft O3 at WRAL Tower

46. Northern Hemisphere Air Quality
Proposed Northern Hemisphere domain on a polar-stereographic projection for MM5-CMAQ applications.

47. Areas for Possible Collaboration
Model Evaluation using satellite and conventional measurements
Model Inter-comparison WRF / CMAQ
UNC has a lot of computer and software resources including ArcGIS for geographic data processing, Imagine for satellite data processing, and a lot of other visualization packages for large scale spatial data processing and displaying
Update Geographic data used in their forecasting systems
Processing emission data using geographic information. We have in-house tools for generating surrogates in emission SMOKE computation for air quality modeling.
Distribution to the community
Training
We can help them update their geographic data used in their forecasting systems. For instance, there are 30 meter resolution land use data for whole country from 1992 satellite data and now 30 meter resolution land use data are being developped for the whole 50 states. If the landuse data they are using are older or coarse, we can help they update the information.
3. Our group has been very strong in processing emission data using geographic information. We have in-house tools for generating surrogates in emission SMOKE computation for air quality modeling. If they have any needs in emission areas, we can certainly help.
4. Over the years, this group has been developing data processing tools and data management and processing systems for emission and air quality modeling. We have capabilities and experiences. We can help them in
developing tools or systems in forecasting data processing and management

48. Thank You