A Community Approach to the Development and Applications of Environmental Decision Support Systems

1. 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 • 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 • 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

15. EmisView Main GUI

16. Analysis Results • 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 ComponentsPosted 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. 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 CMAS Team

23. Modeling Research • Updates on the CMAQ • 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

25. GOES/AERONET AOD

26. CMAQ/GOES AOD

27. MODIS AOD/PM 2.5

28. PM 2.5 and MODIS AOD

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

31. Modeling Domains 108-km 36-km

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) LMD GCM METCHEM

38. LMD GCM vs. METCHEM Spatial Patterns – Fly ash & Dust(mg/m3) LMD GCM 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 Sea Surface Temperature (Top) NCEP Sea Surface Temperature (Bottom) Notice the large difference in magnitude and spatial distribution Satellite Derived SST

44. 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 MM5 Wind Simulations

45. Four Dimensional Data Assimilation for Air Quality Model WRAL Assimilation of aloft O3 at WRAL Tower

46. Northern Hemisphere Air Quality

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

48. Thank You