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

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)

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

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

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

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)

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)

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

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

CMAS Online Help Desk Model Clearinghouse

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)

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

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

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

EmisView Main GUI

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

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

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

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

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)

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.

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

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

Integrated System Solution Chart Figure 3. Integrated System Solution chart

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

CMAQ/GOES AOD Figure 2: 17 July 2004 17Z, CMAQ forecast (top left) and GOES observations (bottom left). 23 July 2004 13Z, CMAQ forecast (top right) and GOES observations (bottom right). The color bar indicates AOD ranging from 0.0 (blue) to 1.4 (red).

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.

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 (http://idea.ssec.wisc.edu/). 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)

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)

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 1995-1999, 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.

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

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

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

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

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

LMD GCM Results vs. Ronald Brown Cruise Data

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

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

Aerosol Optical Depth

METCHEM AOD vs. AERONET

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

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

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

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

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

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

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

Thank You