# Presentation: 933681

## 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**
**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 **
**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

- Model Clearinghouse

- Online Help Desk

## 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**

**Notes:**
Quality Assurance Tool for emission inventory data

## EmisView Main GUI 

## Analysis Results

- From the results table you can sort, filter, show/hide cols, format, create plots, show a description, load/save configuration, export

**Notes:**
Source Classifications Code

## 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 **
**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

**Notes:**
Figure 3. Integrated System Solution chart 

## GOES/AERONET AOD

**Notes:**
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

**Notes:**
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

**Notes:**
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

## Integrated Meteorology-Chemistry Model (METCHEM)

**Integrated Meteorology-Chemistry Model (METCHEM)**

**H & V Transport, Cloud Physics & Chemistry,  Gas/Particulate Chemistry, PM Microphysics (Modal), D****ry & Wet Removal**
**(MAQSIP CTM)**

**Met. Couple**
**(MCPL)**

**Meteorology**
**(MM5)**

**Emissions Processing**
**(SMOKE)**

**Radiative Feedback of Aerosols**

## 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**

**Notes:**
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**

**Notes:**
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 SO****2****, BC, OM, and PM****2.5**

## 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, HNO****3****, HCHO, N****2****O****5****, HNO****4****, O****3****, and SO****2**
**5 aerosol species: SO****4****, NH****4****, NO****3****, 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 (g/m3)

- LMD GCM

- METCHEM

## LMD GCM vs. METCHEM Spatial Patterns – Fly ash & Dust (g/m3)

- LMD GCM

- METCHEM

**Notes:**
BC max for the domain was ~ 43 ug/m3 in Africa
SO4 max was 88 over same location although China also had high values

## Aerosol Optical Depth

## METCHEM AOD vs. AERONET

## Variable Grid Resolution: Emissions

-  **36 Km Uniform Grids**

-   **36 to 4 Km Variable Grids**

**Variable Grid Resolution: Emissions**

## Variable Grid Resolution

**Variable Grid Resolution**

- Ugrid – 36 km

- Vgrid – 36 to 4 km

- Surface Ozone

## 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

- Assimilation of aloft O3 at WRAL Tower

- WRAL

**Notes:**
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.

## Northern Hemisphere Air Quality

**Notes:**
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**

**Notes:**
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