1 / 12

Eun-Su Yang and Sundar A. Christopher Earth System Science Center

Improving Air Quality Forecasts using CMAQ with Satellite-Derived Fire Emissions. Eun-Su Yang and Sundar A. Christopher Earth System Science Center University of Alabama in Huntsville Shobha Kondragunta NOAA/NESDIS. NSSTC Data Assimilation Workshop May 5, 2009. Outline.

kuniko
Download Presentation

Eun-Su Yang and Sundar A. Christopher Earth System Science Center

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Improving Air Quality Forecasts using CMAQ with Satellite-Derived Fire Emissions Eun-Su Yang and Sundar A. Christopher Earth System Science Center University of Alabama in Huntsville Shobha Kondragunta NOAA/NESDIS NSSTC Data Assimilation Workshop May 5, 2009

  2. Outline • Overview of modeling system • GA/FL fires in 2007 • Fire emissions estimated from satellite data • CMAQ simulations with and without fire emissions for PM2.5 and AOT. • Air quality forecast ability: how fast?

  3. MM5/SMOKE/CMAQ AREA, MOBILE, POINT, BIOGENIC MIMS MCIP SMOKE:Emission Inventory Model meteorological inputs MM5 validation with satellite and ground-based measurements: PM2.5, AOT, CO, … CMAQ local emission rates IOAPI fire emission rates GOES MODIS AVHRR Air Quality Index Forecast Good Moderate Unhealthy for Sensitive Group Unhealthy Very Unhealthy Hazardous SMOKE: Sparse Matrix Operator Kernel Emissions MCIP: Meteorology-Chemistry Interface Processor MIMS: Multimedia Integrated Modeling System I/O API: Input/Output Applications Programming Interface

  4. Fires in April and May of 2007 MODIS Terra: 1615Z May 22 2007 GOES + MODIS + AVHRR http://rapidfire.sci.gsfc.nasa.gov/realtime/2007142 Dry spring in 2007 caused extensive wildfires in Georgia and Florida. Fire detection is near real time.

  5. Emissions Algorithm Emissions (g) = burned area (ha) * fuel load (kgC/ha) * emission factors (g/kgC) * fuel consumed (%) • Inputs • MODIS Vegetation Property-based Fuel System (MVPFS) (NASA MODIS) – NESDIS product • Fire location and size (NOAA GOES) – NESDIS product • Fuel moisture category factor (NOAA AVHRR) – NESDIS product • Emissions factors – Literature • Outputs • PM2.5, CO, NOx, NMHC, etc. emissions in tons/hour in near real time Zhang, X and S. Kondragunta, Geophysical Research Letter, 2006 Zhang et al., Atmospheric Environment, 2008 Zhang and Kondragunta, Remote Sensing of Environment, 2008

  6. PM2.5 simulations Wildland fires enhance PM2.5 concentrations in AL and MS.

  7. PM2.5 concentrations due to fires PM2.5 with fires minus PM2.5 without fires Wildland fires affect air quality far downwind of fire source regions.

  8. Comparison with ground-based PM2.5 observations AirNow IMPROVE CMAQ simulations reproduce overall PM2.5 concentration levels.

  9. Aerosol Optical Thickness (AOT) simulations MODIS AOT is explained by CMAQ with local (most regions) and fire (GA and AL) emissions.

  10. CO simulations +1.0* +0.0 *CMAQ [CO] < AIRS [CO] CMAQ simulations reproduce AIRS CO, but CMAQ CO is systematically lower than AIRS CO.

  11. One day forecast 5 mins SMOKE:Emission Inventory Model meteorological inputs MCIP MM5 30 mins 20 mins 5 mins CMAQ local emission rates IOAPI 30 mins 30 mins 5 mins fire emission rates 30 mins Air Quality Index Forecast GOES MODIS AVHRR Computing time on MATRIX (32 processors) = 30 + 5 + 30 mins ≈ 1 hour. Forecasts depend on how fast we update fire emissions.

  12. Summary • CMAQ reasonably well reproduces PM2.5, AOT, and CO. • Local emissions contribute to air quality degradation even during fire season. • Fire emissions are dominant near and downwind of fire regions. • One-day CMAQ simulations require about one hour of computing time on MATRIX.

More Related