Eun-Su Yang and Sundar A. Christopher Earth System Science Center University of Alabama in Huntsville Shobha Kondragunta NOAA/NESDIS Improving Air Quality.

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Presentation transcript:

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

 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? Outline

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

MODIS Terra: 1615Z May Dry spring in 2007 caused extensive wildfires in Georgia and Florida. Fire detection is near real time. Fires in April and May of 2007 GOES + MODIS + AVHRR

Emissions Algorithm Zhang, X and S. Kondragunta, Geophysical Research Letter, 2006 Zhang et al., Atmospheric Environment, 2008 Zhang and Kondragunta, Remote Sensing of Environment, 2008 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 Emissions (g) = burned area (ha) * fuel load (kgC/ha) * emission factors (g/kgC) * fuel consumed (%)

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

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

CMAQ simulations reproduce overall PM2.5 concentration levels. Comparison with ground-based PM2.5 observations AirNowIMPROVE

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

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

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

CMAQ reasonably well reproduces PM2.5, AOT, and CO. Summary 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.