NASA Air Quality Remote Sensing Training LADCO, Madison, Wisconsin March 12 – 15, 2011 Model and Satellite comparison overview R. Bradley Pierce NOAA/NESDIS/STAR.

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Presentation transcript:

NASA Air Quality Remote Sensing Training LADCO, Madison, Wisconsin March 12 – 15, 2011 Model and Satellite comparison overview R. Bradley Pierce NOAA/NESDIS/STAR airborne surface network

Outline I.Air Quality Modeling Overview II.How can satellite retrievals be used for AQ model verification? TEXAQS II Satellite/Model Intercomparison August 2006 Importance of Averaging Kernels  Tropospheric Emission Spectrometer (TES) O3, CO  Measurements Of Pollution In The Troposphere (MOPITT) CO Emission Verification  Ozone Monitoring Instrument (OMI) NO2 Transport Verification  Atmospheric Infrared Sounder (AIRS) CO  Moderate Resolution Imaging Spectroradiometer (MODIS) AOD NOAA P3 airborne insitu LADCO Case Study September 1-6, 2011 Transport Verification  Atmospheric Infrared Sounder (AIRS) CO  Moderate Resolution Imaging Spectroradiometer (MODIS) AOD Vertical Structure Verification  Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) III. Summary

Input Output Meteorological Prediction Chemical Aerosol Prediction Air Quality Modeling Overview EPA Community Multiscale Air Quality (CMAQ) Modeling System

Input Output Meteorological Verification Airborne Validation EPA Community Multiscale Air Quality (CMAQ) Modeling System

Input Output Chemical Aerosol Verification Airborne Validation EPA Community Multiscale Air Quality (CMAQ) Modeling System

RAQMS has been used to support airborne field missions [Pierce et al, 2003, 2007, 2008], develop capabilities for assimilating satellite trace gas and aerosol retrievals [Pierce et al., 2007, 2008, Fishman et al., 2008, Sunita et al., 2008] and assess the impact of global chemical analyses on regional air quality predictions [Song et al., 2008, Tang et al., 2008] 1.Online global chemical and aerosol assimilation/forecasting system 2.UW-Madison hybrid  coordinate model (UW-Hybrid) dynamical core 3.Unified stratosphere/troposphere chemical prediction scheme (LaRC-Combo) developed at NASA LaRC 4.Aerosol prediction scheme (GOCART) developed by Mian Chin (NASA GSFC). 5.Statistical Digital Filter (OI) assimilation system developed by James Stobie (NASA/GFSC) Model Description Meteorological Data Chemical/Aerosol Retrievals 6 hourly 3-D gridded chemistry/meteorology predictions Real-time wildfire emissions GEIA emission inventories Data Assimilation RAQMS Online chemistry unified strat/trop chemistry/aerosols

2006 TexAQS / GoMACCS Texas Air Quality Study / Gulf of Mexico Atmospheric Composition and Climate Study Satellite PlatformInstrumentsSome key data productsVertical Resolution NASA AuraTESCO, O3Trop. column/4 km OMIO3, NO2Trop. column NASA AquaMODISAerosol optical depthTrop. column AIRSO3,COUTLS NASA TerraMOPITTCOTrop. column MODISAerosol optical depthTrop. column NASA CALIPSOCALIOPAerosol backscatter ratioTrop. vertical profile NASA EOS satellites and their AQ measurement capabilities

Presented during the TEXAQS II TCEQ Rapid Science Synthesis

TES Vertical Sensitivity: Averaging Kernel Rinsland et al., GRL, Vol 33, L22806, doi: /2006GL027000, 2006 High thermal contrast between surface and atmosphere – higher sensitivity Low thermal contrast between surface and atmosphere – lower sensitivity The retrieval at 215mb is sensitive to average CO in middle and upper troposphere The retrieval at 825mb is sensitive to average CO in lower troposphere The averaging kernels broaden and overlap under conditions with low thermal contrast – less Degrees of Freedom (DOF)

Boundary layer and upper tropospheric CO and O3 enhancements over Houston (30 o N)

TES provides less information on vertical structure of O3 and CO then RAQMS. Need to convolve RAQMS with Averaging Kernel for valid comparisons. Column comparisons are most appropriate.

Measurements Of Pollution In The Troposphere (MOPITT) Combines a Pressure Modulated Radiometer (PMR) and Length Modulated Radiometer (LMR). Using PMRs and LMRs with different pressures provides information about the vertical profile.

RAQMS Column CO vs MOPITT RAQMS CO vs NOAA P3 RAQMS mean CO column is low relative to V2 MOPITT RAQMS median O3 profile is high relative to P3 measurements Beware of potential retrieval biases! (MOPITT V2 retrieval has high bias relative to insitu that has been corrected in V3)

DOMINO version 2.0 ( Ozone Monitoring Instrument (OMI)

OMI Tropospheric NO2 CONUS August, 2006 RAQMS Tropospheric NO2 CONUS August, 2006 RAQMS Trop NO2 vs OMI RAQMS NO2 vs NOAA P3 RAQMS mean trop NO2 column is low relative to OMI RAQMS median NO2 profile is high relative to P3 measurements But RAQMS doesn’t capture high insitu values, which would lead to mean low biases

OMI NO2 retrievals are valuable for emission verification and compare favorably with columns estimated from insitu airborne profiles. TES and MOPITT CO retrievals are valuable for model transport verification but the vertical sensitivity of the retrievals and potential biases must be accounted for. Now look at utility of combining Atmospheric Infrared Sounder (AIRS) CO and Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD)

AIRS and MODIS August 2006 analysis produced with the Giovanni online data system, developed and maintained by the NASA GES DISC ( Atmospheric Infrared Sounder (AIRS) Moderate Resolution Imaging Spectroradiometer (MODIS) Total Column CO (10 18 mol/cm 2 ) Aerosol Optical Depth (AOD)

McMillan, W. W., et al. (2010), An observational and modeling strategy to investigate the impact of remote sources on local air quality: A Houston, Texas, case study from the Second Texas Air Quality Study (TexAQS II), J. Geophys. Res., 115, D01301, doi: /2009JD AIRS vs TES CO Averaging Kernels (Houston, TX) AIRS provides less information about boundary layer CO then TES

AIRS 500mb CO MODIS AOD 08/24 08/26 08/28 08/30 08/31 AIRS provides better spatial coverage (swath) then TES (nadir) AIRS provides better spatial coverage (cloud cleared) then MODIS (cloud free) McMillan, W. W., et al. (2010), An observational and modeling strategy to investigate the impact of remote sources on local air quality: A Houston, Texas, case study from the Second Texas Air Quality Study (TexAQS II), J. Geophys. Res., 115, D01301, doi: /2009JD

Combining Atmospheric Infrared Sounder (AIRS) CO and Moderate Resolution Imaging Spectroradiometer (MODIS) AOD:  improves verification of model transport since AIRS provides better spatial coverage (cloud clearing)  provides better estimates of boundary layer transport since MODIS is sensitive to boundary layer and free tropospheric aerosols Now apply the same verification tools to the LADCO September 01-06, 2011 Case Study

Madison East, WI South Bend, IN Port Huron, MI Lansing, MI LADCO Case Study: September 1-6, 2011 (High O3/PM2.5 episode)

IDEA AOD Trajectory Forecast

IDEA Composite (AIRNow, WF-ABBA, MODIS, GFS 850mb Winds)

Little Rock, AR Maywood2, IL Likely Scenario Agricultural fires in lower Mississippi River valley led to poor AQ (Little Rock, AR) Northward transport led to moderate AQ in Great Lakes region (Maywood, IL)

RAQMS Surface CO Analysis 08/31- 09/06

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AK(RAQMS) vs AIRS CO column August 31 – September 06, % +25%

RAQMS vs AIRS AOD August 31 – September 06, % +25%

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) CALIPSO flies as part of the Aqua satellite constellation (or A-Train), which consists of the Aqua, CloudSat, CALIPSO, PARASOL, and Aura satellite missions. Aqua CALIPSO PARASOL CloudSat Aura

CALIPSO LADCO Daytime Observations 09/01

Attenuated Backscatter Vertical Feature Mask Region of interest: 0-5km Alabama to Wisconsin CALIPSO LADCO Daytime Observations 09/01

RAQMS underestimates aerosol extinction over AL and TN (A) and misses lofted aerosols above 3km (B) (A) (B)

Summary OMI NO2 retrievals are valuable for emission verification and compare favorably with columns estimated from insitu airborne profiles  see follow-on lecture and activity on NO2 Regridding TES, MOPITT and AIRS CO retrievals are valuable for model transport verification but the vertical sensitivity of the retrievals must be accounted for.  TES and MOPITT have some sensitivity to boundary layer CO enhancements  AIRS is primarily sensitive to mid-tropospheric (500mb) CO  TES provides profile curtain (no swath) MODIS AOD retrievals are valuable for both emission verification (wildfires) and transport.  Combining with AIRS CO improves verification of model transport since AIRS provides better spatial coverage (cloud clearing) CALIPSO provides aerosol extinction curtain (no swath) that complements MODIS AOD (column)

 These tools could be expanded to include AIRS, OMI, MODIS, and CALIPSO data for CAMX verification at LADCO Summary (cont) IDL Tools to interpolate CAMX model fields to MOPITT and TES L2 retrievals have been developed and tested using TCEQ CAMX simulations. Would be interested in working with CAMX modelers to apply tools to LADCO case studies!!