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Air Quality Applications: GOES Aerosol Optical Depth Assimilation to Improve Air Quality Forecasts

Air Quality Applications: GOES Aerosol Optical Depth Assimilation to Improve Air Quality Forecasts. Shobha Kondragunta (NESDIS/STAR) Qiang Zhao (IMSG). Project Objectives. Assess merits and limitations of satellite data in improving air quality forecasts.

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Air Quality Applications: GOES Aerosol Optical Depth Assimilation to Improve Air Quality Forecasts

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  1. Air Quality Applications: GOES Aerosol Optical Depth Assimilation to Improve Air Quality Forecasts Shobha Kondragunta (NESDIS/STAR) Qiang Zhao (IMSG) GOES-RRR Review June 10 2010

  2. Project Objectives • Assess merits and limitations of satellite data in improving air quality forecasts. • Develop assimilation methodologies for GOES aerosol products (Aerosol Optical Depth, AOD). • Conduct assimilation runs using WRF-CMAQ and analyze the impact of GOES AOD data assimilation. • Prepare for GOES-R. • Work in tandem with NWS to transition data/tools/methodologies for operational implementation. GOES-RRR Review June 10 2010

  3. data assimilation window consisting of several analysis-forecast mini-cycles GOES AOD Data Assimilation System forecast to 48 hours forecast to 48 hours DA forecast to 48 hours AOD retrievals DA forecast to 48 hours AOD retrievals DA forecast to 48 hours AOD retrievals

  4. Assimilation No Assimilation Improved Aerosol Predictions with Satellite Data Assimilation • Assimilation (Cressman analysis) of GOES Aerosol Optical Depth (AOD) in a NOAA-EPA Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) model shows improved aerosol predictions for an east coast pollution episode. Simulation was for a 4-day period (August 2-5, 2006). Observed vs Predicted Surface PM2.5 (µg/m3) Concentrations Assimilation AOD

  5. Time Series of PM2.5 concentrations GOES AOD Assimilation MODIS AOD Assimilation • WRF-CMAQ assimilation runs are reinitialized every 24 hours with satellite AOD observations. GOES has a 10-hr observational window during which hourly mini-cycling is used. • Both GOES and MODIS assimilation improve surface PM2.5 predictions. GOES has a bigger impact than MODIS due to hourly cycling. • Loss of information around 18 UTC in base case as well as assimilation run was determined to be due to boundary layer dynamics

  6. Impact of Satellite Data Assimilation on 24-hr Average Surface PM2.5 Predictions for a Haze Episode BASE DA-GOES GOES Data not screened for outliers GOES Data screened for outliers Symbols are EPA observations and contoured fields are CMAQ predictions GOES-RRR Review June 10 2010

  7. Impact of Satellite Data Assimilation on 24-hr Average Surface PM2.5 Predictions for a Smoke Episode Base DA-GOES GOES Data not screened for outliers GOES Data screened for outliers Symbols are EPA observations and contoured fields are CMAQ predictions GOES-RRR Review June 10 2010

  8. Comparison of CMAQ and CALIPSO Vertical Profiles of Aerosols (Haze Episode) for Different Grid Locations Because assimilation tunes the aerosol concentration profile by the same ratio in all layers and aerosols were confined to the boundary layer, improvements at the surface are observed

  9. Comparison of CMAQ and CALIPSO Vertical Profiles of Aerosols (Smoke Episode) • Assimilation tunes aerosol concentrations based on CMAQ aerosol vertical profile • Because assimilation tunes the profile by the same ratio everywhere vertically, aerosol concentrations are amplified in the region where aerosols peak. Thus, aerosol concentrations near the surface can be underestimated/overestimated despite data assimilation

  10. Height-Time Cross-section of Aerosol Extinction Base GOES Data Assimilation Haze Episode GOES Data Assimilation Base Smoke Episode

  11. Improvements to Assimilation Runs CMAQ AOD currently does not include coarse mode contribution. The difference between total and fine mode AOD could be significant, especially in the central and western part of the country Test runs made to use fine mode part of the AOD observations to evaluate CMAQ model’s performance GOES-RRR Review June 10 2010

  12. Jan Feb Mar May Apr Jun Multi-Year Monthly Mean Fine Mode Fraction (Terra) Aug Sep Jul Oct Dec Nov GOES-RRR Review June 10 2010

  13. Smoke Episode Assimilation Run using total AOD Assimilation Run using fine mode AOD GOES-RRR Review June 10 2010

  14. Interactions with AWG • Model forecast output is on hourly time scale. Need to understand how to utilize 5-minute ABI retrievals. • Product tailoring is probably required. GOES-RRR Review June 10 2010

  15. Interactions with Proving Ground • Model simulations (with and without data assimilation) will be used to generate proxy data for ABI aerosol algorithms. • CRTM group already generated tables for optical properties for WRF-GOCART and GFS-GOCART models. Work is underway to prepare the tables for CMAQ. • ABI aerosol retrievals using CMAQ-CRTM generated radiances will be provided to user/focus group for feedback. GOES-RRR Review June 10 2010

  16. Challenges • Science • Lack of aerosol vertical profile information from GOES and MODIS • Lack of dust and near real time biomass burning emissions in the CMAQ model (note: new version of CMAQ with biomass burning emissions has just become available) • Logistics • Defining pathways to NCEP’s operational development (GSI analysis scheme) was not optimal but is now underway. GOES-RRR Review June 10 2010

  17. Follow-on Research: Developing Regional AQ Data Assimilation Capability with GSI • Target NEMS/NMM-B inline AQ model • Being developed at NCEP under ESMF framework • Based on CMAQ/CB05/AERO5 • Expected to be workable in about a year • We may have to generate proxy IC datasets with current MET and CMAQ model outputs for DA development • Phased approach to build up the AQ DA capability • Phase 1: Use column AOD as analysis variable. Model variables will be adjusted according to the AOD increment • Phase 2: Take direct model variables as analysis variables and connect observed AOD through observation operator • Phase 3: Similar to Phase 2, but cross species correlations will be considered in the DA formulation

  18. Developing Regional GSI-AQ (Phase 1) NMMB-AQ ICON MET+AQ MET+AQ Analysis Adjust AQ With AOD Analysis BCON MET+AQ This generic interface will be built under GOES-R risk reduction project for GOES/MODIS AOD assimilation in a GSI framework. Model AOD will be computed using CRTM. MET+AQ Forecasts MET+AOD Analysis EPA NEI AOD Retrievals Calculate AOD from AQ MET+AOD Forecasts GSI-AQ MET Observations

  19. Summary • Developed a Cressman analysis based assimilation method to demonstrate the impact of GOES AOD on CMAQ predicted surface PM2.5 predictions. • One haze episode • One smoke episode • Prepared for 3D-var assimilation • Background error • Species correlation analysis • Investigated length scales • Preparing for 3D-var assimilation in GSI • In collaboration with NCEP, methodology to build an aerosol assimilation interface has been worked out. GOES-RRR Review June 10 2010

  20. Acknowledgments • GOES-R program for risk reduction funds • NCEP for computer access and provision of CMAQ model for assimilation runs GOES-RRR Review June 10 2010

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