Performance evaluation of isoprene in ozone modeling of Houston

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Performance evaluation of isoprene in ozone
modeling of Houston

• Mark Estes, Clint Harper, Jim Smith, Weining
  Zhao, and Dick Karp
• Texas Commission on Environmental Quality
• Presentation for the CMAS Conference, October
  2008
• mestes_at_tceq.state.tx.us

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Acknowledgements

• TCEQ Air Modeling Team Doug Boyer, Pete
  Breitenbach, Bright Dornblaser, Barry Exum,
  Marvin Jones, Chris Kite, Jim MacKay, Jocelyn
  Mellberg, Ron Thomas, Zarena Post, Steve Davis.
• TCEQ Monitoring Operations

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Questions of interest

• At the isoprene monitors used to evaluate
  performance, what is the long-term behavior? How
  does the model behave during similar time
  periods?
• How much geographic variation is observed in
  these patterns, and does the modeled variation
  match observed variation?

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Methods Biogenics modeling

• Emissions model GloBEIS v3.1 biogenic emissions
  model (Yarwood et al., Guenther et al.) at 2km
  maximum resolution.
• Land cover data University of Texas Center for
  Space Research (UT-CSR) land cover data (Feldman
  et al., 2007), 30 meter native resolution.
• Vegetation data Houston Green Urban Forest
  Survey (Smith et al., 2005)
• Met data interpolated temperature data for
  local networks, and GOES-derived
  photosynthetically active solar radiation data
  (Byun et al., 2005)

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Methods Photochemical grid modeling

• CAMx v4.51, run at 4km and flexi-nested to 2km.
• MM5 v3.7.3, with ETA PBL scheme, UH GOES-derived
  sea surface temperatures, UT-CSR land cover data,
  NOAH LSM, 4km maximum resolution, analysis
  nudging on outer grids, obs nudging with profiler
  data in 4km grid, TKE Kv scheme.
• Carbon Bond 05 chemical mechanism (Luecken et
  al., 2008).
• TCEQ emissions inventory, version
  bcYYMMM.reg8_pscfv2

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Episodes of interest

• May 19 June 3, 2005
• June 17 June 30, 2005
• July 26 August 8, 2005
• May 31 June 15, 2006
• August 15 September 15, 2006 (TexAQS II field
  study intensive)
• September 16 October 11, 2006 (TexAQS II field
  study intensive)
• Total number of days of interest 96

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Example for Aug 16, 2006 Total isoprene
emissions, 1924 tons/day for whole domain 610
tons/day in Houston nonattainment area.
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Auto GC locations
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Suburban/ex-urban monitoring site
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Rural monitoring site
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Industrial monitoring site
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Finding

• Temporal patterns Modeled isoprene matches the
  diurnal and seasonal patterns of the
  measurements, but doesnt always match the
  magnitude.
• Spatial patterns Modeled isoprene appears to be
  correlated with the measured spatial patterns,
  but doesnt always match the magnitude.

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Geographic analyses

• Isoprene performance varies by site. Is the
  geographic distribution of trees correct?

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Are the trees in the right places?

• Calculate the difference between the elevations
  estimated by the Shuttle Radar Tomography Mission
  (Feb 11-22, 2000) and the elevation of the ground
  surface using the National Elevation Database
  (USGS). The difference can represent the height
  of the tree canopy.
• Calculate the Normalized Difference Vegetation
  Index (a vegetative greeness index) for a Landsat
  image of approximately the same age (1999). This
  tells where the vegetation is located.
• Identify all areas with both high NDVI and height
  of 4 to 80 meters.
• Calculate the number of tree pixels within each
  4km grid cell.
• Contrast the locations of these areas to the
  areas identified by the UT-CSR landcover data as
  forested.
• Plot isoprene emissions per grid cell vs. number
  of tree pixels per grid cell.

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UT-CSR Land Coverall categories displayed
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UT-CSR Land Coveronly forested categories
displayed
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Areas with tree canopy NDVI between x and y,
and canopy height between 4m and 80m
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Current state of these analyses
Photos by Bohne, U. Vermont
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Future work

• Further comparisons between modeled isoprene and
  TexAQS II observations (aircraft data, RHB ship
  data, Moody Tower data)
• C. Warneke analysis comparing PTRMS data aboard
  NOAA P3 aircraft to the biogenic emissions models
  GloBEIS, MEGAN, and the latest version of BEIS.
• Hyperspectral satellite data analysis to
  distinguish tree species? Aerial photography to
  assist in species identification?