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Using SOM Clusters to Study U.S. Ozone Profile Variability and Pollution

This study uses Self-Organizing Map (SOM) clusters to analyze the variability of U.S. ozone profiles and their linkages to pollution. By examining ozone mixing ratio data collected from ozonesonde profiles, the study identifies different cluster profiles and their associations with pollution, meteorological features, and surface air quality. The study also explores the impact of ozone pollution on U.S. air quality standard violations.

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Using SOM Clusters to Study U.S. Ozone Profile Variability and Pollution

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  1. Using Self-Organizing Map (SOM) Clusters to Create Ozonesonde-based Climatologies and Characterize Linkages among U.S. Ozone Profile Variability and Pollution Ryan M. Stauffer1, A. M. Thompson2, G. S. Young3, S. J. Oltmans4,5, B. J. Johnson5 1Earth System Science Interdisciplinary Center (ESSIC), University of Maryland 2Earth Sciences Division, NASA Goddard Space Flight Center 3Department of Meteorology, The Pennsylvania State University 4Cooperative Institute for Research in Environmental Sciences, University of Colorado 5NOAA Earth System Research Laboratory, Global Monitoring Division QOS 2016 – Edinburgh, U.K. 11:15 am 6 September, 2016

  2. Self-Organizing Maps (SOM) • Neural network (data set represented by “nodes”) developed by Kohonen (1995) • In our studies – used as a clustering tool applied to ozonesonde O3 mixing ratio profile data • Many user-selectable options for profile data (see Stauffer et al., 2016a – JGR) • Altitude Range (Surface – 12 km vs. Surface – 6 km) • Number of SOM algorithm iterations (fine-tuning) R. Stauffer QOS 2016

  3. Past SOM Results – Jensen et al. (2012) SHADOZ Ozonesonde data (1998 – 2009) from Ascension Island and Natal, Brazil (surface – 15 km amsl O3 mixing ratio data) Clusters of O3 profiles reveal “nominal” O3 (cluster 1), clean/convective (cluster 3), pollution (cluster 4), and anomalous profile shapes (cluster 2) Jensen et al. (2012) Fig.3 http://croc.gsfc.nasa.gov/shadoz/ R. Stauffer QOS 2016

  4. U.S. Ozonesonde Sites (Stauffer et al., 2016a – JGR) • 4530 total O3 profiles • ~Weekly launches, some frequency increases for campaigns • Confined region (6º latitude separation), diverse geography

  5. Each U.S. Site’s 9 Cluster, Surface – 12 km O3 SOM Figure 4, Stauffer et al. (2016a) SOM cluster means for each site shown. Note UT/LS variability (1, 2, 3); pollution variability (7, 8, 9) Similar SOM organization for all sites – convenient visualization R. Stauffer QOS 2016

  6. Narrowed Focus: Trinidad Head, CA, Surface – 6 km SOM Thin Layers of Pollution Clean, Baseline O3 Amounts Figure 2, Stauffer et al. (2016b) Trinidad Head, CA: Lower-tropospheric O3 laminae evident. Can we discover more links to meteorological features, pollution sources, and surface air quality?

  7. ERA-Interim 500 hPa Heights Corresponding to SOM Clusters Figure 4a, Stauffer et al. (2016b) Low O3 amounts under varying synoptic meteorology. Indications of both subtropical influence (1) and effects from large-scale troughs (7) on clean profiles. Downstream of ridge – very favorable for high O3.

  8. ERA-Interim 500 hPa Heights April – May July - August Pollution Effects on Profiles? – AIRS (Aqua) CO Anomalies 700 hPa CO Anomaly (ppbv) Figure 2/8b, Stauffer et al. (2016b) Examining April – May and July – August profiles separately reveals two distinct factors affecting cluster 9 profiles (Stratosphere-to-troposphere exchange vs. pollution) R. Stauffer QOS 2016

  9. Links to U.S. O3 Air Quality Standard Violations • Examine surface O3 at three high-elevation (> 1 km amsl), “background” O3 sites, all downwind of Trinidad Head ozonesonde site • We are interested in frequency of U.S. O3 air quality standard violations (>70 ppbv 8-hr average) corresponding to O3 profile clusters

  10. Links to U.S. O3 Air Quality Standard Violations Historical frequency of O3 standard violations on day of cluster number (Cleaner site) Tropospheric O3 profile shapes are closely linked to surface O3 pollution R. Stauffer QOS 2016

  11. Summary and Take Home • SOM is a viable alternative method for describing O3 profile variability • Surface – 12 km O3 profile clusters capture UT/LS variability, pollution effects • SOM focused on surface – 6 km altitude range reveals meteorological drivers, and links to surface air quality/pollution • Frequent observations of high O3 laminae at Trinidad Head, CA -> stratosphere-to-troposphere exchange, pollution, synoptic-scale meteorological connections • Tropospheric O3 profile shape (cluster number) is closely associated with surface O3 pollution. This relationship makes local pollution controls difficult R. Stauffer QOS 2016

  12. Acknowledgments/Select References NASA Funding: NNG05G062G (NASA Aura Validation Program), NNX10AR39G (NASA DISCOVER-AQ Project), NNX11AQ44G (NASA Air Quality Applied Science Team), and NNX12AF05G (NASA SEAC4RS Project) • Stauffer, R. M., A. M. Thompson, and G. S. Young (2016), Tropospheric ozonesonde profiles at long-term U.S. monitoring sites: 1. A climatology based on self-organizing maps, J. Geophys. Res., doi:10.1002/2015JD023641. • Stauffer, R. M., A. M. Thompson, S. J. Oltmans, and B. J. Johnson (2016), Tropospheric ozonesonde profiles at long-term U.S. monitoring sites: 2. Links between Trinidad Head, CA, profile clusters and inland surface ozone measurements, submitted JGR • Jensen, A. A., A. M. Thompson, and F. J. Schmidlin (2012), Classification of Ascension Island and Natal ozonesondes using self-organizing maps, J. Geophys. Res., 117, D04302, doi: 10.1029/2011JD016573. • Kohonen, T. (1995), The Basic SOM, in Self-Organizing Maps, pp. 77–130, Springer, New York. • Newchurch, M. J., M. A. Ayoub, S. Oltmans, B. Johnson, and F. J. Schmidlin (2003), Vertical distribution of ozone at four sites in the United States, J. Geophys. Res., 108(D1), 4031, doi:10.1029/2002JD002059. Thank you! R. Stauffer QOS 2016

  13. Extras

  14. Talk Roadmap • Brief introduction to self-organizing map (SOM) clustering • Previous results (Jensen et al., 2012), motivation for our studies • Paper 1 (Stauffer et al., 2016a – JGR) • Clustering USA Surface – 12 km amsl ozonesonde data • Key Results: 1) Seasonality of various O3 profile shapes, 2) tropopause height and UT/LS O3 variability, 3) pollution impacts • Paper 2 (Stauffer et al., 2016b – in review JGR) • Clustering Surface – 6 km amsl O3 data from coastal Trinidad Head, CA, USA site (site subject to intercontinental pollution transport) • Key Results: 1) Pollution and stratospheric O3 laminae, 2) links to meteorology and surface air quality, 3) satellite pollution signatures R. Stauffer QOS 2016

  15. CONUS O3 Monthly Climatology

  16. 3x3 SOM (9 Clusters) Wallops Island, VA Notice slight variation among neighboring clusters (e.g. 1 – 3), and large differences among disconnected clusters (e.g. 7 and 3). An advantage of SOM over other clustering algorithms R. Stauffer QOS 2016

  17. Seasonality of SOM Clusters Histograms of launch months within each cluster. Seasonality of CONUS O3 profile shapes often unclear R. Stauffer QOS 2016

  18. SOM Algorithm – 2D Visualization 1) Initialize SOM nodes (PCA or random) 2) Find closest node for each vector (X), called best matching unit (BMU). These are the node’s member vectors 3) Update nodes with average of member vectors and other nodes’ vectors based on the neighborhood function 4) Repeat process with many iterations (neighborhood learning reduces – SOM converges)

  19. Past SOM Results – Jensen et al. (2012) JGR SHADOZ Ozonesonde data (1998 – 2009) from Ascension Island Clusters of O3 profiles at Ascension Island and Natal can be linked to meteorology and other drivers: Stability, convection, biomass burning We consider suitability of SOM to cluster mid-latitude ozonesonde profiles Do we find similar links between O3 profiles and meteorology, chemistry, etc.? High O3 Amounts Stability Biomass Burning Source Effects Jensen et al. (2012) Fig. 9

  20. SOM Ozonesonde Example: Surface – 12 km Data SOM assigns similarly-shaped O3 profiles to clusters R. Stauffer QOS 2016

  21. Node/Tropopause Height Relationship Note Total Column O3 is generally inversely related to tropopause height. >400 Dobson Units in Node 3!

  22. Node/Tropospheric Column O3 Relationship Climatology able to describe node 1 – 3 O3 below the tropopause, but not 7 and 9 (~40% of all profiles!)

  23. CONUS O3 Clusters – Comparisons with Climatology Day-to-day changes to O3 profile can be extreme in mid-latitudes • Pollution from fires, anthropogenic activity, etc. • Atmospheric dynamics: STE, see figure  With such variability, how reliable and descriptive are simple averages (i.e. the monthly O3 climatology)? • Satellite algorithms and chemical model output are validated with and use O3 climatology as “first guesses” • Compare SOM O3 profile clusters against monthly climatologies at each site Wallops Island, VA, Profiles Stratosphere-to-Troposphere Exchange (STE) R. Stauffer QOS 2016

  24. Node Comparisons w/ O3 Climatology O3 is 2x climatological values at 8 – 12 km in nodes 1 – 3 Deviations from climatology also evident in nodes 7 and 9

  25. Trinidad Head, CA, Seasonality Summer months at Trinidad Head: Clean, background O3 profiles (nodes 1 and 7) frequently observed. Polluted profiles (nodes 3 and 6) – large intraseasonal variability

  26. Linking ozonesonde profile clusters to surface O3 data in CA Examine surface O3 at three high-elevation (> 1 km amsl), “background” O3 sites, all downwind of Trinidad Head ozonesonde site

  27. Surface O3 Monthly Climatology • Yosemite and Truckee nominally more polluted than Lassen Volcanic (~8 – 10 ppbv O3 lower in summer)

  28. SOM Cluster/Yosemite Surface O3 Relationship Surface Sonde Average daily surface O3 corresponding to each sonde cluster. Generally excellent agreement between sonde and surface O3 Discrepancies in clusters with low sonde O3

  29. SOM Surface O3 Anomalies Compare surface O3 corresponding to sonde clusters with surface O3 climatology +5 – 10 ppbv O3 associated with polluted clusters 3 and 6 + O3 anomalies last for 4 days

  30. Global Ozonesonde Site Candidates Prototype site locations with sufficient data record lengths Measurements from these stations (including additional SHADOZ sites) will be compared against CTM output (e.g. GMI)

  31. SHADOZ Tropical SOM Preview Java – Tropical West Pacific High tropopause, convectively active (tropical S-shape profiles) Reunion – Subtropical Indian Ocean Near Subtropical Jet, frequent STE and UT/LS variability

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