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

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 O 3 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) 2 R. Stauffer QOS 2016

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

U.S. Ozonesonde Sites (Stauffer et al., 2016a – JGR) total O 3 profiles ~Weekly launches, some frequency increases for campaigns Confined region (6º latitude separation), diverse geography

Each U.S. Site’s 9 Cluster, Surface – 12 km O 3 SOM 5 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 Figure 4, Stauffer et al. (2016a)

Narrowed Focus: Trinidad Head, CA, Surface – 6 km SOM 6 Trinidad Head, CA: Lower-tropospheric O 3 laminae evident. Can we discover more links to meteorological features, pollution sources, and surface air quality? Thin Layers of Pollution Clean, Baseline O 3 Amounts Figure 2, Stauffer et al. (2016b)

ERA-Interim 500 hPa Heights Corresponding to SOM Clusters Low O 3 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 O 3. Figure 4a, Stauffer et al. (2016b)

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

Links to U.S. O 3 Air Quality Standard Violations 9 Examine surface O 3 at three high- elevation (> 1 km amsl), “background” O 3 sites, all downwind of Trinidad Head ozonesonde site We are interested in frequency of U.S. O 3 air quality standard violations (>70 ppbv 8-hr average) corresponding to O 3 profile clusters

Links to U.S. O 3 Air Quality Standard Violations 10 R. Stauffer QOS 2016 Tropospheric O 3 profile shapes are closely linked to surface O 3 pollution Historical frequency of O 3 standard violations on day of cluster number (Cleaner site)

Summary and Take Home SOM is a viable alternative method for describing O 3 profile variability – Surface – 12 km O 3 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 O 3 laminae at Trinidad Head, CA -> stratosphere-to- troposphere exchange, pollution, synoptic-scale meteorological connections – Tropospheric O 3 profile shape (cluster number) is closely associated with surface O 3 pollution. This relationship makes local pollution controls difficult 11 R. Stauffer QOS 2016

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 SEAC 4 RS 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: /2015JD 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: /2011JD 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: /2002JD Thank you! R. Stauffer QOS 2016

Extras 13

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 O 3 profile shapes, 2) tropopause height and UT/LS O 3 variability, 3) pollution impacts Paper 2 (Stauffer et al., 2016b – in review JGR) – Clustering Surface – 6 km amsl O 3 data from coastal Trinidad Head, CA, USA site (site subject to intercontinental pollution transport) – Key Results: 1) Pollution and stratospheric O 3 laminae, 2) links to meteorology and surface air quality, 3) satellite pollution signatures 14 R. Stauffer QOS 2016

CONUS O 3 Monthly Climatology 15

3x3 SOM (9 Clusters) Wallops Island, VA 16 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

Seasonality of SOM Clusters 17 Histograms of launch months within each cluster. Seasonality of CONUS O 3 profile shapes often unclear R. Stauffer QOS 2016

SOM Algorithm – 2D Visualization 18 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)

Past SOM Results – Jensen et al. (2012) JGR 19 SHADOZ Ozonesonde data (1998 – 2009) from Ascension Island Clusters of O 3 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 O 3 profiles and meteorology, chemistry, etc.? Jensen et al. (2012) Fig. 9 High O 3 Amounts Stability Biomass Burning Source Effects

SOM Ozonesonde Example: Surface – 12 km Data 20 SOM assigns similarly-shaped O 3 profiles to clusters R. Stauffer QOS 2016

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

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

CONUS O 3 Clusters – Comparisons with Climatology 23 Wallops Island, VA, Profiles Stratosphere-to- Troposphere Exchange (STE) Day-to-day changes to O 3 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 O 3 climatology)? Satellite algorithms and chemical model output are validated with and use O 3 climatology as “first guesses” Compare SOM O 3 profile clusters against monthly climatologies at each site R. Stauffer QOS 2016

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

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

Linking ozonesonde profile clusters to surface O 3 data in CA 26 Examine surface O 3 at three high- elevation (> 1 km amsl), “background” O 3 sites, all downwind of Trinidad Head ozonesonde site

Surface O 3 Monthly Climatology 27 Yosemite and Truckee nominally more polluted than Lassen Volcanic (~8 – 10 ppbv O 3 lower in summer)

SOM Cluster/Yosemite Surface O 3 Relationship 28 Average daily surface O 3 corresponding to each sonde cluster. Generally excellent agreement between sonde and surface O 3 Discrepancies in clusters with low sonde O 3 SondeSurface

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

Global Ozonesonde Site Candidates 30 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)

SHADOZ Tropical SOM Preview 31 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