C ombine A erosol T rajectory T ool ( CATT ) - Links Single Site - Single Day Trajectory Concentr. Percentile Trajectories User-Defined Filter Trajectory All Sites, Single Day Trajectory Src. Contr,‘Hopke’ Single Site, Time- Range Traj Inc. Prob. -‘Poirot’Avg. Conc, ‘Kenski’Weighed Prob.-’Green’ Gridded Transport Metrics Browsers Multi-Site, Single Day Trajectory Ensemble Trajectory Browsers

Combined Aerosol Trajectory Tool, CATT Illustrated Instruction Manual Supported by: MARAMA contract on behalf of Mid-Atlantic/Northeast Visibility Union (MANE-VU) for the Inter-RPO Workgroup for Data Analysis Supplemental funding from Environmental Protection Agency, OAQPS, Agreement # National Science Foundation, Grant # Performed by the Center for Air Pollution Impact and Trend Analysis (CAPITA, Washington University In collaboration with Cooperative Institute for Research in the Atmosphere, CIRA-VIEWS Program January 10, 2004

Acknowledgements The CATT Tool is the result of an effective CIRA-CAPITA collaboration to create a sequential value-adding chain. CIRA has opened the VIEWS and the ATAD databases for use by CAPITA. In fact the current CATT ensemble trajectory browser is accessing the VIEWS database for chemical data in real time! CAPITA added the trajectory browser code and the user interface. The result is a textbook illustration of the new distributed computing paradigm! It is hoped that the values that the CATT project added to the chain will be accessed and utilized by others and continue the value-adding process. The opportunities for mutual empowerment are truly endless The functionality of CATT was strongly influenced by the dynamic infusion of ideas from Rich Poirot. Beyond setting the initial goal of the CATT-Tool project, he also supplied continuous feedback on both the initial CATT design as well as on other features that we have added for our own reasons. Serpil Kayin of MARAMA made sure that we actually finished this un-finishable 'project'. The entire DataFed/CATT code was written by Kari Höijärvi of CAPITA

Table of Contents Introduction The CATT Browser Web Page Data Query (Q) Interface (Q) Parameter Filter Location Filter Time Filter Trajectory Rendering Interface (RT) Application of Filters for Data “Slicing” Single Site, Single Day Trajectories Multi-Site, Single Day Trajectories All Visible Sites, Single Day Trajectories Limiting Trajectories by Parameter Value Single Site, Time-Range Trajectories Percentile Filter Seasonal aggregations Gridding and Grid Operators Incremental Probability Metric, IP (‘Rich Poirot’ Metric) Potential Source Contribution Function, PSCF (‘Phil Hopke’ Metric) Grid-Average Concentration Metric, DM (‘Donna Kenski’ Metric) Weighed Probability Metric, WP (‘Mark Green’ Metric) TrajAgg: User-Defined Trajectory Viewer CATT Gridding Applications

CATT Software Components and Data Flow The CATT software consists of two rather independent components: 1.Chemical filter component. This component is accomplished through queries to chemical data sets. The output of this step is a list of “qualified” dates for a specific receptor location. 2.Trajectory aggregator component. This component receives the list of dates for a specific location and performs the trajectory aggregation, residence time calculation and other spatial operations to yield a transport pattern for specific receptor location and chemical conditions.

The CATT Browser Web Page The CATT program is a standard web page accessible through a URL by any user. The CATT browser has two data views, the Map and Time views. Each view serves double purpose: to display data as maps or time series and to accept user input (clicking on Map/Time view) for navigation (browsing) To the left of each view are view-specific controls to change either the content or form of the view. The top group of controls, ViewControls relate to the entire view, the bottom group of buttons are the LayerControls and the changes depend on which active (current) layer is in the view. The general map view settings include setting the overall image size, geographic zoom rectangle (latitude-longitude), image margins and axis labels. The form, accessible through the magnifying glass – button, is considered self-explanatory. The ‘T button allows the entry of user-specified title on the map image.

View Controls and Settings

Status and Navigation Bar The File menu item is for the design of new applications. It should only be used by developers and not by routine browsers of CATT. Traj_Point which shows the value of the species at different loc and time. Trajj_Line depicts the ensamble trajectories as lines. Traj_Grid shows the gridded trajectories as shaded contours. The Layer menu, highlighted in a yellow box, is an important navigational control of CATT. It displays and allows the selection of the ‘current layer’. Most of the user interaction is confined to the current layer. In CATT, the three layers are:

Data Query (Q) Interface (Q) Chemical filter conditions determine the subset of the chemical data for which the backtrajectories are extracted, rendered, or gridded. The chemical filters fall into three major categories, filtering by parameter (e.g. SO4), location or by time. The chemical filter settings are accessible through the Query form, loaded by the query button, Q, on the right side of the map view of the Data Viewer.

Trajectory rendering options (RT) The trajectory rendering interface is accessed through the RT button, while the Traj_line layer is current.

Single day, single site back-trajectory browser Settings: param_filter = all values loc_filter = loc_code time_filter = datetime This simple CATT mode is most useful when the backtrajectory for a specific chemical data point is to be viewed. For instance, browsing the time series of a given location indicates a high value, for example at Shenandoah. Clicking on that day in the time view moves the cursor for that date and also shows the Shenandoah trajectory for that day.

Single day, multiple site back-trajectory browser Settings: param_filter = all values loc_filter = loc_code_list (user specified) time_filter = datetime This CATT mode is helpful to show the airmass histories for a set of specific sites that have unique features identified by the user. In this mode, the list of receptor locations is fixed as specified by the loc_code_list (e.g. ACAD1 SHEN1 GRSM1 UPBU1). This mode may be useful when preparing transport-illustrations for a report.

Multi-site color-coded backtrajectories for sites with SO4f data Settings: param_filter = expression expression = value > 5 loc_filter = loc_range (defined by the map view zoom rectangle) time_filter = datetime The trajectory rendering is enhanced by color-coding and by changing the line thickness of the trajectories in proportion to the magnitude of the parameter value. The trajectory rendering is set by the RT button. In this example, rainbow coloring is used.

Backtrajectories for organics OCf on the day of the Quebec smoke, ; b. Backtrajectories for a sulfate event Trajectories with receptor SO4 concentration near 0 are shown as thin blue line. Trajectories with receptor SO4 concentration over 20 ug/m3 are shown as heavier red lines. This color/thickness coding of trajectories conveys, in an intuitive way, the transport direction where ‘dirty’ and ‘clean’ air is coming from on a given day The map zoom rectangle was reset (by the magnifying glass icon), to cover the Eastern U.S. only In this mode, the list of receptor locations include all the sites that are in the zoom rectangle of the map view. Moving the location cursor in the map view will not change the selected trajectories.

Multi-site backtrajectories for a. All datab. SO4f > 20 g/m 3 Settings: param_filter = expression expression = value > 5 loc_filter = loc_range (defined by the map view zoom rectangle) time_filter = datetime The SO4 parameter was restricted to expression = value > 0 and value > 20 respectively. Imposing a value filter can eliminate ‘irrelevant’ trajectories, while highlighting those with ‘interesting’ value range (high or low).

Ensemble backtrajectories for SOILf at Big Bend, Settings: param_filter = all values loc_filter = loc_code (a single user- selected location) time_filter = datetime_range (time range set in the time view of the DATA VIEWER) The ensemble of backtrajectories to a single site illustrates the transport climatology to a specific site. Preferential airmass transport pathways are clearly evident in this view. This view shows the geographic boundary of the ATAD backtrajectories over the Gulf of Mexico. High receptor concentration trajectories are enhanced with reddish colors and thicker lines. The high SOILf concentration (red) is originating either from the SE or from the West.

High percentile of SO4f at Great Smoky Mountain site (95-100%); b. Low SO4f percentile trajectories at GRSM (0-5%) Settings : param_filter = percentiles (set at 0-20 or ) loc_filter = loc_code (a single user- selected location) time_filter = datetime_range (time range set in the time view of the DATA VIEWER) The percentile filter selects a subset of the chemical species based on the percentile (relative) values of the station concentration. This is a powerful mode of CATT since it can convey in a single view the transport direction of dirty and clean air as derived from long monitoring time series. Since it uses percentiles, it is ‘autoscaling’ the trajectory selection.

Seasonal transport, high and low percentile during summer (JJA) at GRSM Aggregations for specific seasons are intended to illustrate the transport conditions during differences of these seasons. The seasonal (actually monthly) filter is always imposed in addition to the other filter conditions (loc_filter, time_filter). The specific month to be used for the aggregation can be selected in the main query form accessible through the Q-button

Details of the Incremental Probability Metric The IP metric requires the computation of two residence time matrices: filtered and unfiltered reference matrix. The resulting IRTP matrix is simply the difference: IRTP = (Filtered Restime Matrix – Unfiltered Restime Matrix) Normalized Un-Filtered Restime matrix Normalized Filtered Restime matrix for LYBR, SO4f, 80 th percentile.

Incremental Probability map LYBR, SO4, (80%), The IRTP metric highlights the differences between the filtered and unfiltered trajectory counts by literally calculating the difference of the two normalized matrices. The resulting difference matrix, has both positive and negative values The positive, reddish areas have ‘higher than average’ probability of transport and the bluish areas ‘higher than average’ probability of transport for the selected filter conditions

Incremental Probability Metric, IP (‘Rich Poirot’ Metric) datafed.aspx?page=CATT/CATT_IPhttp://webapps.datafed.net/dvoy_services/ datafed.aspx?page=CATT/CATT_IP Settings: Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_reference_grid' param_filter = all_values use_weight = none Button ‘Q’:View: 'map'; Layers: 'Traj_Line', 'Traj_Grid'; ID='ws_data' param_filter = percentile (80-100) loc_filter = loc_code time_filter = datatime_range Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_grid' use_weight = none normalize = true Button ‘O’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_mgo' Expression = a - b

Potential Source Contribution Function, PSCF (‘Phil Hopke’ Metric) _services/datafed.aspx?page=CA TT/CATT_SChttp://webapps.datafed.net/dvoy _services/datafed.aspx?page=CA TT/CATT_SC Settings: Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_reference_grid' param_filter = all_values use_weight = none Button ‘Q’:View: 'map'; Layers: 'Traj_Line', 'Traj_Grid'; ID='ws_data' param_filter = percentile ( ) loc_filter = loc_code time_filter = datatime_range Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_grid' use_weight = none normalize = true Button ‘O’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_mgo' Expression = a / b

Grid-Average Concentration Metric, DM (‘Donna Kenski’ Metric) y_services/datafed.aspx?page= CATT/CATT_DMhttp://webapps.datafed.net/dvo y_services/datafed.aspx?page= CATT/CATT_DM Settings: Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_reference_grid' param_filter = all_values use_weight = none Button ‘Q’:View: 'map'; Layers: 'Traj_Line', 'Traj_Grid'; ID='ws_data' param_filter = all_data loc_filter = loc_code time_filter = datatime_range Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_grid' use_weight = linear normalize = true Button ‘O’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_mgo' Expression = a / b

Weighed Probability Metric, WP (‘Mark Green’ Metric) t/dvoy_services/datafed.as px?page=CATT/CATT_WPhttp://webapps.datafed.ne t/dvoy_services/datafed.as px?page=CATT/CATT_WP Settings: Button ‘Q’: View: 'map'; Layers: 'Traj_Line', 'Traj_Grid'; ID='ws_data' param_filter = all_values loc_filter = loc_code time_filter = datatime_ramge Button ‘G’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_grid' use_wight = linear normalize = true Button ‘O’: View: 'map'; Layer: 'Traj_Grid'; ID='ws_mgo' Expression = a

TrajAgg viewer Given a table of receptor locations, dates, and chemical concentration the TrajAgg tool draws the corresponding ensemble of backtrajectories or residence time contour plots. The TrajAgg page has a single map view with trajectory Traj_line and Traj_grid layers.

Submission form for the chemical filter table The user defined filter table can be submitted and edited using the form, accessible through the button E. Following the submission (saving) of the data table on the server, the TajAgg viewer automatically displays the data in trajectory or grid mode. The table consists of simple comma separated fields with the first line indicating the column names. The fields loc-code and datetime are mandatory. Such csv tables can be exported from Excel. The loc_code field has to contain location identifiers that are in the IMPROVE/STN list. The list can be viewed in the main viewer window through the drop-down box for locations. If the chemical data for this table are obtained at location other than the IMPROVE/STN site list, the user can hand-select a nearby IMPROVE/STN location for the backtrajectories

Button ‘G Reference Grid SettingsReference Grid Settings (Same as for Traj_line Query)

Button ‘G’ Filtered Grid SettingsButton ‘O’ Grid OperatorButton ‘R’ Grid Rendering

Transport of Different Species for Everglades, FL

Transport of SEf to Different Sites

Transport of NO3f to Different Sites

Sulfate Transport to BIBE, GRSM, and LBYR

Sulfate Transport Pattern on

Average Concentration of Different Species – Dkenski Metric you guess the species

OCf Concentration Field (DKenski Metric)