Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Combined Aerosol Trajectory Tool CATT Indicates the origin of air masses for specific aerosol condition MANE-VU & MRPO Tools in support of Inter-RPO Data Analysis Workgroup CAPITA Trajectory Aggregator – User Defined Filter Kitty – Simple Combined Aggregator • Back trajectory coming from one site CATT – Combined Aerosol Trajectory Aggregator Differing TrajPoint weight combinations • These were all created using the CATT Aerosol Trajectory Aggregator Fine Particle (PM 2.5) – Decreasing concentration limit Combined Aerosol Trajectory Tool (CATT) Example: Airmass origin for high (2.5*average) nitrate Boundary Waters Doly Sods Lye Brook Smoky Mtn. Triangulation indicates nitrate source in the corn belt CATT Tool User Interface CATT: Single web page Map View: Superimposed GIS layers Rendering Zoom, Pan, Point Color, Size, Visibility Layer Controls: Location selector Parameter selector Filter conditions Direction of Dust Origin at 5 IMPROVE Sites High ‘dust’ concentration at 5 sites indicate the same airmass pathway from the tropical Atlantic NOAA ARL ATAD Weather Serv. Ad hoc Data Processing Value Chain ATAD Traject Gebhart (2002) Upper Air Data PMF Tool Pareto (2001) PMF “Sources” NPS-CIRA Coutant (2002) IMPROVEData CATT Tool Aggregation Husar (2003) Poirot (2003) SQL Queries SELECT Lat as lat, Lon as lon, Loc_Code as loc_code, SUM(ResTime) AS [VALUE] FROM dTrajResTime WHERE ([Date] IN (SELECT datetime FROM dSourceApp WHERE (Loc_Code = 'loc_code') sql_filter_clause)) GROUP BY GridCode, Lat, Lon, Loc_Code ORDER BY Lon ASC Settings that are unique to a specific query are designated by red text Query (filter) result: List of dates the satisfy the chemical filter conditions Trajectory and Residence Time Grid Residence time and ATAD trajectory data superimposed for June 1, 2000. Residence time aggregate (sum) for a range of dates Airmass Source Regions by Season e.g. Sum Residence Time for Loc=LYBR, Date between June-Sept Lye Brook, DJF Gr Smoky Mtn, JJA Lye Brook, JJA Gr Smoky Mtn, JJA Seasonal Incremental Probability Year DJF MAM JJA SON Secular Changes: 1988-94; 1995-2000 1988-2000 1994-2000 1988-1994 Transport Probability Metrics • The transport metric is calculated from two residence time grids, one for all trajectories and another for trajectories on selected (filtered days). Both residence time grids are normalized by the sum of all resdence times in all grid cells: pijf=rij/SS rij pija=rij/SS rij • pijf, is the filtered and pija is the unfiltered residence time probabilitiy that an airmasses passes through a specific grid. There is a choice of transport probaility metrics: • The Incremental Residence Time Probability (IRTP) proposed by Poirot et al., 2001 is obtained by subtracting the chemically filtered grid from the unfiltered residence time grid, IRTP = pijf - pija • The other metric is the Potential Source Contribution Function (PSCF) proposed by Hopke et al., 19xx which is the ratio of the filtered and unfiltered residence time probabilities, PSCF = pijf / pija Transport Metric Selection • Currently, there is a choice of two different transport probability metrics: • Incremental Residence Time Probability (IRTP) proposed by Poirot et al., 2001 is the difference between the chemically filtered and unfiltered residence time probalbilities. Positive values of IRTP in a grid indicates more than average liekihood of transport; (red); negative IRTP values (blue) represent less than average likeihood of transport. • Potential Source Contribution Function (PSCF) proposed by Hopke et al., 19?? is computed as the ratio of the filtered and unfiltered residence time probabilities. Higher values of PSCF is indicative of inreased source contribution. • Greens Metric, Clustering….etc • The desired metric is selected through a dialog box invoked by clicking on the right-most button in the TRAJ_CHEM layer. Incremental Transport Probability Results Combined Aerosol Trajectory Tool (CATT) CATT Presentation and Workgroup Discussions Project Status/Summary Phase II Completed 1. Relational Database of PMF/UNMIX and trajectory data: Complete 2. Develop specific SQL filtering and aggregation queries • Chemical filtering/aggregation: Developed • Trajectory filtering/aggregation: Developed • Paired Chemical/Trajectory data: Developed, needs user input, testing, feedback 3. Graphic interface for user input (query) and for data output: Developed, needs user input, testing, more feedback Not There! Further Analysis When? Where? The new CATT: A Community Tool! GIS Part of an Analysis Value Chain Grid Processing Emission Why? There! Comparison How? AEROSOL Collection IMP. EPA Aerosol Sensors Integration VIEWS Aerosol Data CATT-In CAPITA Integrated AerData AerData Cube Aggreg. Aerosol Next Process CATT Weather Data Gridded Meteor. Assimilate NWS TRANSPORT Traject. Data Trajectory ARL TrajData Cube CATT-In CAPITA Aggreg. Traject. Next Process New CATT Components: Chemical filter CATT-In CAPITA Accomplished through queries to chemical data sets. AerData Cube Aggreg. Aerosol The output a list of “qualified” dates for a specific location (s). CATT TrajData Cube CATT-In CAPITA Aggreg. Traject. Trajectory aggregator Receives the list of dates for a specific location Performs the trajectory/restime aggregation Yield transport pattern for specific receptor location and chemical filter conditions. Trajectory Aggregator Tool Inputs: – – – – – Receptor location. Single location; multiple receptors; weighed multi-site Receptor times. Time range for each site Temporal filter/weight conditions. Date range; specific dates; weights for each date Trajectory input files. Pre-computed or on the fly calculated (e.g. HYSPLIT, ATAD etc) Trajectory aggregation metrics. Endpoint counts, residence time, incr. probability TAT Output: – – XMLGrid, GIS layers, ASCII point Rendered contour images of transport metric Project Extension Proposal to MANE_VU: Extensions to the CATT Analysis Tool 1. Flexible connectivity to chemical datasets, especially to VIEWS 2. Flexible import of pre-calculated back-trajectory data 3. Additional chemical filtering and trajectory aggregation algorithms 4. Spatial interpolation and rendering (contouring) of point data 5. CATT server hardware and software installation and maintenance CATT Extension 1: VIEWS and other Aerosol Datasets Background: A combined aerosol trajectory aggregation tool, CATT, has been developed for easy aggregation and browsing of airmass histories associated with specific aerosol conditions. The CATT tool, is now supported (MANEVU/MRPO) for 16 stations and for pre-calculated residence times. Proposal: We propose to extend the CATT tool by allowing the use of the entire VIEWS chemical database and other remotely accessible datasets to set the aerosol filter conditions. Implementation and Status: The technologies or remotely accessing the VIEWS and other databases and performing chemical filtering queries to VIEWS has been tested for feasibility. This extension to CATT has was option on the Phase II contract but funds were not available. Husar will lead this effort in close cooperation with B. Schichtel of CIRA/VIEWS. Deliverables: The main deliverable of this sub-project is a fully functioning CATT aerosol transport analysis tool applicable to the entire VIEWS database as well as to other chemical/trajectory data pairs. Extending CATT to VIEWS Current MANEVU/MRPO Project Prepared by Battelle and Sonoma Tech. Inc. Proposed Extension to VIEWS ‘On the fly trajectory aggregation for 150+ VIEWS sites (supported by EPA- CATTTAT) 16 sites 180+ VIEWS sites over the entire US; 100+ ‘species’ Typical Source Profiles 15 ‘species’ CATT Tool Implementation • Further information about CATT is available , including tesing. • The tool displays the incremental transport probability for specified query conditions Query Conditions Combined Aerosol Trajectory Tool - CATT CATT Extension 2: Trajectory Data Connectivity Background: The extended CATT tool should be applicable to an evolving list of datasets. Each new dataset consists of an aerosol monitoring dataset and a companion trajectory dataset. Thus, for each new CATT dataset, the trajectory data will be submitted to the CATT system separately. Proposal: We propose the development of a procedure for easy import of pre-computed trajectory data into the CATT trajectory database. At a minimum, the trajectory import facility will include ATAD and HYSPLIT trajectory formats. Implementation and Status: The trajectory import facility does not exists. In the past, trajectory import to CATT was executed through special one-of-a kind program. The new, semi-automated import facility will facilitate data transfer through FTP and subsequent import to CATT. The task will be executed by the CAPITA database/network manager. Deliverables: The main deliverable of this sub-project is a functioning import facility for ATAD and HYSPLIT and possibly other data formats. CATT Extension 3: Filtering and Aggregation Algorithms Background: CATT is an exploratory tool to examine the relationship between aerosol pattern and the associated transport conditions. An n evolving set of algorithms that evaluates that relationship. Proposal: We propose to develop a flexible framework for adding new aerosol filtering and trajectory aggregation algorithms. The initial algorithms will include, incremental probability, residence time ratio, aerosolweighed residence time, trajectory clustering, followed by emerging new algorithms. Some schemes will be implemented in multi-receptor mode. Implementation and Status: Currently, the incremental probability and residence time ratio algorithms are implanted in the CATT tool. Deliverables: Extension 3 of the CATT tool will implement the aerosol-weighed residence time, trajectory clustering algorithms. At least two additional new filtering/aggregation schemes will be added to the tool. CATT Extension 4: Spatial Interpolation and Rendering Background: A tool for transforming point measurement data to a continuous surface would aid the analysis of CATT transport as well as VIEWS chemical data. In particular, gridding/contouring will allow comparison with other data from monitoring, emissions, satellite as well as with model results/ Proposal: We propose to incorporate spatial interpolation and rendering facilities into the CATT tool. The Gridder/Contourer tool will be applicable to the spatial datasets from CATT, VIEWS as well as other spatial point data. Implementation and Status: The gridder/contourer technology has been developed at CAPITA over the past 15 years. Stefan Falke, who did his PhD dissertation on spatial interpolation will lead the effort. Deliverables: The deliverable of this sub-task is is a web-based Gridder/Contourer incorporated in CATT. The outputs from the Gridder/Contour will be suitable for import into GIS and other processing/rendering software. Spatial Interpolation and Rendering Site Code 1534 1538 1535 1540 108 107 1142 1135 1137 1143 102 1144 1138 1140 88 89 2388 2380 2396 2392 2393 2397 83 2410 2394 Lat Lon 21.3291 21.3966 21.3102 20.7808 20.8086 19.4309 61.533 61.2066 61.1822 61.5341 63.7233 63.7258 64.8411 58.3592 41.56 42.5519 42.4319 44.5883 44.6156 44.0263 43.8338 44.9432 48.0065 45.5182 44.0538 -158.093 -157.972 -157.858 -156.446 -156.282 -155.258 -150.25 -149.821 -149.814 -149.032 -148.968 -148.963 -147.72 -134.509 -124.086 -124.059 -123.346 -123.274 -123.092 -123.084 -123.035 -123.006 -122.973 -122.967 -122.938 VALUE Spatial Interpolation Settings 4.60 4.10 4.00 6.50 4.09 2.78 4.90 5.10 5.90 5.40 4.56 5.80 7.40 5.20 Point Rendering PM25 Conc. 6.75 8.70 6.10 7.20 9.00 8.80 7.80 5.70 9.30 The spatial interpolation operator transforms a data table to a gridded map using nearby measurement data. The gridding and contouring of the selected data is done ‘on the fly’ by the user Contour Rendering Grid PM25 PM25 Conc. Interpolate Gridding: Accounting for Data Density The advanced spatial interpolation algorithm accounts for the variations in point data density inherent in air quality monitoring networks. The resulting interpolated estimates are more representative of the urban and rural differences in air pollutant concentrations. Low Point Data Density High Point Data Density Rendering: Rectangular, Contour, Lines A flexible display rendering of gridded data will be implemented to accommodate different applications. The flexibility enables clearer presentation and easier comparison with other data. Rectangular Grid Cells Contoured Grid Cells Contoured Grid Lines CATT Extension 5: CATT Server Maintenance Background: The web-based CATT tool needs to reside on a web server, consisting of three parts: (1) SQL server that manages the relational database for chemical and trajectory data (MS SQL Server); (2) General-purpose web server to respond to HTTP queries (MS IIS); (3) CATT analysis server for the computation and presentation of CATT analysis. Proposal: This task covers the installation and maintenance of the CATT web-delivery delivery system (SQL Server, Web Server, CATT Server) for one year. Implementation and Status: The server hardware and software used in Phase II CATT project is currently operational. For this CATTextension, all 3 servers will be upgraded to some extent, including the hardware. The CATT system will be maintained by Kari Hojarvi at CAPITA.. Deliverables: A web-based CATT tool will be upgraded, installed and maintained for one year by CAPITA staff. CATT Server Hardware and VIEWS Connectivity Auxiliary Data VOYGER Browser CAPITA Server Data Replication CATT server connected to CIRA and CAPITA VIEWS & Auxiliary Data CATT Data Replication Software Replication Analysis & Aux. Data Viewer Analysis Tools & Delivery Analysis Server VIEWS Viewer CIRA Server VIEWS Data VIEWS Business Logic