Title: Development of IDL-based geospatial data processing framework for meteorology and air quality modeling
Institution(s) Represented: University of Maryland - Daniel Tong
Lead PI: Daniel Tong
AQRP Project Manager: Gary McGaughey
TCEQ Project Liaison: Bright Dornblaser
Awarded Amount: $69,985.00
Abstract
Development of IDL-based geospatial data processing framework for meteorology and air quality modeling
This project investigates basic computational algorithms to handle Geographic Information System (GIS) data and satellite data, which are essential in regional meteorological and chemical modeling. It develops a set of generalized libraries within a geospatial data processing framework aiming to process geospatial data more efficiently and accurately. The tool can process GIS data both in vector format (e.g., ESRI shapefiles) and raster format (e.g., GEOTIFF and IMG) for any given domain. Processing speeds will be improved through selective usages of polygon-clipping routines and other algorithms optimized for specific applications. The raster tool will be developed utilizing a histogram reverse-indexing method that enables easy access of grouped pixels. It generates statistics of pixel values within each grid cell with improved speed and enhanced control of memory usage. Spatial allocating tools that use polygon clipping algorithms require huge computational power to calculate fractional weighting between GIS polygons (and/or polylines) and gridded cells. To overcome the speed and computational accuracy deterioration issues, an efficient polygon/polyline clipping algorithm is crucial. A key for faster spatial allocation is to optimize computational iterations in both polygon clipping and map projection calculations.
The project has the following specific objectives: (A) To develop an optimized geospatial data processing tool that can handle raster data format and vector data format with enhanced processing time and accuracy, for any given target domain. (B) To collect and to process sample GIS and satellite data. Applications will include a spatial regridding method for emissions and satellite data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD), the Ozone Monitoring Instrument (OMI), and the Global Ozone Monitoring Experiment (GOME)-2 NO2 column data. (C) To perform an engineering test with processed fine resolution LULC data.
Executive Summary: projectinfoFY12_13\12-TN2\12-TN2 Executive Summary.pdf
Work Plan: projectinfoFY12_13\12-TN2\12-TN2 Work Plan.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 Mar 2013 MTR.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 Apr 2013 MTR.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 May 2013 MTR.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 Jun 2013 MTR.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 Jul 2013 MTR.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 Aug 2013 MTR.pdf
Technical Report(s): projectinfoFY12_13\12-TN2\12-TN2 Sep 2013 MTR.pdf
QAPP: projectinfoFY12_13\12-TN2\12-TN2 QAPP.pdf
Final Report: projectinfoFY12_13\12-TN2\12-TN2 Final Report.pdf
Publications & Citations
The project team presented at the Community Modeling and Analysis System (CMAS) Conference in October 2013. Presentations: "HCHO and NO2 column comparisons between OMI, GOME-2 and CMAQ during 2013 SENEX campaign (21 slides)" Hyun Cheol Kim, Li Pan, Pius Lee, Rick Saylor, and Daniel Tong Posters: Fine-scale comparison of GOME-2, OMI and CMAQ NO2 columns over Southern California in 2008" Hyun Cheol Kim, Sang-Mi Lee, Fong Ngan, and Pius Lee
