Monthly Technical Report PROJECT TITLE

Development of an IDL-based geospatial data processing framework for meteorology and air quality modeling

PROJECT # 12-TN2

PROJECT PARTICIPANTS

University of Maryland (UMD), College Park Air Resources Laboratory, NOAA

DATE SUBMITTED

7/8/2013

REPORTING PERIOD

From: June 1, 2013 To: June 30, 2013

REPORT # 4

University of Maryland (UMD)

Invoice # #2

Amount $0.00

A Financial Status Report (FSR) and Invoice will be submitted separately from each of the Project Participants reflecting charges for this Reporting Period. I understand that the FSR and Invoice are due to the AQRP by the 15th of the month following the reporting period shown above. Detailed Accomplishments by Task We have collected raster data, and investigated basic computational algorithms for raster data processing. We downloaded Global Land Cover Characterization (GLCC) Version 1.2 data sets from U.S. Geological Survey (USGS) (http://edc2.usgs.gov/glcc/glcc.php), which includes Global Ecosystems, International Geosphere Biosphere Programme (IGBP), Biosphere Atmosphere Transfer Scheme, Simple Biosphere Model Scheme, Simple Biosphere 2 Model, and USGS Land Use/Land Cover Scheme data sets with global l-km resolution coverage. Algorithms for raster data processing is rather straightforward compared to vector processing algorithms which uses complicated polygon clipping algorithms. However, optimizations of raw data accessing method and pixel indexing are required to handle huge raster data efficiently. We have built partial data accessing routines for several GIS raster data formats such as Geo Tagged Image File (GeoTIFF) and ERDAS IMAGINE (.IMG) file, to avoid unnecessary access of whole data set which usually causes memory problems. We also utilized histogram reverse-indexing method from IDL histogram routine, which enables easy access of grouped pixels for given indices (e.g. target domain cell index). For the detailed description on reverse-indexing, one can refer http://www.id1coyote.com/tips/histogram tutorial.html. It also generates statistics of pixel values within each grid cell with improved speed and enhanced control of memory usage. Figure 1 shows examples of USGS LULC data processing for urban fraction and water fraction. Figure 2 also shows an example of pixel statistics of 30-m fine resolution raster data on a 4-km domain cell.

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" ". .Figure 1. Example of USGS LULC data processing for urban fraction (left) and water fraction(right)

Land Use Land Cover Distribution8000

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NLCD index30

Figure 2. 30-m NLCD land use land cover data set near Houston (left), and an example pixeldistribution from a grid cell

Preliminary Analysis

N/A

Data Collected

We have collected GLCC LULC data sets from USGS and National Land Cover Database(NLCD) from Multi-Resolution Land Characteristics Consortium (MRLC).

2

Identify Problems of Issues Encountered and Proposed Solutions of Adjustments

N/A

Goals and Anticipated Issues for the Succeeding Reporting Period

1. Extension of histogram reverse-indexing method for arbitrary-shaped polygons.2. Utilization ofNLCD data format

Detailed Analysis of the Progress of the Task Order to Date

All other Project Tasks are in schedule as presented in the Task Order.

Submitted to AQRP by:

Principal Investigator: Daniel Tong

(Printed or Typed)

3