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Max Wiosna 05/09/11 GIS-Dr. John Deleo & Dr. Nolan Atkins. Analysis of a Spring-time Snowstorm: April 1 st , 2011. Surface Analysis. Abstract.

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max wiosna 05 09 11 gis dr john deleo dr nolan atkins
Max Wiosna

05/09/11

GIS-Dr. John Deleo & Dr. Nolan Atkins

Analysis of a Spring-time Snowstorm: April 1st, 2011

abstract
Abstract
  • An analysis of snowfall data was conducted from data taken from NWS offices in the Northeast. With the help of the elevation data from the National Elevation Dataset at the USGS, elevation dependency was analyzed. Multiple interpolation methods were used to contour or shade snowfall data and overlay it over the elevation raster of the Northeast. Lower snowfall amounts were commonly found in valley locations, as was higher snowfall amounts over the higher terrain. It was found that contouring the snowfall data, and overlaying it on the elevation raster, worked better to see contrasting snowfall amounts in locations where the elevation changed rapidly.
project description
Project Description
  • The analysis of a spring-time Northeast snowstorm, and how snowfall totals differed in areas of sharp elevation gradients.
  • Thesis Statement: Locations that received snowfall from this storm, where the elevation changed sharply from one point to the next, should see a wide range in snowfall totals.
data aquisition
Data Aquisition
  • NWS snowfall data-
    • http://www.erh.noaa.gov/box/pns.shtml
  • Northeast Elevation data-
    • http://seamless.usgs.gov/website/seamless/viewer.htm
  • U.S. cities-
    • Shapefile taken from Hail lab
  • U.S. states-
    • Shapefile taken from Hail lab
methods data analysis techniques used
Methods/Data Analysis Techniques Used
  • Converting snowfall data from an excel spreadsheet to a Shapefile
  • Manipulating Elevation data
  • Interpolation methods-
    • Spline
    • IDW
    • Natural Neighbor
    • Kriging
  • Shading/Contouring
  • Labeling and choosing color tables
snowfall data conversion
Snowfall Data Conversion
  • In excel there was a column for city, snowfall, lat, lon, and state.
  • Once all the data was inputted, I had about 410 data points.
  • Afterwards, go into Arc Catalog and right click on the folder your data is in. Then go to new and click on shapefile.
  • Then make sure the feature is a point and that the projection was projected coordinate system, world, and then WGS 1984 since it was lat and lon data attached to each point.
manipulating elevation rasters
Manipulating Elevation Rasters
  • So first I got all the elevation Raster’s piece by piece.
  • Then I combined them all in Arc Catalog.
  • To do that you need to go to: Toolbox->Data Management Tools->Raster->Raster Dataset->Mosaic.
  • Afterwards you just put all of your Raster’s together into one.
  • Just set number of bands to 1 and make it 32-bit.
interpolation methods explained
Interpolation Methods Explained
  • Spline-
    • Instead of averaging, it fits a stretchable surface based on the values of the points.
    • Its good for getting estimated values above the min and max.
    • It isn’t good when you have two points close by with extreme differences.
  • IDW- Inverse Data Weighted method
    • One size fits all method-it basically averages out all the points in the dataset.
    • Works best for a dense data set with lots of points.
    • Only negative about it is that it can’t interpolate above or below the min and max value in the dataset.
  • Natural Neighbor-
    • The value of each cell is a weighted average of all the points in the cell.
    • The weight is proportional to each point in the cell.
    • What you get is a Raster.
  • Kriging-
    • Like IDW, the surface is obtained through a weighted average of all the points, but kriging weights each and every point to determine how much correlation each has in the cell.
    • Much more math involved then any other method.
slide11

IDW Output:

IDW3- Power = 5, Points = 24

IDW2- Power = 0.5, Points = 24

IDW1- Power = 2, Points = 12

slide12

Kriging Output

Kriging1- Universal, Linear, 12 Points

Kriging2- Universal, Linear, 24 Points

natural neighbor
Natural Neighbor

Output Cell Size: 0.00623

contouring natural neighbor
Contouring Natural Neighbor

Contour Interval: 1 inch

Input Raster: Your Natural Neighbor Interpolation of your snowfall data

Labeling: Go to properties, go to labels, pick the field contour

concluding comments
Concluding Comments
  • So, in all three areas, there were degrees of ranging snowfall amounts across widely varying terrain.
  • Some were more pronounced then others, but the signal was still there.
  • Other areas were very difficult to explain due to possibly being too far away from the storm, or possibly being shadowed.
  • Southern New England showed more of a signal than Northern New England.
  • Overall thesis statement was somewhat validated through the areas and locations shown.