I spent a few weeks trying to accomplish this with the help of a few classmates with no success. I had the idea of using GIS to produce a map (particularly using ESRI's ArcMap software) that shows the quality of terrain in certain areas of the US for storm chasing. There are definitely some qualities about the topography in certain areas of the country that make it better or worse for chasing, and most people agree on them. The biggest ones I think of are:
-flatness of the terrain
-openness (land use)
-road network
-severe weather/tornado climatology
It was my goal to find a way to "quantify" the qualities listed above and map them to show which areas of the country are better or worse for chasing. Obviously some areas should stick out, but the true ideas behind this are 1) evaluate the suitability for chasing on a very detailed scale and 2) identify "sleeper" areas - i.e., little known areas that may remain unknown to many chasers that are actually great terrain for chasing.
I have found resources for the data needed to create such a map. They include the National Elevation Dataset, a 1 arcsecond data set containing elevation for all practical areas of the US (see http://seamless.usgs.gov/), a land cover data set with the same resolution as that of the NED (see also this site), and road shapefiles across the country (available in many locations, including www.grlevelxstuff.com and http://www.meteor.iastate.edu/~slincoln/GRS/shapefiles/).
My plan would be to set up a grid and for each point on that grid to assign the variable (for the sake of the topic, called "storm chasing index" - SCI), using the formula:
SCI = A*B*C*D, where
A represents the quality of the topography (i.e., terrain flatness)
B represents the land use
C represents the quality of the road network, and
D represents severe weather climatology,
and each of A through D are essentially normalized to range from 0 to 1, where one would define either 0 as best or worst quality for that element. The resulting value of SCI would be between 0 and 1 and that would be what is plotted.
Regarding A: Things such as the average elevation, standard deviation of elevation, and average slope across a bin of data are good mathematical ways to evaluate the flatness of the land (low standard deviation and slope would be better). Things such as the first or second partial derivative of elevation with respect to direction could also be beneficial, as it would indicate the quantity of terrain flipping, or "hilliness".
Regarding B: Given the different land use categories (e.g., 1 for water, 2 for urban, 3 for grassland), one could assign a normalized value to each category which represents the quality of chasing on that land use value (can't chase on water and chasing in urban areas would be very hard, and chasing in grasslands/prairies, or temperate forest would be easier).
Regarding C: this one would probably be the hardest to actually do, but in each grid box, one could compute the weighted sum of the length of road (the weights could be assigned based on road type: gravel road vs. two lane paved road vs. four-lane free access highway vs. four lane controlled access highway), the number of road intersections, and perhaps count the number of consecutive miles of straight road segments.
Regarding D: One cool use of ArcMap is the "density" function in the spatial analyst tool extension of the software. Simply download the tornado tracks, touchdown points, lift points, etc data from SPC's SVRGIS site and run the tool at whatever resolution you want to create a map of tornado climatology (or other severe event climatology if you wish) that can be applied to the grid.
The reason I and my partners have been unable to complete this task is because the machines we are trying to run this on are simply not capable of handling the large amount of data required to do this. The NED data comes in 1 deg lat by 1 deg lon pieces, and even those small chunks are very large (on the order of 100 MB). Thus the tools simply fail due to lack of memory. But I'm sure ArcMap is not the only GIS software out there that can do this, nor is this the only method to quantify storm chasing terrain. I'm now turning to the Stormtrack community to see if anyone knows a better way to do this or has the technology to do it as I've tried.
-flatness of the terrain
-openness (land use)
-road network
-severe weather/tornado climatology
It was my goal to find a way to "quantify" the qualities listed above and map them to show which areas of the country are better or worse for chasing. Obviously some areas should stick out, but the true ideas behind this are 1) evaluate the suitability for chasing on a very detailed scale and 2) identify "sleeper" areas - i.e., little known areas that may remain unknown to many chasers that are actually great terrain for chasing.
I have found resources for the data needed to create such a map. They include the National Elevation Dataset, a 1 arcsecond data set containing elevation for all practical areas of the US (see http://seamless.usgs.gov/), a land cover data set with the same resolution as that of the NED (see also this site), and road shapefiles across the country (available in many locations, including www.grlevelxstuff.com and http://www.meteor.iastate.edu/~slincoln/GRS/shapefiles/).
My plan would be to set up a grid and for each point on that grid to assign the variable (for the sake of the topic, called "storm chasing index" - SCI), using the formula:
SCI = A*B*C*D, where
A represents the quality of the topography (i.e., terrain flatness)
B represents the land use
C represents the quality of the road network, and
D represents severe weather climatology,
and each of A through D are essentially normalized to range from 0 to 1, where one would define either 0 as best or worst quality for that element. The resulting value of SCI would be between 0 and 1 and that would be what is plotted.
Regarding A: Things such as the average elevation, standard deviation of elevation, and average slope across a bin of data are good mathematical ways to evaluate the flatness of the land (low standard deviation and slope would be better). Things such as the first or second partial derivative of elevation with respect to direction could also be beneficial, as it would indicate the quantity of terrain flipping, or "hilliness".
Regarding B: Given the different land use categories (e.g., 1 for water, 2 for urban, 3 for grassland), one could assign a normalized value to each category which represents the quality of chasing on that land use value (can't chase on water and chasing in urban areas would be very hard, and chasing in grasslands/prairies, or temperate forest would be easier).
Regarding C: this one would probably be the hardest to actually do, but in each grid box, one could compute the weighted sum of the length of road (the weights could be assigned based on road type: gravel road vs. two lane paved road vs. four-lane free access highway vs. four lane controlled access highway), the number of road intersections, and perhaps count the number of consecutive miles of straight road segments.
Regarding D: One cool use of ArcMap is the "density" function in the spatial analyst tool extension of the software. Simply download the tornado tracks, touchdown points, lift points, etc data from SPC's SVRGIS site and run the tool at whatever resolution you want to create a map of tornado climatology (or other severe event climatology if you wish) that can be applied to the grid.
The reason I and my partners have been unable to complete this task is because the machines we are trying to run this on are simply not capable of handling the large amount of data required to do this. The NED data comes in 1 deg lat by 1 deg lon pieces, and even those small chunks are very large (on the order of 100 MB). Thus the tools simply fail due to lack of memory. But I'm sure ArcMap is not the only GIS software out there that can do this, nor is this the only method to quantify storm chasing terrain. I'm now turning to the Stormtrack community to see if anyone knows a better way to do this or has the technology to do it as I've tried.