SO503 Honors Modern Methods
Spring 2013
Lab 4: Wind and Storm Surge
Call function in external file to make a stick/tadpole plot.
Create a table.
Calculate regression statistics.
There are two Matlab m files tadpole.m. and sticks.m. These must go into the same directory, and you cannot rename sticks.m Sticks.m was found with a quick google search.
Get the following data. You should insure that you get data in Local Standard time. You should also only use data for the last week in January (20-31), unless you want the additional complexity of having to come up with a time measure that spans two months.
Thomas Point light wind data at http://www.ndbc.noaa.gov/station_page.php?station=tplm2 . You want the wind speed and direction, and the barometric pressure (there will be additional data).
Annapolis tide gauge data at http://tidesandcurrents.noaa.gov/data_menu.shtml?stn=8575512%20Annapolis,%20MD&type=Tide+Data . You want the predicted level and the preliminary water level, in meters, every 6 minutes, with the MLLW datum. The file will have two measurements from the primary and backup gauges.
You can do any required manipulations of the data file in either Matlab or Excel , but the requirements listed below must be done in Matlab and your code must show how they were done. This data set is relatively short, but your program should be prepared to handle a much larger data set.
General Matlab tips and resources. See particularly Manipulating ASCII data files
Due Mon 11 Feb at 0800 hours in the Blackboard dropbox, in a single Word Document:
A stick diagram showing the winds for the period 20-31 January 2013.
A graph showing the relationship between the tide residual and the barometric pressure.
Compute the correlation coefficient between barometric pressure and wind speed.
Prepare a table showing the time, wind speed, and direction, for all those times when the wind exceeded 15 m/s. Design your Matlab code so that it would be easy to change this value if the wind speeds were much different.
Discussion of the following points, as a coherent written discussion:
What the correlation coefficient means in this case.
What other variables you have which you think would have a higher correlation coefficient, and what you would have to do to compute the correlation coefficient.
What the sticks diagram tells you about the wind, and if this offers an improvement over what we did with the GPS data in Excel, Matlab, and the GIS program.
An appendix with your Matlab code, with enough comments for someone to understand you (like you if you have to come back to this kind of problem next fall).