Project

Outline - The project will be to analyze temperature data from the UVic School Weather Network, and to submit this as a written report. Two types of data are to be considered:

a) Minute-resolution data for stations  DeepCove, DiscoveryElementary, Helgesen, JamesBay, Keating, UVicSCI, and JohnMuir

These files contain only the temperature data. at 1-minute resolution. In Matlab, these data can be read-in with the /oadcommand. E.g.

In T = load(DeepCove temperature. date

The time stamps have to be generated from the start time tt_start (1st number), end time tt end (second number) and total number of data points NT (3rd number). In Matlab this can be done as follows:

tt start = In T(1); tt end = InT(2); NT = InT(3);

tt = linspace(tt start, tt end,NT);

Note, tt_start, tt_end, tt are given as decimal days ( as discussed below). data points 4 to NT+3 contain the temperature in degrees Celsius. The time reference for these minute-resolution data sets is UTC.

Present the data, and characterize it using techniques discussed in class. Obvious things to do are just plot the time series, find means, PDFs, spectra. More involved projects will present correlations between the different stations (that's why you have data from several stations!).

What differences and patterns can you find between the stations, and can you explain using your knowledge of the local climate?

Handling dates:

Times are given in decimal days, starting on 01/01/00. In Matlab, these dates can be manipulated with the commands "datestr" and "datenum". If dis a date, then datestr(d)will return a nice string with the date.To convert a date string into a number use datenum. E.g. datenum(2017,1,1)will return 736696.0. For plotting, its OK to simply plot decimal days (say days from 1 Jan 2010), or to use datetick(x)to make date-formatted ticks.

b) Hour-resolution data (Hour-resolution data)

One-hour resolution temperature data are taken from a larger set of stations in Greater Victoria (35 stations). Data are on an even time grid, from 2008-01-01 00:00 PST to 2017-08-31 00:00 PDT, with each row representing temperature at a given time, and each column at a given station. The first two rows are the longitude (degrees E) and latitude (degrees N) of each station. The first row gives the time stamps (common to all stations). (Note: The timestamps of the minute-resolution data are given in UTC. The timestamps of the hourly data are local, PST/PDT).

First, it would be nice to replicate the map on UVic School Weather Network. Use some sort of 2-D interpolation to put the sparse data on a Ion/lat grid, and then plot. Indicate station locations. A file containing data for the coastline (2 columns: longitude, latitude) is provided here: Coast line data. Note the islands, etc are missing.

Then use this data set to look for spatial patterns of variability between the stations (i.e. calculate the Empirical Orthogonal Eigenfunctions, or Principal Components). Plot the strongest modes of variability and indicate what fraction of the variance they represent. Plotting time series of the modal amplitudes is also very effective way of thinking about the system. Again, spend a couple of paragraphs explaining the patterns you found, and indicate if those patterns might have a physical meaning. Looking at the seasonality of the amplitudes may help.

Dealing with geographic data

A data file with the coastline is given above, to be used for your maps. One thing to keep in mind is that 1 degree of latitude is 60 nautical miles (-110 km), but 1 degree of longitude is only 60*cos(lat) nautical miles, so it is useful to scale your x and y axis so the aspect ratio is [1 cos(lat)], where "lat" is some latitude that is on the center of your plot. This area is small enough that this approximation will be good enough - for larger areas you would need to choose a projection from a sphere onto a map (i.e. the "Mercator", "Azimuthal" etc projections).

The project -

The final project is to submit a written a report. This report should consist of a very brief introduction, a short description of how the data were collected, and then sections that present your observations/analysis. I'd expect that you will have around 20 plots. You should describe in the text the most important observations that you make about your data. In making these statements, hopefully you will come up with other analyses to try.

Attachment:- Assignment Files.rar