Jenna_Assignment_1

20 bins: 30 bins: 50 bins: What is the best number of bins, why? 30 appears to be the best number of bins because it gives accurate locations of the peaks without the histogram looking too busy and cluttered. 20 bins: 30 bins: 50 bins:
 * Part 1: Create 1D Histogram and the Associated PDF **
 * 1) Choose 1 variable that is interesting to you, and create a histogram of that variable.
 * 1) Using the same variable create a PDF (normalized).
 * || Loop || Matlab ||
 * Mean || 26.9981 || 26.9910 ||
 * Variance || 79.3304 || 79.0519 ||
 * Skewness || 0.2903 || 0.2934 ||
 * Kurtosis || 2.4586 || 2.4555 ||

Part 2: Scatter Plot, 2D Histogram/PDF, covariance, and marginal distributions
Try scatterhist, or marginhist.m for octave users (you may have to google and download it).
 * 1) Choose 2 (or more) variables that are interesting to you. One can be the variable used in part 1. Create a scatter plot of the 2 variables against each other.

Some additional tools are at http://mpo581-hw2.wikispaces.com/Multivariate+display+tools Play with the size and number of bins. What is best, and why? Label the colorbar or contours with appropriate units
 * 1) Make a 2D histogram of the same data in the scatter plot of Part 2, Question 1.
 * 1) Normalize your 2D histogram so that it is a true joint PDF (as in Part 1 Question 2).


 * 1) Compute covariance from the 2D PDF using by looping over the PDF bins, as in Eq. 3.27 and Fig. 3.16, p53-54 in the book.

My covariance calculated over a loop: 2.4853 MATLAB calculated covariance: 3.1829 0.0247 0.0247 0.0012

Part 3: Study a conditional sample

 * 1) I will be examining the area within 15km of the storm center.
 * 2) Wind speed, SLP, and dew point are examined below:















Part 4: Scientific Interpretation
1. From the 2D histogram of wind speed and radius, it is clear that there is a maximum of high wind speeds at 14-15km away from the center of the storm. This tells us that the storm at that point has the beginnings of an eyewall around 15km away from the center, as the majority of the wind speeds closer to the center are very low for a hurricane.

2. In the 2D histogram of dew point and radius the highest peak is at 15km and ~295K, showing that the lowest dewpoints in the eye of the storm were forecast to occur about 15km out, with the majority of the rest of the eye seeing relatively higher dewpoints.

Obviously, there is something wrong with the data and I suspect it has something to do with the RAD_KM variable. Here is a simple plot of both RAD_KM and SLP using the same model run used in all the data above:



It is clear from the above image that the RAD_KM and SLP are off center from each other. Another model run shows the following:



This shows that the RAD_KM data is even more far off in this model run.