Computation in AS&T Project Assignment
WEATHER DATA
The National Weather Service collects massive amounts of weather data every day. Those data are available to all of us on the agency's online service. Analyzing large amounts of data can be confusing, so it's a good idea to start with a small data set, develop an approach that works, and then apply it to the larger data set that we are interested in.
We have extracted precipitation information from the National Weather Service for one location for all of 1999 and stored it in a file called Weather_Data.xls. (The .xls indicates that the data are in an Excel spreadsheet.) Each row represents a month, so there are 12 rows, and each column represents the day of the month (1 to 31), so there are 31 columns. Since not every month has the same number of days, data are missing for some locations in the last several columns. We place the number - 99999 in those locations. The precipitation information is presented in hundredths of an inch. For example, on February 1 there was 0.61 inch of precipitation, and on April 1, 2.60 inches. A sample of the data is displayed in Table 3.11, with labels added for clarity; however, the data in the file contain only numbers.
|
Table 3.11 Precipitation Data from Asheville, North Carolina
|
|
1999
|
Day 1
|
Day 2
|
Day 3
|
Day 4
|
· · ·
|
Day 28
|
Day 29
|
Day 30
|
Day 31
|
|
January
|
0
|
0
|
272
|
0
|
|
0
|
0
|
33
|
33
|
|
February
|
61
|
103
|
0
|
2
|
|
62
|
-99999
|
-99999
|
-99999
|
|
March
|
2
|
0
|
17
|
27
|
|
0
|
5
|
8
|
0
|
|
April
|
260
|
1
|
0
|
0
|
|
13
|
86
|
0
|
-99999
|
|
May
|
47
|
0
|
0
|
0
|
|
0
|
0
|
0
|
0
|
|
June
|
0
|
0
|
30
|
42
|
|
14
|
14
|
8
|
-99999
|
|
July
|
0
|
0
|
0
|
0
|
|
5
|
0
|
0
|
0
|
|
August
|
0
|
45
|
0
|
0
|
|
0
|
0
|
0
|
0
|
|
September
|
0
|
0
|
0
|
0
|
|
138
|
58
|
10
|
-99999
|
|
October
|
0
|
0
|
0
|
14
|
|
0
|
0
|
0
|
1
|
|
November
|
1
|
163
|
5
|
0
|
|
0
|
0
|
0
|
-99999
|
|
December
|
0
|
0
|
0
|
0
|
|
0
|
0
|
0
|
0
|
Use the data to find the following:
a. The total precipitation in each month
b. The total precipitation for the year
c. The month and day on which maximum precipitation during the year was recorded.
d. Find the statistical mean daily precipitation for each month and the mean daily precipitation for the year, and then find the standard deviation for each month and for the year.
e. Plot the month precipitation for January, February, and March (note the those negative values should not appear in your plot)
Tip: data source is Weather_data.xls was given available in blackboard as well, you need to download the excel file, then using excel program, you need to save as csv file format, and then in octave use csvread command read (find online help to see how to use that function in your script) into octave workspace to do analysis and plotting..
When you prepare your scientific MLA format report, you need to follow the following steps:
1. State the problem
2. Describe input and Output
3. Develop a Hand example
4. Develop a MATLAB Solution
5. Test the solution
Attachment:- Project Assignment.rar