Analysis of Time Series data on Rainfall for Coonoor (v0.1 dtd 06-03-2014)
Dataset: Monthly rainfall in mm
Period: Jan 1935 to Dec 2013
Source and Credit: UPASI, Coonoor. Obtained as hard copy.
Raw data
Data entered in Excel Sheet as two column (Data, rainfall in mm) and then saved as csv. The matrix format is for ease of viewing.
Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | |
1935 |
0 |
0 |
0 |
0 |
0 |
28.5 |
65 |
143.5 |
53.1 |
272.8 |
238 |
113.5 |
1936 |
35.8 |
277.9 |
268.7 |
9.1 |
69.6 |
118.9 |
57.9 |
67.8 |
148.1 |
327.7 |
329.9 |
101.1 |
1937 |
26.7 |
172.5 |
160.8 |
206.3 |
70.4 |
89.7 |
73.4 |
59.7 |
63.5 |
302.5 |
257.1 |
73.1 |
1938 |
0 |
192 |
215.4 |
124.5 |
35.8 |
30 |
88.4 |
137.9 |
141 |
250.4 |
68.8 |
315.5 |
1939 |
104.4 |
18 |
39.4 |
246.1 |
84.3 |
116.1 |
40.6 |
62.2 |
137.4 |
259.8 |
241.5 |
46.2 |
1940 |
1.5 |
34 |
9.7 |
245.6 |
181.1 |
133.6 |
24.6 |
119.1 |
99.3 |
211.8 |
516.6 |
62.2 |
1941 |
101.9 |
116.1 |
0 |
90.9 |
129 |
127 |
39.1 |
62 |
141.7 |
123.9 |
265.7 |
92.5 |
1942 |
0 |
0 |
0 |
117.3 |
184.9 |
49.5 |
68.1 |
88.4 |
85.3 |
185.9 |
167.9 |
300.5 |
1943 |
296.4 |
77.5 |
15.7 |
149.1 |
62 |
78.2 |
49.3 |
121.4 |
80.5 |
293.6 |
241.1 |
80.3 |
1944 |
135.6 |
169.2 |
124.2 |
74.2 |
63 |
113 |
35.8 |
123.7 |
121.2 |
340.4 |
550.4 |
155.5 |
1945 |
30.5 |
20.6 |
0 |
136.7 |
55.6 |
29.5 |
115.1 |
98 |
45.2 |
318.3 |
592.3 |
11.2 |
1946 |
66.8 |
26.2 |
82.5 |
184.4 |
77.5 |
48.5 |
46.2 |
101.6 |
160.3 |
266.9 |
450.9 |
309.6 |
1947 |
223.3 |
27.7 |
160.5 |
272.8 |
72.6 |
36.3 |
7.4 |
125 |
79.5 |
106.7 |
70.1 |
306.3 |
1948 |
148.3 |
94.2 |
11.2 |
46.2 |
119.4 |
33.8 |
63.3 |
55.1 |
34.5 |
293.1 |
792.7 |
284.2 |
1949 |
0 |
0 |
0.3 |
144 |
81.5 |
29.5 |
157 |
56.4 |
82 |
297.9 |
333.8 |
0.5 |
1950 |
0 |
157.5 |
81.8 |
29.5 |
113.5 |
41.4 |
83.1 |
107.9 |
31 |
126.7 |
248.7 |
34.3 |
1951 |
103.1 |
3.1 |
171.5 |
280.7 |
103.1 |
23.9 |
79.3 |
42.4 |
231.1 |
67.8 |
423.2 |
36.1 |
1952 |
52.1 |
285 |
5.3 |
116.1 |
9.9 |
45.5 |
40.9 |
47.7 |
81.3 |
103.9 |
36.1 |
417.1 |
1953 |
141.7 |
82.5 |
134.9 |
99.3 |
60.5 |
100.1 |
139.7 |
86.9 |
70.4 |
399 |
89.7 |
46 |
1954 |
220 |
47.2 |
209.8 |
116.3 |
157.7 |
24.4 |
72.9 |
100.3 |
22.1 |
275.3 |
33.5 |
111.8 |
1955 |
122.9 |
7.1 |
52.6 |
129.3 |
110 |
24.4 |
45.5 |
27.4 |
183.4 |
280.4 |
153.4 |
160.3 |
1956 |
60.5 |
26.4 |
0.8 |
93.5 |
35.3 |
111 |
27.2 |
73.9 |
93.2 |
281.9 |
424.2 |
142.7 |
1957 |
0 |
88.1 |
96.3 |
72.6 |
354.8 |
114.3 |
53.9 |
65.5 |
67.8 |
548.6 |
459.7 |
172 |
1958 |
0 |
107.4 |
108.4 |
193.1 |
263.6 |
10 |
33.3 |
55.5 |
66.4 |
119.4 |
303.1 |
69.4 |
1959 |
10.8 |
40.4 |
3.6 |
108.7 |
162.3 |
102.7 |
113.9 |
89.1 |
115.9 |
274 |
444.6 |
173.4 |
1960 |
44 |
1 |
387.5 |
327.4 |
125.4 |
54.2 |
192 |
29.2 |
97.1 |
430.2 |
502 |
52 |
1961 |
10.6 |
76.4 |
0 |
300.3 |
124.1 |
64.4 |
142.5 |
64.8 |
193.2 |
348.3 |
452 |
43.1 |
1962 |
53.8 |
186 |
52.2 |
27.8 |
123 |
53.8 |
55.8 |
119.4 |
137.4 |
480.8 |
133.4 |
263.2 |
1963 |
130.1 |
63.4 |
235.7 |
113.9 |
49.8 |
80.8 |
58.2 |
17 |
95.5 |
243.8 |
487 |
192.8 |
1964 |
0 |
39 |
109.5 |
24.2 |
72.6 |
64.2 |
159.2 |
167.6 |
77.8 |
132.6 |
237.6 |
373.2 |
1965 |
20.8 |
204.6 |
66 |
152.2 |
58.8 |
7.2 |
64.8 |
188.2 |
27.2 |
90 |
315.6 |
417.2 |
1966 |
332 |
44.2 |
69.2 |
129.4 |
40.8 |
35.6 |
93.2 |
107 |
108.2 |
481 |
524.2 |
175.8 |
1967 |
123.4 |
0 |
4.2 |
6.2 |
103 |
59.6 |
67.6 |
47.4 |
48.4 |
295.4 |
164.8 |
174 |
1968 |
16.2 |
268.2 |
201.8 |
143.4 |
89.6 |
54.8 |
70.2 |
84.2 |
113.6 |
166 |
215.2 |
145.2 |
1969 |
0.6 |
49.6 |
2.2 |
210.2 |
59.6 |
137.4 |
79.4 |
354.6 |
20.8 |
321 |
308.4 |
227.2 |
1970 |
42.8 |
39 |
146.6 |
190 |
92.6 |
17.6 |
77 |
97.8 |
41.8 |
230.6 |
291.4 |
39.8 |
1971 |
50 |
14.6 |
280.6 |
55.2 |
147 |
12 |
59.2 |
113.4 |
156.2 |
311.4 |
196.6 |
318.4 |
1972 |
4.6 |
0.6 |
0 |
25.8 |
143.8 |
75.2 |
40.2 |
17.4 |
232.8 |
415.4 |
418.4 |
344 |
1973 |
0 |
4 |
20.4 |
21.2 |
134.2 |
114.8 |
139 |
87.8 |
82.6 |
604.2 |
247.4 |
224.9 |
1974 |
2.8 |
5.6 |
28.4 |
86 |
122.1 |
40.6 |
87 |
47.9 |
279.2 |
120.6 |
56.4 |
40.2 |
1975 |
51.6 |
26.6 |
131.6 |
76.8 |
132.4 |
106 |
145.4 |
74.2 |
264.8 |
112.2 |
140 |
30 |
1976 |
0 |
0 |
62.4 |
164 |
29 |
47.6 |
66 |
167 |
78 |
318 |
342 |
148 |
1977 |
0 |
100.2 |
49.2 |
172 |
257.2 |
28 |
43.8 |
76.8 |
244.6 |
602 |
864.6 |
6 |
1978 |
123 |
210 |
27 |
238 |
94 |
54 |
69 |
33 |
113 |
417 |
832 |
488 |
1979 |
7 |
178 |
157 |
42 |
22 |
36 |
83 |
58 |
266 |
475 |
1348 |
281 |
1980 |
0 |
2 |
59 |
147 |
85 |
48 |
36 |
48 |
85 |
237 |
534 |
34 |
1981 |
43 |
0 |
94 |
71 |
111 |
47 |
65 |
60 |
164 |
324 |
123 |
174 |
1982 |
8 |
0 |
2 |
78 |
92 |
67 |
97 |
46 |
95 |
359 |
339 |
31 |
1983 |
35 |
0 |
8 |
37 |
95 |
58 |
54 |
93 |
150 |
363 |
91 |
482 |
1984 |
298 |
285 |
395 |
53 |
44 |
54 |
92 |
20 |
264 |
238 |
150 |
142 |
1985 |
118.8 |
0 |
0.1 |
215.7 |
75.3 |
107.2 |
81.1 |
69.7 |
174.4 |
113.9 |
249.6 |
309.1 |
1986 |
74.8 |
224.2 |
99.8 |
75.8 |
30.3 |
55.1 |
52.5 |
23.4 |
167.5 |
234.2 |
112.9 |
315.9 |
1987 |
56.4 |
36.1 |
55.5 |
16.6 |
122.1 |
41.2 |
8.7 |
61 |
106.4 |
357 |
340.3 |
536.4 |
1988 |
16.6 |
24.3 |
26.4 |
162.5 |
105.9 |
15.6 |
204.4 |
112.6 |
182.5 |
97.5 |
193.3 |
192.9 |
1989 |
16.6 |
0 |
204.6 |
140.6 |
34.4 |
45.7 |
131.4 |
41.2 |
164 |
147.4 |
292 |
171.8 |
1990 |
233 |
0 |
70.4 |
43 |
84.9 |
25.1 |
41.6 |
76.8 |
47 |
807.7 |
375.1 |
125.7 |
1991 |
124.4 |
5.3 |
61.4 |
55.2 |
22.2 |
176.1 |
101.8 |
93.6 |
158.6 |
280.6 |
536.3 |
21.3 |
1992 |
47.1 |
0 |
0 |
104.8 |
94.5 |
106.7 |
48.6 |
71.1 |
104.4 |
176.8 |
936.4 |
65.1 |
1993 |
0 |
0 |
82.8 |
6.9 |
75.6 |
74.8 |
102.8 |
109.6 |
33.2 |
309.8 |
1060.2 |
265.3 |
1994 |
52.5 |
86 |
31.2 |
176.6 |
70.7 |
35.8 |
80 |
38.8 |
128.5 |
497.4 |
593.2 |
54.3 |
1995 |
193.1 |
11.1 |
195.4 |
137.5 |
78.4 |
50.7 |
110.4 |
148.7 |
62.5 |
209.4 |
179 |
8 |
1996 |
8.1 |
99.4 |
49.1 |
162 |
79.7 |
67.5 |
83.8 |
90.9 |
182.2 |
423.2 |
255.3 |
553.6 |
1997 |
119.2 |
0 |
39 |
33.6 |
151.8 |
81 |
114 |
74.6 |
133.1 |
680.2 |
528 |
279.6 |
1998 |
51.2 |
11.7 |
3.2 |
36.5 |
55 |
58.3 |
107.7 |
201.1 |
87.8 |
169.3 |
214.4 |
667.5 |
1999 |
1.9 |
264.1 |
11.1 |
113.8 |
47.4 |
21.6 |
80.4 |
54.6 |
106.7 |
603.4 |
431.3 |
150.8 |
2000 |
47.9 |
243.7 |
0 |
72.6 |
114.9 |
40.7 |
22.7 |
162 |
242.5 |
140 |
419.9 |
153.1 |
2001 |
54.1 |
8.6 |
6 |
326.3 |
62.1 |
69.4 |
49.8 |
106.2 |
131.4 |
185.3 |
502 |
372.9 |
2002 |
41.9 |
19.2 |
14.2 |
55.4 |
136.9 |
15.4 |
75.2 |
63 |
51.5 |
530.2 |
547.7 |
51.8 |
2003 |
0 |
49.3 |
246.1 |
197.9 |
65.2 |
68.8 |
122.6 |
93.7 |
51.5 |
386.3 |
365.2 |
33.5 |
2004 |
118.9 |
52.9 |
4 |
78.9 |
646.2 |
47 |
96.2 |
36.9 |
391.1 |
654.2 |
505.9 |
10.9 |
2005 |
24.2 |
25.7 |
107 |
233.6 |
45.2 |
92.2 |
137.5 |
56.7 |
117.6 |
228.7 |
558.4 |
103.9 |
2006 |
79.9 |
0 |
142.8 |
111.8 |
109.8 |
55.3 |
36.3 |
67.5 |
160.2 |
755.1 |
578.6 |
91.4 |
2007 |
26.6 |
140.4 |
45 |
68.9 |
86.1 |
111.4 |
85.4 |
145.4 |
121.4 |
392.8 |
107 |
284.4 |
2008 |
13.1 |
396.9 |
391.2 |
104.1 |
86.1 |
81.7 |
109.7 |
232.1 |
27 |
541.6 |
109 |
75.6 |
2009 |
5.3 |
0 |
160.1 |
40.7 |
99.9 |
34.7 |
93 |
178.6 |
33.4 |
131.5 |
1164.3 |
190.6 |
2010 |
65.9 |
2.2 |
0 |
36.5 |
170.2 |
79.3 |
151.8 |
76.9 |
166.7 |
217.3 |
499.3 |
188.5 |
2011 |
30.2 |
195.7 |
16.7 |
219.8 |
62.6 |
106.7 |
80.4 |
100.3 |
110.1 |
625.3 |
442 |
117.6 |
2012 |
36.5 |
6 |
70.1 |
95 |
44.9 |
53.1 |
29.5 |
109.9 |
35.6 |
750.2 |
0 |
0 |
2013 |
6.2 |
48.8 |
126 |
106.7 |
134.7 |
52.2 |
51.6 |
67.5 |
173.9 |
211.4 |
272.5 |
119.5 |
This was converted into a single column list of only rainfall data (rain_list_full.csv) which was used for analysis.
Code in R
rf <- read.csv(“rain_list_full.csv”)
rfts <- ts(rf, start=c(1935,1), end=c(2013, 12), frequency=12)
plot(rfts)
rfstl <- stl(rfts[,1], s.window = “periodic”)
plot(rfstl)
str <- StructTS(rf[,1],frequency = 5, type =“level”)
Graphs and Interpretation
Graph 1 – Raw Data
Graph 2: stl output
The above output shows that the seasonality component is not very strong although there seems to be a regular pattern in it. The trend component is even less significant. The remainder seems to explain a lot of the variation in the rainfall data (It is significant). (Is there some method to examine the remainder to decompose it further? How do we find El Nino type of larger time scale events in this. From a planning point of view, how do we understand the variation in rainfall over time?)
Graph 3: structTS output
Need help in interpreting this. At the commandline the structTS yielded this output
Call:
StructTS(x = rf[, 1], type = “level”)
Variances:
level epsilon
3.925e-01 2.328e+04