Ministry of Earth Sciences
PARLIAMENT QUESTION: ACCURACY OF CYCLONE FORECASTS
Posted On:
05 FEB 2026 11:50AM by PIB Delhi
The year-wise analysis of the accuracy of cyclone forecasts, including track, intensity, and landfall, issued by the India Meteorological Department (IMD) for the period 2016–2025 is provided in Annexure-1.
There has been significant improvement in cyclone forecast accuracy during the last decade due to the continuous upgradation of observations, analysis, and prediction tools & techniques, improvements in numerical modeling, including enhanced data assimilation, higher resolution, improved physics, warning products generation and dissemination, etc. There is an improvement in track forecast accuracy by 20 to 25%, landfall and intensity (Maximum Sustained Wind- MSW) forecast accuracy by 35 to 45% in the recent five years (2021-2025) compared to the previous five years (2016-2020).
The latest data on deaths due to cyclones in the State/UT-wise during 2014-2023, as available from the National Crime Records Bureau (NCRB), Ministry of Home Affairs (MHA), is given in Annexure-2 along with the number of cyclones making landfall in India (last row). The early warnings by the IMD and the timely action taken by the Government (Central & State) have significantly reduced the loss of life due to cyclones in recent times.
IMD’s cyclone forecasting and warning system is distinguished by its high accuracy in track and intensity prediction, achieved through the use of state-of-the-art numerical weather prediction models, multi-model ensemble, advanced data assimilation techniques, and continuous monitoring using satellites, Doppler Weather Radars (DWRs), ocean buoys, coastal observational networks and finally the in-house developed Decision Support System (DSS) for the generation forecasts and warnings.
In order to further improve the monitoring, forecasting, and dissemination of warning infrastructure, the Government of India has launched Mission Mausam in early 2025, which aims to expand and modernise India’s weather observation network and forecasting systems. This includes increasing the number of weather stations, upgrading radar networks, and using machine-learning and modern models to improve forecasting accuracy, with coherent support from High Performance Computing Systems (HPCSs) and intelligent Decision Support Systems (DSSs).
This information was submitted by Minister of State ( Independent Charge) Earth Sciences Dr. Jitendra Singh in Rajya Sabha on 5th February 2026.
Annexure-1
Annual average track forecast errors (km) during 2016-2025:
|
Year
|
12-hr
|
24-hr
|
36-hr
|
48-hr
|
60-hr
|
72-hr
|
84-hr
|
96-hr
|
108-hr
|
120-hr
|
|
2016
|
59.7
|
96.1
|
129.6
|
185.1
|
238
|
291.7
|
330.4
|
379.5
|
344.1
|
438.3
|
|
2017
|
43.7
|
61.4
|
87.2
|
107.6
|
190.1
|
189.6
|
292.5
|
304.2
|
158.7
|
159.7
|
|
2018
|
55.4
|
87.5
|
99.2
|
124.2
|
131.2
|
134.3
|
165.8
|
189
|
220.8
|
247.6
|
|
2019
|
41
|
68.6
|
87.8
|
103.7
|
120.4
|
148.6
|
177.7
|
217.8
|
261.3
|
337.5
|
|
2020
|
50.3
|
72.5
|
76.4
|
85.3
|
89.1
|
111.4
|
105.5
|
88.8
|
86.3
|
93.3
|
|
2021
|
43.7
|
62.9
|
82.6
|
91.4
|
105.7
|
164
|
248
|
15.3
|
|
|
|
2022
|
42.3
|
77.5
|
108
|
167.1
|
204.2
|
315.3
|
378.2
|
535.3
|
576.5
|
|
|
2023
|
48.3
|
76.5
|
98.4
|
120.7
|
138.8
|
147.2
|
157.3
|
176.8
|
181.5
|
224.8
|
|
2024
|
37.6
|
65.6
|
76.9
|
83.5
|
100.3
|
114
|
70
|
153
|
|
|
|
2025
|
42
|
80
|
102
|
120
|
169
|
204
|
245
|
129
|
|
|
Annual average intensity forecast errors (kt) during 2016-2025:
|
Year
|
12-hr
|
24-hr
|
36-hr
|
48-hr
|
60-hr
|
72-hr
|
84-hr
|
96-hr
|
108-hr
|
120-hr
|
|
2016
|
4.6
|
7.2
|
8.5
|
8.3
|
9.7
|
11.2
|
14
|
18.4
|
9.5
|
5
|
|
2017
|
4.3
|
5.7
|
10.8
|
12.4
|
9
|
8.2
|
9
|
7.8
|
5
|
3.7
|
|
2018
|
4.8
|
8.2
|
12
|
11.6
|
12.8
|
12.9
|
12.9
|
13.8
|
13.3
|
9.2
|
|
2019
|
5.5
|
8.7
|
11.7
|
12.7
|
14.7
|
17.4
|
19.3
|
19.8
|
19.9
|
21.2
|
|
2020
|
5
|
7.1
|
8.7
|
8.8
|
9.7
|
9.3
|
10.8
|
13.9
|
8.7
|
4.3
|
|
2021
|
3.5
|
6.2
|
8.6
|
9.5
|
9.3
|
10.8
|
18.8
|
21
|
|
|
|
2022
|
2.4
|
3.8
|
4.2
|
4
|
3.8
|
5
|
5.6
|
6.7
|
10.3
|
|
|
2023
|
3.7
|
7.3
|
9.1
|
10.7
|
11.3
|
12.5
|
13.9
|
16.5
|
15.3
|
18.3
|
|
2024
|
2.3
|
4.1
|
5.2
|
5.3
|
4.7
|
5
|
5
|
5
|
|
|
|
2025
|
1.7
|
3.1
|
4.7
|
2.7
|
3.5
|
3.9
|
2.9
|
1
|
|
|
1 kt = 1.85 kmph
Annual average landfall point errors during 2016-2025:
|
Year
|
12-hr
|
24-hr
|
36-hr
|
48-hr
|
60-hr
|
72-hr
|
84-hr
|
96-hr
|
108-hr
|
120-hr
|
|
2016
|
7.8
|
14.1
|
71.6
|
127.2
|
129.2
|
180.1
|
253.2
|
286
|
403.4
|
|
|
2017
|
19.1
|
50.4
|
29.8
|
59
|
|
|
|
|
|
|
|
2018
|
26.7
|
44
|
42.1
|
40.3
|
56.4
|
67.6
|
|
|
|
|
|
2019
|
8.9
|
27.1
|
21.9
|
34.7
|
15
|
37.2
|
|
|
|
|
|
2020
|
10
|
17.6
|
53.5
|
69.7
|
27.7
|
43
|
77
|
47
|
47
|
|
|
2021
|
6.8
|
16.4
|
10.6
|
19.8
|
97
|
158.5
|
|
|
|
|
|
2022
|
16.5
|
14.8
|
21.7
|
24.5
|
20.2
|
4.5
|
4.9
|
|
|
|
|
2023
|
13.0
|
17.0
|
31.2
|
48.8
|
65.8
|
65.7
|
66.6
|
71.1
|
9.1
|
|
|
2024
|
5.4
|
14.4
|
19
|
24
|
18
|
2.2
|
1.1
|
1.1
|
|
|
|
2025
|
71
|
76
|
113
|
82
|
113
|
121
|
128
|
|
|
|
Annexure-2
|
State/UT-wise Number of Deaths due to Cyclones During 2014-2023
|
|
SL
|
State/UT
|
2014
|
2015
|
2016
|
2017
|
2018
|
2019
|
2020
|
2021
|
2022
|
2023
|
|
1
|
Andhra Pradesh
|
41
|
0
|
3
|
1
|
7
|
0
|
3
|
0
|
1
|
1
|
|
2
|
Arunachal Pradesh
|
0
|
0
|
2
|
0
|
1
|
0
|
0
|
0
|
0
|
0
|
|
3
|
Assam
|
0
|
1
|
1
|
0
|
0
|
0
|
2
|
0
|
4
|
0
|
|
4
|
Bihar
|
1
|
0
|
4
|
5
|
3
|
0
|
0
|
0
|
0
|
0
|
|
5
|
Chhattisgarh
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
6
|
Goa
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
7
|
Gujarat
|
6
|
0
|
0
|
0
|
0
|
3
|
0
|
40
|
0
|
0
|
|
8
|
Haryana
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
9
|
Himachal Pradesh
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
10
|
Jharkhand
|
3
|
0
|
0
|
2
|
3
|
0
|
0
|
0
|
0
|
0
|
|
11
|
Karnataka
|
0
|
0
|
0
|
0
|
0
|
1
|
2
|
0
|
0
|
0
|
|
12
|
Kerala
|
0
|
0
|
0
|
113
|
1
|
0
|
0
|
0
|
0
|
0
|
|
13
|
Madhya Pradesh
|
2
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
14
|
Maharashtra
|
2
|
0
|
3
|
0
|
1
|
0
|
2
|
72
|
0
|
0
|
|
15
|
Manipur
|
0
|
0
|
0
|
0
|
0
|
3
|
0
|
0
|
0
|
0
|
|
16
|
Meghalaya
|
0
|
0
|
0
|
2
|
0
|
0
|
0
|
1
|
2
|
0
|
|
17
|
Mizoram
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
18
|
Nagaland
|
0
|
0
|
0
|
1
|
0
|
0
|
0
|
0
|
0
|
0
|
|
19
|
Odisha
|
0
|
0
|
0
|
0
|
6
|
14
|
0
|
0
|
0
|
0
|
|
20
|
Punjab
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
0
|
0
|
|
21
|
Rajasthan
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
22
|
Sikkim
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
23
|
Tamil Nadu
|
0
|
0
|
2
|
6
|
95
|
0
|
0
|
0
|
0
|
0
|
|
24
|
Telangana
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
25
|
Tripura
|
0
|
0
|
0
|
0
|
2
|
0
|
0
|
0
|
0
|
0
|
|
26
|
Uttar Pradesh
|
7
|
13
|
0
|
3
|
5
|
11
|
0
|
0
|
0
|
0
|
|
27
|
Uttarakhand
|
0
|
0
|
0
|
0
|
0
|
0
|
4
|
0
|
0
|
0
|
|
28
|
West Bengal
|
0
|
0
|
0
|
0
|
0
|
0
|
22
|
2
|
2
|
0
|
|
|
Total number of deaths (in 28 States)
|
62
|
15
|
15
|
133
|
124
|
33
|
35
|
115
|
9
|
1
|
|
29
|
A & N Islands
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
2
|
0
|
1
|
|
30
|
Chandigarh
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
31
|
D&N Haveli and Daman&Diu @+
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
1
|
0
|
0
|
|
32
|
Delhi UT
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
33
|
Jammu & Kashmir @*
|
0
|
0
|
0
|
0
|
1
|
0
|
2
|
0
|
0
|
0
|
|
34
|
Ladakh @
|
-
|
-
|
-
|
-
|
-
|
-
|
0
|
0
|
0
|
0
|
|
35
|
Lakshadweep
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
36
|
Puducherry
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
0
|
|
|
Total Number of Deaths (in 8 UTs)
|
0
|
0
|
0
|
0
|
1
|
0
|
2
|
3
|
0
|
1
|
|
|
Total deaths in the country
|
62
|
15
|
15
|
133
|
125
|
33
|
37
|
118
|
9
|
2
|
|
Number of Cyclones that made landfall
|
1
|
0
|
1
|
0
|
3
|
2
|
4
|
3
|
1
|
1
|
Source of data regarding number of deaths: National Crime Records Bureau (NCRB), Ministry of Home Affairs (MHA).
As per the data provided by the State/UTs
‘+’ Combined data of erstwhile D & N HAVELI AND DAMAN & DIU UT during 2014-2019
‘*’ Data of erstwhile JAMMU & KASHMIR State, including LADAKH, during 2014-2019
‘@’ Data of the newly created Union Territory
********
NKR/JP
(Release ID: 2223598)
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