Home India Ministry of Earth Sciences PARLIAMENT QUESTION: ACCURACY OF CYCLONE FORECASTS...
Date: 2026-02-05 Category: Press Release State: Union Government Country: India

PARLIAMENT QUESTION: ACCURACY OF CYCLONE FORECASTS

Issued by Ministry of Earth Sciences · Not Applicable

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Executive Summary & Key Takeaways

**Executive Summary** The document addresses a parliamentary question regarding the accuracy of cyclone forecasts issued by the India Meteorological Department (IMD). It highlights improvements in forecasting accuracy from 2016-2025 due to continuous upgrades in technology and analysis. It also presents data on cyclone-related deaths from 2014-2023 as well as annual average track forecast errors (km) and annual average intensity forecast errors (kt) during 2016-2025. The information was submitted on February 5, 2026. **Key Points / Main Content** * **Forecast Accuracy Improvement:** * Significant improvement in cyclone forecast accuracy over the last decade. * Improvement in track forecast accuracy by 20-25%. * Landfall and intensity forecast accuracy improved by 35-45% in 2021-2025 compared to 2016-2020. * **Forecasting and Warning System:** * IMD's cyclone forecasting and warning system is highly accurate in track and intensity prediction. * Utilizes state-of-the-art numerical weather prediction models, multi-model ensemble, advanced data assimilation techniques, and continuous monitoring. * Employs satellites, Doppler Weather Radars (DWRs), ocean buoys, coastal observational networks, and in-house developed Decision Support Systems (DSS). * **Mission Mausam:** * Government of India launched Mission Mausam in early 2025. * Aims to expand and modernize India's weather observation network and forecasting systems. * Includes increasing weather stations, upgrading radar networks, and using machine-learning and modern models. * Utilizes High Performance Computing Systems (HPCSs) and intelligent Decision Support Systems (DSSs). * **Data on Deaths:** * Presents State/UT-wise data on deaths due to cyclones from 2014-2023. * Data sourced from the National Crime Records Bureau (NCRB) and the Ministry of Home Affairs (MHA). * Early warnings and timely action by the government have significantly reduced loss of life due to cyclones. * **Annual Averages (2016-2025):** * The document includes tables with annual averages of track forecast errors, intensity forecast errors, and landfall point errors during 2016-2025. **Impact Analysis** **Stakeholder: Government (Central & State)** * **Impact:** The government has significantly reduced loss of life in recent times by providing early warnings and taking timely action * **Action Required:** Continue to improve the monitoring, forecasting, and dissemination of warning infrastructure by expanding and modernizing India's weather observation network and forecasting systems. **Stakeholder: India Meteorological Department (IMD)** * **Impact:** IMD is responsible for year-wise analysis of the accuracy of cyclone forecasts * **Action Required:** Continue to work towards improving forecasting accuracy through the use of improved forecasting technologies and techniques. **Stakeholder: General Public** * **Impact:** Benefits from improved accuracy in cyclone forecasting, which enables better preparedness and reduced risk of loss of life and property. * **Action Required:** Stay informed about cyclone forecasts and heed warnings issued by authorities.

Key Entities Referenced

India Meteorological Department (IMD): Primary agency responsible for cyclone forecasting and warnings in India. Ministry of Earth Sciences: The central ministry to which the IMD is affiliated, overseeing weather forecasting and related activities. Mission Mausam: A Government of India initiative launched in early 2025 to improve weather observation and forecasting.
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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- hr2016 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 11 kt = 1.85 kmph Annual average landfall point errors during 2016-2025: Year 12- 24-hr 36-hr 48-hr 60-hr 72-hr 84-hr 96- 108-hr 120-hr hr 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 41 0 3 1 7 0 3 0 1 1 Pradesh 2 Arunachal 0 0 2 0 1 0 0 0 0 0 Pradesh 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 06 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 0 0 0 0 0 0 0 0 0 0 Pradesh 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 2 1 0 0 0 0 0 0 0 0 Pradesh 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 0 027 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 62 15 15 133 124 33 35 115 9 1 number of deaths (in 28 States) 29 A & N 0 0 0 0 0 0 0 2 0 1 Islands 30 Chandigarh 0 0 0 0 0 0 0 0 0 0 31 D&N Haveli 0 0 0 0 0 0 0 1 0 0 and Daman&Diu @+ 32 Delhi UT 0 0 0 0 0 0 0 0 0 0 33 Jammu & 0 0 0 0 1 0 2 0 0 0 Kashmir @* 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 0 0 0 0 1 0 2 3 0 1 of Deaths (in 8 UTs) Total deaths in 62 15 15 133 125 33 37 118 9 2 the country Number of Cyclones 1 0 1 0 3 2 4 3 1 1 that made landfall 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) Visitor Counter : 222 Read this release in: Urdu , ही , Bengali , Tamil

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