Weather Forecasting in India: Accurate Predictions | UPSC

Introduction

• India has seen a significant rise in public trust in weather forecasting, thanks to notable improvements in forecast accuracy. This growing confidence has fueled increased demand for precise and detailed weather predictions, driven by a surge in extreme weather events and climate change-induced disasters. In response, the government is planning a major enhancement of the India Meteorological Department’s (IMD) capabilities.

Historical Development of Weather Forecasting in India

Ancient Period:

• • • 3000 B.C.: Early philosophical writings such as the Vedas and Upanishads discussed cloud formation, rain, and seasonal cycles caused by the Earth’s movement around the sun.
    • 500 A.D.: Varahamihira’s work, Brihatsamhita, provided evidence of deep knowledge of atmospheric processes.
    • Ancient Understanding : It was believed that rain comes from the sun (Adityat Jayate Vrishti) and that good rainfall in the rainy season was crucial for bountiful agriculture and food security.
    • Kautilya’s Era: Arthashastra by Kautilya contains records of scientific measurements of rainfall and its application to the country’s revenue and relief efforts.
    • Kalidasa’s Period: In the epic Meghdoot, Kalidasa mentioned the onset of the monsoon over central India and traced the path of monsoon clouds.

Colonial Period:

• • • First Meteorological Observatory: Established at Calcutta in 1785 and Madras in 1796.
    • Asiatic Society of Bengal: Founded in 1784 at Calcutta and in 1804 at Bombay (now Mumbai), promoted scientific studies in meteorology in India.
    • Foundation of India Meteorological Department (IMD): Modern weather forecasting in India began with the establishment of the IMD in 1875 by Sir Henry Blanford. The department’s initial focus was monsoon prediction, crucial for the agrarian economy of the time.
    • Advancements in Monsoon Understanding: Under Gilbert Walker, appointed head of IMD in 1903, significant progress was made in understanding the monsoon system. Walker’s work laid the foundation for modern-day comprehension of phenomena like El Niño.

Post-Independence Period:

• • • Expansion of IMD: The IMD expanded its network of weather stations across India, significantly enhancing the country’s ability to monitor and predict weather.
    • Introduction of Numerical Weather Prediction (NWP): The adoption of NWP models, which utilize complex mathematical equations, allowed for more accurate and timely weather forecasts, a leap forward in meteorological science.
    • Advances in Technology: The launch of the INSAT system in 1982 marked the beginning of real-time satellite data usage, improving the accuracy of weather forecasting, particularly in predicting cyclones.

21st Century Developments:

• • • Monsoon Mission 2012: This initiative aimed to improve long-range monsoon forecasts, essential for government economic planning and agricultural activities.
    • Enhanced IMD Capabilities: Recent years have seen a focus on upgrading the IMD’s observational network, installing high-performance computing systems, deploying sophisticated instruments like Doppler radars, and improving computer simulation models.

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Significance of Weather Forecasting in India 

Agriculture and Food Security:

• • • Accurate weather forecasting is crucial for planning agricultural activities, especially in a country like India where 70-80% of the annual rainfall is dependent on the monsoon. Forecasts help farmers decide the best times for sowing, irrigation, and harvesting, thereby reducing the risk of crop failure and ensuring food security.
    • Example: In Punjab and Haryana, weather forecasts guide the timing of paddy sowing, ensuring crops are planted when the monsoon is expected, thus optimizing water use and preventing crop loss due to untimely rains.

Disaster Management and Mitigation:

• • • India is highly vulnerable to natural disasters like cyclones, floods, and droughts. Accurate weather predictions allow authorities to prepare and respond effectively, reducing the impact of these disasters on lives and property.
    • Example: The accurate prediction of Cyclone Phailin in 2013 enabled the evacuation of nearly a million people in Odisha and Andhra Pradesh, significantly reducing the death toll compared to previous cyclones.

Reduction of Economic Losses:

• • • Weather vagaries like droughts and inadequate monsoons can lead to significant economic losses, particularly in agriculture and energy sectors. Accurate weather forecasts help mitigate these losses by allowing for better resource management and contingency planning.
    • Example: The forecast of a weak monsoon in 2015 allowed the Maharashtra government to implement drought relief measures in Marathwada, minimizing agricultural losses and alleviating rural distress.

Construction and Urban Planning:

• • • Weather forecasts are essential for planning construction activities, particularly during the monsoon and winter seasons. This helps in avoiding delays, ensuring worker safety, and minimizing costs related to weather-related disruptions.
    • Example: During the construction of the Mumbai Metro, accurate weather predictions allowed for the scheduling of activities around the monsoon, preventing waterlogging at construction sites and ensuring project timelines were met.

Transport and Logistics:

• • • The safety and efficiency of road, rail, and sea transportation in India depend heavily on accurate weather predictions. This is particularly true for managing operations during adverse weather conditions like fog, storms, and heavy rains.
    • Example: In North India, IMD’s fog forecasts during winter help manage flight schedules at airports like Delhi’s Indira Gandhi International Airport, reducing delays and enhancing passenger safety.

Public Health and Safety:

• • • Weather predictions play a crucial role in public health by alerting authorities and the public to extreme weather conditions like heatwaves, cold waves, and heavy rains, allowing for timely preventive measures.
    • Example: During the 2019 heatwave in Bihar, IMD’s alerts led to the issuance of public advisories and the establishment of cooling centers, significantly reducing the number of heat-related illnesses and fatalities in the state.

Water Resource Management:

• • • Effective management of water resources, including irrigation and reservoir management, relies on accurate weather forecasts. These forecasts help in planning the use and distribution of water, particularly in regions dependent on monsoon rains.
    • Example: In Maharashtra, weather forecasts are used to manage water levels in dams like Koyna, ensuring adequate water storage during the monsoon for use in the dry season, thereby supporting agricultural and drinking water needs.

Successes of Weather Forecasting in India 

Improved Cyclone Forecast System:

• • • Timely and accurate cyclone forecasts have greatly enhanced response and evacuation mechanisms, significantly reducing the loss of life during severe cyclones.
    • Example: In 2013, the accurate forecast of Cyclone Phailin allowed for the evacuation of over a million people, resulting in significantly fewer casualties compared to similar cyclones in the past. Similarly, Cyclone Amphan in 2020 saw minimal loss of life due to timely warnings and evacuations.

Monsoon Prediction Accuracy:

• • • The accuracy of long-range monsoon forecasts has been nearly 100% over the last decade, which has been instrumental in supporting agricultural planning and water resource management across India.
    • Example: The accurate prediction of the monsoon in 2019 allowed farmers in Maharashtra to plan their sowing and irrigation schedules effectively, leading to better crop yields and reduced water wastage.

Weather Forecasting for Transportation:

• • • The Winter Fog Experiment (WIFEX) developed by the IMD has significantly improved the dissemination of fog-related information, which aids airlines and passengers in planning their travel.
    • Example: During the winter of 2019-2020, fog forecasts from WIFEX helped manage flight schedules at Delhi’s Indira Gandhi International Airport, reducing delays and improving safety for passengers.

Air Quality Monitoring:

• • • The IMD’s SAFAR (System of Air Quality and Weather Forecasting and Research) system is used to monitor air pollution levels in major cities like Delhi, which has contributed to better public health management and policy-making.
    • Example: During Diwali in 2020, SAFAR’s accurate air quality forecasts enabled authorities in Delhi to take preemptive measures, such as limiting fireworks and issuing health advisories, to protect public health.

Global Recognition:

• • • The IMD’s expertise in weather forecasting is globally recognized, as it is one of the six Regional Specialized Meteorological Centres of the World Meteorological Organization (WMO). The IMD has also contributed significantly to the UN’s ‘Early Warning for All’ initiative.
    • Example: The IMD’s contributions have been critical in providing early warning systems for tropical cyclones in the Indian Ocean region, helping neighboring countries like Bangladesh and Sri Lanka improve their disaster preparedness and response.

Challenges in Weather Forecasting in India 

Greater Variability in Weather Phenomena:

• • • Forecasting weather in tropical regions like India is particularly challenging due to the greater variability in weather patterns compared to temperate zones, making predictions more complex.
    • Example: The unpredictability of the Indian monsoon, with sudden shifts in intensity and duration, complicates forecasting efforts, leading to challenges in accurate agricultural planning.

Hyper-Locality Challenges:

• • • The current IMD grid covers a 12km x 12km area, but improving accuracy, especially for localized predictions, requires a more granular 3km x 3km or 1km x 1km grid.
    • Example: The lack of hyper-local forecasting was evident during the 2017 Mumbai floods, where localized heavy rainfall in certain areas was not predicted accurately, leading to significant disruption and damage.

Inherent Uncertainties:

• • • Weather forecasting inherently involves uncertainties; the more precise the forecast, the greater the uncertainty, especially in long-term predictions.
    • Example: The unexpected shift in Cyclone Fani’s path in 2019, despite accurate initial forecasts, highlights the inherent uncertainties in predicting tropical cyclones, leading to last-minute adjustments in evacuation plans.

Data Assimilation and Modeling:

• • • Incorporating diverse and accurate data into numerical models remains a challenge. For instance, difficulties in assimilating satellite data affected the accuracy of forecasts during critical weather events.
    • Example: During the 2015 Chennai floods, the inability to assimilate real-time satellite data into weather models led to delayed and less accurate predictions, exacerbating the impact of the floods.

Outdated Infrastructure:

• • • The IMD’s reliance on outdated and insufficient observational infrastructure, including early warning systems, limits its effectiveness, especially during critical weather events.
    • Example: The 2013 Uttarakhand floods exposed the inadequacies of the early warning systems in the region, resulting in delayed evacuation efforts and higher casualties.

Collaboration with Neighboring Countries:

• • • The lack of enhanced collaboration with neighboring countries like China and Pakistan in data sharing related to transboundary air pollution and shared water resources limits the effectiveness of weather predictions.

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Way Forward 

Hyper-Local Analysis:

• • • The IMD should aim to develop hyper-local forecasts for smaller grids, such as 3km x 3km or even 1km x 1km, to enhance precision and meet the increasing demand for localized weather predictions.
    • The UK Met Office uses a 1.5km grid model for forecasting, providing highly detailed and accurate weather predictions for small regions, which has been particularly effective in predicting localized flooding events.

Tailoring to Local Specifics:

• • • Developing computer simulation models tailored to local specifics, focusing on district, block, and even village levels, will improve forecast accuracy and relevance for diverse geographic areas.
    • Japan’s Meteorological Agency has developed region-specific models that account for the unique topographical and climatic conditions of its many islands, improving the accuracy of forecasts in disaster-prone areas.

Integration of AI and ML:

• • • Incorporating AI and ML algorithms can help analyze vast meteorological data sets, improving prediction models and accuracy, especially in complex weather scenarios.
    • The National Oceanic and Atmospheric Administration (NOAA) in the United States has integrated AI and ML into its weather prediction systems, enhancing the accuracy of hurricane path predictions and enabling better preparedness. 

Academic and Research Collaboration:

• • • Collaborating with academic institutions, like the partnership between IMD and IIT Bombay, can lead to advancements in computing technologies, enhancing forecast precision and resilience.
    • The European Centre for Medium-Range Weather Forecasts (ECMWF) collaborates with numerous academic institutions across Europe, leveraging cutting-edge research to maintain its position as one of the leading weather forecasting organizations in the world.

Infrastructure Upgrades:

• • • Prioritizing the installation of modern meteorological infrastructure, such as ocean observation systems, high-resolution satellites, and Doppler radars, especially in vulnerable regions, will significantly improve weather forecasting capabilities.
    •  Australia’s Bureau of Meteorology has invested in a network of advanced Doppler radars and satellite systems that provide real-time data, enabling highly accurate weather predictions that are crucial for managing the country’s frequent extreme weather events.