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Cyclone Dana, which struck Odisha and West Bengal in October 2024, highlighted the limitations of current weather monitoring and forecasting systems in India. Just hours before landfall, two major international forecasting systems—the US Global Forecasting System (GFS) and the European Centre for Medium-Range Weather Forecasts’ Integrated Forecasting System (IFS)—provided conflicting forecasts. GFS predicted landfall near the Odisha-West Bengal border, while IFS placed it closer to Paradip in Odisha. However, the cyclone made landfall near Bhitarkanika National Park in Odisha, almost 100 km from Paradip Port.
Unlike global forecasting systems, the India Meteorological Department (IMD) provided more accurate landfall predictions, utilising doppler weather radars (that track the movement and speed of a cyclone using microwave signals) at Odisha’s Paradip and Gopalpur ports, along with satellite data. The department, however, fell short at forecasting the resultant rainfall. It had predicted more rainfall for Odisha than for neighbouring West Bengal, but the opposite happened. On October 25, the day of the cyclone’s landfall, only three districts of Odisha received over 40 mm of absolute rainfall. Kendrapara recorded the highest rainfall of 85.9 mm, which is 899 per cent more than the normal for the particular date. In West Bengal, six districts received more than 40 mm of absolute rainfall with West Midnapore district recording the highest rainfall of 71.1 mm which was 2,534 per cent more than the normal. On October 26, five districts of Odisha and 13 districts in West Bengal received rainfall of 50 mm or more.
This occurred because just before and after making the landfall, cyclone Dana was stuck between two anti-cyclones or high pressure areas at a height of 5-6 km above sea level in which the rotation of air is in the clockwise direction, opposite to the air circulation in a cyclone. “One of these was west of the landfall and the other was towards the east of the landfall. The anti-cyclone on the west was feeding dry air to the cyclone which led to its rapid dissipation once it crossed over to land and did not let it move further inland. The cyclone was still getting moisture from the Bay of Bengal towards the east which led to the unexpected heavy rainfall in West Bengal,” says K J Ramesh, climate scientist and former director-general of IMD. The weather models of IMD were showing the development of at least one of these anti-cyclones but the timing was not clear therefore it would not have been included in the rainfall forecast, he adds. “The mapping of mid troposphere moisture in and around a cyclone is crucial to know where and how much it would rain,” says Roxy Mathew Koll, climate scientist at the Indian Institute of Tropical Meteorology, Pune. “The observations, modelling and forecasting of moisture in general and specifically for cyclones has failed miserably till now,” he adds.
The other important aspects that are missing from observations of cyclone characteristics hence from modelling and forecasting are data from the ocean surface, specifically of sea surface and subsurface temperatures and wind speed data from ground based high wind speed recorders. “Across the world’s ocean basins the measurement of subsurface temperatures is not done very well right now and this is one of the gaps in data for tropical cyclones that we have,” says Koll.
While satellites are good at monitoring of sea surface temperatures, subsurface temperature data needs to come from sur-face monitoring using instruments such as buoys (floating objects anchored in a waterbodies to collect data on sea surface and subsurface temperatures, currents and winds) or ARGO-floats (robotic floats that drift below the ocean surface to collect information such as temperature, salinity and others). Ocean heat data from these monitoring instruments is used to calculate the tropical cyclone heat potential, which measures ocean heat from the surface down to 700-1,000 metres. This measurement is crucial for predicting a tropical cy-clone’s likelihood of rapid intensification (RI), where wind speeds increase by more than 55 km/h in 24 hours, according to Ramesh.
RI has made recent cyclones in the North Indian Ocean region (which includes the Bay of Bengal and the Arabian Sea) increasingly unpredictable.
While the science is clear, experts admit that the country’s coast does not have enough density of instruments to correctly track all cyclones. Further, many of the existing instruments are either outdated or non-functional. There are two networks of buoys in the North Indian Ocean. The first is the Ocean Moored Buoy Network for Northern Indian Ocean (OMNI), which is maintained by the National Institute of Ocean Technology (NIOT), Chennai, and the collected data is managed by the Indian National Centre for Ocean Information Services (INCOIS), Hyderabad. The network has 12 buoys only 9 of which are currently in the North Indian Ocean region and only eight are reporting data, according to the INCOIS website. “Around 50 per cent of the buoys in the Bay of Bengal are functional. Two of these fell in the path of the cyclone Dana. They helped us predict the land-fall precisely,” says Ajay Kumar B, scientist with INCOIS. “India lost a lot of its own buoys during the COVID-19 pandemic as they could not be maintained and repaired,” says Koll.
The second network is the Research Moored Array for African-Asian-Australian Monsoon Ana-lysis and Prediction (RAMA). It is a collaboration between the US National Oceanic and Atmospheric Administration (NOAA) and INCOIS. At present, there are 25 RAMA buoys in the region, out of which only 11 are reporting data. The North Indian Ocean also has around 500 to 700 ARGO floats.
“NOAA has updated most of their buoys in the Arabian Sea and the west Indian Ocean regions but not in the Bay of Ben-gal,” says Koll. The replacement of the Indian buoys has been slow because of the push by the Indian government for manu-facturing them in India instead of getting them from other countries, he adds.
Most of the instruments transmit their data to a satellite which then relays the information to the base monitoring station. For instance, the AGRO floats remain under the ocean for 10 days before coming to the surface to transmit the data. “If outdated technology is used there is a higher chance of the data redundancy and transmission delays these instruments not reaching on time,” says Ramesh.
Another important instrument in cyclone forecasting is high-speed wind recorders. “Doppler weather radars measure wind speeds slightly above surface level; however, for accurate cyclone predictions, continuous data on surface-level wind speeds is also critical, which is captured by high-speed wind recorders,” says Ramesh. Currently, the density of these recorders is low, with approximately 25 along In-dia’s east coast and 10 on the west coast. Ideally, there should be at least one IoT (internet of things)-based recorder in every coastal taluka to accurately measure wind speeds and improve tropical cyclone forecasts.
This was first published in the 16-30 November, 2024 print edition of Down To Earth