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This is an archive article published on September 6, 2012

IMD getting monsoon forecasts wrong: Govt

This year,the IMD seems to have got it doubly wrong.

The government conceded in Parliament Wednesday that the accuracy of long-range monsoon forecasts in the last four years had only been about 50 per cent,an admission that is expected to strengthen critics of the Met department who have questioned the credibility of its estimates.

Every year,the India Meteorological Department (IMD) tries to predict in April the total rainfall the country is likely to get in the June-September monsoon season. This year,the IMD seems to have got it doubly wrong.

In April-end,it said rainfall would be 99 per cent of the long period average. In June,it revised this to about 96 per cent. But when it hardly rained in June and July,the IMD drastically cut its prediction and said total rainfall was not likely to be more than 85 per cent of average.

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But August changed it all,with most parts of the country receiving widespread rainfall. The 23 per cent shortfall at the end of July,has come down to 12 per cent,and if the trend continues,the deficiency at the end of the season could be 8-10 per cent.

“All this while,the IMD seems to be reacting to events,whereas its forecasts should be able to see what is likely to happen. The fundamental question therefore is that why do we need long-range forecast at all,” said a government scientist.

“The long-range forecast in April does not tell us when and where it will rain. There are large variations in rainfall regionwise,but that does not get captured in a single average figure for the entire country. For example,none of the IMD predictions until July could see what happened in Rajasthan in August. So we need to seriously consider whether we should only concentrate on short-range forecasts which we can make with good accuracy,” he said.

But Dr D Sivananda Pai,who has been making long-range forecasts for the IMD for the last few years,said incessant rain in Rajasthan was a result of ‘intra-seasonal variability’ that cannot be predicted two months in advance.

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