New Delhi: The India Meteorological Department (IMD) plans to simplify its monsoon forecasts by announcing monthly predictions of rainfall, which is expected to better prepare farmers to plan their cultivation.
Currently, the department provides a seasonal forecast in April and follows it up with a revision in June. This year, which saw India receive its scantiest rainfall in 37 years, IMD revised its predictions twice.
“Ultimately, if farmers are the ultimate beneficiaries, they only need a 15-20 day warning of rain or the lack of it,” said Madhavan Rajeevan, a meteorologist formerly with IMD.
“We are the only country that puts a number to the rainfall,” he said. “Averaging all-India rainfall to a single number is always risky and increases chances of tripping up.”
If the forecast come in a more timely manner, “numbers don’t really matter”, Rajeevan pointed out.
Agriculture in India is heavily dependent on the monsoon rains, with at least 55% of the farmland requiring adequate rainfall to turn in a good harvest. Farming adds only 17% to the country’s gross domestic product but around 60% of the population is dependent on it for their livelihood.
The south-west monsoon, or summer rains, is generally expected to begin around June and dies down by the end of September.
To simplify matters further, IMD also plans to do away with its present nomenclature of near-normal and below-normal rainfall. It could now just say a monsoon season would be normal if the expected rainfall is within 10% of 89cm, which is a 50-year average.
Reliably anticipating a drought a month in advance is easier than gauging it three months before, said a met department official who declined to be named. “This year we changed our estimates twice,” he said. “Repeatedly changing estimates doesn’t bode well for our credibility.”
This year, when agriculture was expected to boost India’s slowing economy, IMD was unable to gauge the extent of the drought. It didn’t anticipate June’s 45% rainfall deficit and predicted rainfall in August to be 101% of the average. Eventually, India received only 73% of its usual August quota of 17cm.
Met department officials say the proposal to simplify predictions has been discussed previously but has greater currency this year, thanks to the driest monsoon in more than three decades. IMD is likely to consult a wide spectrum of meteorologists before taking a call.
“We will have meetings from November and there has to be a consensus before decisions are taken,” said IMD director general Ajit Tyagi. “But next year’s forecasts are likely to be the same as the previous year’s.”
IMD’s plans reflect a change in its attitude towards weather modelling itself. It plans to give more weight to dynamic weather models and do away with its more than century old statistical models, Mint reported on 8 April.
Dynamic weather models use supercomputers to simulate the weather at a particular time and extrapolate it to days or weeks ahead.
IMD currently uses both statistical and dynamic weather models, generated by several national and international modelling agencies, to generate weather forecasts. Based on how robust these are, it ascribes a weightage to each and generates an average value, widely publicized as the official forecast.
However, dynamic models, though reasonably accurate over weekly and fortnightly time frames, are fallible when extrapolated over a month. That’s why IMD hasn’t given up on its statistical models, in spite of them never having forecast droughts or floods a month or two in advance.
“It’s the best we have for long-range forecasting,” said D.S. Pai, director, forecasting, IMD. “It’s only this year that we introduced a dynamic model that predicts monsoon performance 20 days in advance. It’s worked well, but you never know when it may fail.”
Between 1988 and 2002, the department used a statistical model that worked well. However, it failed to predict a drought in 2002, forcing it to change some parameters.
The tweaked model also couldn’t predict the deficient 2004 monsoon.
Since 2006, IMD has been using newer models and has incorporated results from dynamic weather models.