A recent study has developed a novel prediction method to forecast monsoon significantly earlier than previously possible.
A team of scientists developed the method based on a network analysis of regional weather data, and will propose this approach to the Indian Meteorological Department.
The heavy summer rains are of vital importance for millions of farmers feeding the subcontinent's population. Future climate change will likely affect monsoon stability and hence makes accurate forecasting even more relevant.
"We can predict the beginning of the Indian monsoon two weeks earlier, and the end of it even six weeks earlier than before - which is quite a breakthrough, given that for the farmers every day counts," says Veronika Stolbova from the Potsdam Institute for Climate Impact Research (PIK) and the University of Zurich, the lead-author of the study to be published in the Geophysical Research Letters.
"We found that in North Pakistan and the Eastern Ghats, a mountain range close to the Indian Ocean, changes of temperatures and humidity mark a critical transition to monsoon," explains Stolbova.
Conventionally, the focus has been on the Kerala region on the southern tip of India.
Information about monsoon timing is key for Indian farmers to determine when to carry out the sowing crops like rice, soybean and cotton that normally grow during the June to September monsoon rainy season.
The scientists tested their method with historical monsoon data. It gives correct predictions for onset in more than 70 percent and for withdrawal in more than 80 percent of the considered years.
The main advantage of the proposed approach is that it allows to improve the time horizon of the prediction compared to the methods currently used in India. In addition, the new scheme notably improves the forecasting of monsoon timing during years affected by the global weather phenomenon El Nino - Southern Oscillation (ENSO), particularly in its La Nina phase. This phenomenon significantly alters monsoon timing and decreases the prediction accuracy in existing methods.
