Every year, the South Asian monsoon season brings heavy rain to over a billion people in the Indian subcontinent between June and September. The rain falls in oscillations: Some weeks see 1 to 4 inches of water, while other weeks are mostly dry. Predicting when these dry and wet periods will occur is critical for agricultural and urban planning, enabling farmers to know when to harvest crops and helping city officials prepare for flooding. However, while weather predictions are mostly accurate within one or two days, precisely predicting the weather a week or month out is very difficult.
Predicting the weather is difficult because the atmosphere contains numerous instabilities—for example, the atmosphere is continually heated from the earth below, leading to cold, denser air above hotter, less dense air—as well as instability caused by uneven heating and Earth’s rotation. These instabilities lead to a chaotic situation in which the errors and uncertainties in modeling the atmosphere’s behavior quickly multiply, making it nearly impossible to predict further into the future. Current state-of-the-art models use numerical modeling, which are computer simulations of the atmosphere based on the physics equations describing the motion of fluids.
Now, a new machine-learning-based forecast has been shown to more accurately predict the South Asian monsoon rainfall 10 to 30 days in advance, a significant improvement on current state-of-the-art forecasts that use numerical modeling rather than artificial intelligence to make predictions. In the new research, Bach and his collaborators added a machine-learning component to current state-of-the-art numerical models. This allowed the researchers to gather data about the MISOs and make better predictions of the rainfall on the elusive two-to-four-week timescale. The resulting model was able improve the correlations of the predictions with observations by up to 70%.
“There is a lot of concern about how climate change will affect the monsoon and other weather events like hurricanes, heat waves, and so on,” Bach says. “Improving predictions on shorter timescales is an important part of responding to climate change because we need to be able to improve preparedness for these events.” Understanding monsoon behavior is also important because this type of rainfall is a major atmospheric feature in the global climate.
The integration of machine learning with traditional numerical modeling has shown promising results in predicting South Asian monsoon rainfall. The ability to forecast precipitation 10 to 30 days in advance has significant implications for agricultural planning, flood preparedness, and overall climate forecasting. This new approach represents a step forward in the field of weather prediction and highlights the potential for artificial intelligence to enhance our understanding of complex meteorological phenomena.
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