Improving Weather Forecasting

Numerical weather and climate prediction (NWP) requires immense computational resources to solve complex, non-linear atmospheric equations, limiting forecast speed and scalability.

Quantum algorithms (such as those for non-linear differential equations) can provide an exponential speed-up over classical methods. Quantum Machine Learning (QML) and hybrid models could efficiently process the high-dimensional meteorological data.

This potentialy enhances the speed and accuracy of forecasts and climate projections. Improved prediction is vital for agricultural decision-making, enabling better resource management, such as timely irrigation and fertilization, to mitigate crop loss.