Learn how statistical modeling is revolutionizing weather/climate prediction applications. Such models offer high fidelity in theory and are increasingly viewed as potential replacements to actual simulations. The main drawbacks of such models are the expensive number of flops and the overhead of the memory footprint due to computations resulting from the large dense covariance matrix, which makes it unrealistic in practice. By exploiting the low rank structure of the matrix and redesigning the underlying linear algebra in terms of batch operations, the fidelity of the model is not only maintained but also the corresponding performance achieved on GPUs is unprecedented. Low-rank matrix computations on GPUs boosts existing machine learning algorithms for weather prediction applications and opens new research directions.