Machine Learning-Based Prediction of Spatiotemporal Uncertainties in Global Wind Velocity Reanalyses

Abstract

The characterization of uncertainties in geophysical quantities is an important task with widespread applications for time series prediction, numerical modeling, and data assimilation. In this context, machine learning is a powerful tool for estimating complex patterns and their evolution through time. Here, we utilize a supervised machine learning approach to dynamically predict the spatiotemporal uncertainty of near-surface wind velocities over the ocean. A recurrent neural network (RNN) is trained with reanalyzed 10 m wind velocities and corresponding precalculated uncertainty estimates during the 2012–2016 time period. Afterward, the neural network's performance is examined by analyzing its prediction for the subsequent year 2017. Our experiments show that a recurrent neural network can capture the globally prevalent wind regimes without prior knowledge about underlying physics and learn to derive wind velocity uncertainty estimates that are only based on wind velocity trajectories. At single training locations, the RNN-based wind uncertainties closely match with the true reference values, and the corresponding intra-annual variations are reproduced with high accuracy. Moreover, the neural network can predict global lateral distribution of uncertainties with small mismatch values after being trained only at a few isolated locations in different dynamic regimes. The presented approach can be combined with numerical models for a cost-efficient generation of ensemble simulations or with ensemble-based data assimilation to sample and predict dynamically consistent error covariance information of atmospheric boundary forcings.