The myriad characteristics and causes of weather and climate are coupled together, communicating with one another. To describe these complex relationships, scientists use a computationally expensive method called parameterization, in which they model the relationships at a scale greater than that of the actual phenomena. Argonne environmental scientists and computational scientists are collaborating to use deep neural networks to replace the parameterizations of certain physical schemes in the Weather Research and Forecasting (WRF) model — a comprehensive model that simulates the evolution of many aspects of the physical world.
Recently, Argonne scientists focused on the planetary boundary layer (PBL) — the lowest part of the atmosphere that is most affected by human activity. PLB dynamics (e.g., wind velocity, temperature and humidity profiles) are critical in determining many of the physical processes in the rest of the atmosphere and on Earth. The team used 20 years of computer-generated data from the WRF model to train the neural networks and two years of data to evaluate whether they could provide an accurate alternative to the physics-based parameterizations, feeding the network more than 10,000 data points and discovering that a trained deep neural network can successfully simulate wind velocities, temperature, and water vapor over time and predict behavior across nearby locations with correlations higher than 90 percent compared with the test data. The result was an algorithm that could replace the PBL parameterization in the WRF model. Such less-expensive models will allow researchers to achieve higher-resolution simulations to predict how short-term and long-term changes in weather patterns affect the local scale — down to neighborhoods or specific critical infrastructure.