Deep neural networks speed up weather and climate models
When you check the weather forecast in the morning, the results you see are more than likely determined by the Weather Research and Forecasting (WRF) model, a comprehensive model that simulates the evolution of many aspects of the physical world around us.
“It describes everything you see outside of your window,” said Jiali Wang, an EVS atmospheric & earth scientist, “from the clouds, to the sun's radiation, to snow to vegetation — even the way skyscrapers disrupt the wind.”
The myriad characteristics and causes of weather and climate are coupled together, communicating with one another. Scientists have yet to fully describe these complex relationships with simple, unified equations. Instead, they approximate the equations using a method called parameterization in which they model the relationships at a scale greater than that of the actual phenomena.
Although parameterizations simplify the physics in a way that allows the models to produce relatively accurate results in a reasonable time, they are still computationally expensive. Environmental scientists and computational scientists from Argonne are collaborating to use deep neural networks, a type of machine learning, to replace the parameterizations of certain physical schemes in the WRF model, significantly reducing simulation time.
Read the full article by Savannah Mitchem.