Editors Note: We are arranging interviews with leads on both the hardware and software side of this story and will update it with more information throughout the day. We all expected that the Summit supercomputer at Oak Ridge National Lab would be a major part of pushing deep learning forward in HPC given its balanced GPU and IBM Power9 profile (not to mention the on-site expertise to get those graphics engines doing cutting-edge work outside of traditional simulations). Today, researchers from Berkeley Lab and Oak Ridge, along with development partners at Nvidia demonstrated some rather remarkable results using deep learning to extract weather patterns based on existing high-res climate simulation data. This places the collaboration in the running for this year's Gordon Bell Prize, an annual award based on high performance, efficient use of real-world applications that can scale on some of the world's most powerful supercomputers. We have written before about how deep learning could be integrated into existing weather workloads on supercomputers, but this particular piece of news captures the performance potential of integrating AI into scientific workflows.