Cosmic rays are streams of very high energy particles in space that stem from activity on high energy stars like the sun. They can also originate in sources beyond our solar system, like exploding stars and distant galaxies. The cosmic rays cause electronic problems in satellites and other space instruments and their effects on the human body are seen as negative, but it is not clear to what extent.
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth System Model development could play a key role in reducing climate prediction uncertainty in the coming decade.
II, David John Gagne (University of Oklahoma) | McGovern, Amy (University of Oklahoma) | Brotzge, Jerald (University of Albany) | Coniglio, Michael (NOAA National Severe Storms Laboratory) | Jr., James Correia (NOAA Storm Prediction Center, NOAA/OU Cooperative Institute for Mesoscale Meteorological Studies) | Xue, Ming (University of Oklahoma)
Hail causes billions of dollars in losses by damaging buildings, vehicles, and crops. Improving the spatial and temporal accuracy of hail forecasts would allow people to mitigate hail damage. We have developed an approach to forecasting hail that identifies potential hail storms in storm-scale numerical weather prediction models and matches them with observed hailstorms. Machine learning models, including random forests, gradient boosting trees, and linear regression, are used to predict the expected hail size from each forecast storm. The individual hail size forecasts are merged with a spatial neighborhood ensemble probability technique to produce a consensus probability of hail at least 25.4 mm in diameter. The system was evaluated during the 2014 National Oceanic and Atmospheric Administration Hazardous Weather Testbed Experimental Forecast Program and compared with a physics-based hail size model. The machine-learning-based technique shows advantages in producing smaller size errors and more reliable probability forecasts. The machine learning approaches correctly predicted the location and extent of a significant hail event in eastern Nebraska and a marginal severe hail event in Colorado.
David Mitchell pulls into the parking lot of the Desert Research Institute, an environmental science outpost of the University of Nevada, perched in the dry red hills above Reno. On this morning, wispy cirrus clouds draw long lines above the range. Mitchell, a lanky, soft-spoken atmospheric physicist, believes these frigid clouds in the upper troposphere may offer one of our best fallback plans for combating climate change. But Mitchell, an associate research professor at the institute, thinks there might be a way to counteract the effects of these clouds. It would work like this: Fleets of large drones would crisscross the upper latitudes of the globe during winter months, sprinkling the skies with tons of extremely fine dust-like materials every year.