Recently, researchers at the Department of Energy’s Pacific Northwest National Laboratory teamed up to find out if deep learning—a distinct subset of machine learning—can do a better job at identifying clouds in lidar data than the current physics-based algorithms. The answer: A clear “yes.” The new model is much closer to the answers scientists arrive at but in just a fraction of the time.
Lidar is a remote sensing instrument that emits a pulsed laser and collects the return signal scattered back by cloud droplets or aerosols. This return signal provides information about the height and vertical structure of atmospheric features, such as clouds or smoke layers. Such data from ground-based lidars are an important part of global forecasting.
Earth scientist Donna Flynn noticed that, in some cases, what the algorithms detected as clouds in the lidar images did not match well with what her expert eye saw. The algorithms tend to overestimate the cloud boundaries.
“The current algorithm identifies the clouds using broad brushstrokes,” says Flynn, a co-principal investigator on the project. “We need to more accurately determine the cloud’s true top and base and to distinguish multiple cloud layers.”