ARMing the Edge: Designing Edge Computing–Capable Machine Learning Algorithms to Target ARM Doppler Lidar Processing

Journal Article · 30 September 2023 · Artificial Intelligence for the Earth Systems

DOI: https://doi.org/10.1175/AIES-D-22-0062.1 · OSTI ID:2000585

Jackson, Robert C. ^[1]; Raut, Bhupendra A. ^[2]; Dematties, Dario ^[3]; Collis, Scott M. ^[1]; Ferrier, Nicola ^[4]; Beckman, Pete ^[4]; Sankaran, Rajesh ^[4]; Kim, Yongho ^[4]; Park, Seongha ^[4]; Shahkarami, Sean ^[4]; Newsom, Rob ^[5]

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1. a Environmental Sciences Division, Argonne National Laboratory, Argonne, Illinois
2. a Environmental Sciences Division, Argonne National Laboratory, Argonne, Illinois, b Northwestern–Argonne Institute of Science and Engineering, Northwestern University, Evanston, Illinois
3. c Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, b Northwestern–Argonne Institute of Science and Engineering, Northwestern University, Evanston, Illinois
4. c Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois
5. d Earth and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington

Abstract There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. However, due to bandwidth and storage constraints, the Doppler spectra are not routinely stored. This calls for the automation of cloud and rain detection in ARM Doppler lidar data so that the spectral data in clouds can be selectively saved and further analyzed. During the ARMing the Edge field experiment, a Waggle node capable of performing machine learning applications in situ was deployed at the ARM SGP site for this purpose. In this paper, we develop and test four algorithms for the Waggle node to automatically classify ARM Doppler lidar data. We demonstrate that supervised learning using a ResNet50-based classifier will classify 97.6% of the clear-air images and 94.7% of cloudy images correctly, outperforming traditional peak detection methods. We also show that a convolutional autoencoder paired with k -means clustering identifies 10 clusters in the ARM Doppler lidar data. Three clusters correspond to mostly clear conditions with scattered high clouds, and seven others correspond to cloudy conditions with varying cloud-base heights.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). ARM Data Center; Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Contributing Organization: Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL)
• Grant/Contract Number: AC02-06CH11357; AC05-76RL01830
• OSTI ID: 2000585
• Report Number(s): PNNL-SA-185989; 220062
• Journal Information: Artificial Intelligence for the Earth Systems, Journal Name: Artificial Intelligence for the Earth Systems Journal Issue: 4 Vol. 2; ISSN 2769-7525
• Publisher: American Meteorological SocietyCopyright Statement
• Country of Publication: United States
• Language: English