A Deep Learning Parameterization for Ozone Dry Deposition Velocities
The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We fi...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2020
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| Online Access: | https://hdl.handle.net/1721.1/125517 |