A Deep Learning Parameterization for Ozone Dry Deposition Velocities
Abstract
Abstract 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 find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiälä, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus ~0.1). The same DNN model, when driven by assimilated meteorology at 2° × 2.5° spatial resolution, outperforms the Wesely scheme as implemented in the GEOS‐Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models.
- Authors:
-
[1];
[1];
[3];
[4]
- Department of Civil and Environmental Engineering Massachusetts Institute of Technology Cambridge MA USA
- Earth Signals and Systems Group, Department of Earth, Atmospheric, and Planetary Sciences Massachusetts Institute of Technology Cambridge MA USA
- Institute for Atmospheric and Earth System Research/Physics, Faculty of Science University of Helsinki Helsinki Finland
- School of Engineering and Applied Sciences Harvard University Cambridge MA USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1491211
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Geophysical Research Letters
- Additional Journal Information:
- Journal Name: Geophysical Research Letters Journal Volume: 46 Journal Issue: 2; Journal ID: ISSN 0094-8276
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Silva, S. J., Heald, C. L., Ravela, S., Mammarella, I., and Munger, J. W. A Deep Learning Parameterization for Ozone Dry Deposition Velocities. United States: N. p., 2019.
Web. doi:10.1029/2018GL081049.
Silva, S. J., Heald, C. L., Ravela, S., Mammarella, I., & Munger, J. W. A Deep Learning Parameterization for Ozone Dry Deposition Velocities. United States. https://doi.org/10.1029/2018GL081049
Silva, S. J., Heald, C. L., Ravela, S., Mammarella, I., and Munger, J. W. Wed .
"A Deep Learning Parameterization for Ozone Dry Deposition Velocities". United States. https://doi.org/10.1029/2018GL081049.
@article{osti_1491211,
title = {A Deep Learning Parameterization for Ozone Dry Deposition Velocities},
author = {Silva, S. J. and Heald, C. L. and Ravela, S. and Mammarella, I. and Munger, J. W.},
abstractNote = {Abstract 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 find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiälä, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus ~0.1). The same DNN model, when driven by assimilated meteorology at 2° × 2.5° spatial resolution, outperforms the Wesely scheme as implemented in the GEOS‐Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models.},
doi = {10.1029/2018GL081049},
journal = {Geophysical Research Letters},
number = 2,
volume = 46,
place = {United States},
year = {Wed Jan 16 00:00:00 EST 2019},
month = {Wed Jan 16 00:00:00 EST 2019}
}
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https://doi.org/10.1029/2018GL081049
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