Articles | Volume 14, issue 7
https://doi.org/10.5194/gmd-14-4495-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-4495-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation
Benjamin A. Toms
CORRESPONDING AUTHOR
Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
Karthik Kashinath
Lawrence Berkeley National Laboratory, Berkeley, California, USA
Prabhat
Lawrence Berkeley National Laboratory, Berkeley, California, USA
Lawrence Berkeley National Laboratory, Berkeley, California, USA
Department of Agricultural and Environmental Sciences, University of California, Davis, Davis, California, USA
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Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
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There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola-DiRosario, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins
Geosci. Model Dev., 13, 6131–6148, https://doi.org/10.5194/gmd-13-6131-2020, https://doi.org/10.5194/gmd-13-6131-2020, 2020
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Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
plausibleweather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.
Cited articles
Abhik, S. and Hendon, H. H.: Influence of the QBO on the MJO during coupled
model multiweek forecasts, Geophys. Res. Lett., 46, 9213–9221,
2019. a
Adames, Á. F. and Kim, D.: The MJO as a dispersive, convectively coupled
moisture wave: Theory and observations, J. Atmos. Sci.,
73, 913–941, 2016. a
Adames, Á. F. and Wallace, J. M.: Three-dimensional structure and evolution
of the MJO and its relation to the mean flow, J. Atmos.
Sci., 71, 2007–2026, 2014. a
Adames, Á. F. and Wallace, J. M.: Three-dimensional structure and evolution
of the moisture field in the MJO, J. Atmos. Sci., 72,
3733–3754, 2015. a
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and
Samek, W.: On pixel-wise explanations for non-linear classifier decisions by
layer-wise relevance propagation, PloS one, 10, e0130140, https://doi.org/10.1371/journal.pone.0130140, 2015. a
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson,
D.: Viewing forced climate patterns through an AI Lens, Geophys. Res.
Lett., 46, 13389–13398, 2019. a
Brenowitz, N. D. and Bretherton, C. S.: Prognostic validation of a neural
network unified physics parameterization, Geophys. Res. Lett., 45,
6289–6298, 2018. a
Chen, T. and Chen, H.: Universal approximation to nonlinear operators by neural
networks with arbitrary activation functions and its application to dynamical
systems, IEEE T. Neural Netw., 6, 911–917, 1995. a
Densmore, C. R., Sanabia, E. R., and Barrett, B. S.: QBO influence on MJO
amplitude over the Maritime Continent: Physical mechanisms and seasonality,
Mon. Weather Rev., 147, 389–406, 2019. a
Ebert-Uphoff, I. and Hilburn, K. A.: Evaluation, Tuning and Interpretation of
Neural Networks for Meteorological Applications, arXiv [preprint],
arXiv:2005.03126, 2020. a, b
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The
modern-era retrospective analysis for research and applications, version 2
(MERRA-2), J. Climate, 30, 5419–5454, 2017. a, b
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT press, available at: https://books.google.com/books?hl=en&lr=&id=omivDQAAQBAJ&oi=fnd&pg=PR5&dq=Goodfellow,+I.,+Bengio,+Y.,+and+Courville,+A.:+Deep+learning&ots=MNO3arszWQ&sig=NLK3dUkGYpMXR4-kZ1z7yD9O-7g#v=onepage&q=Goodfellow%2C I.%2C Bengio%2C Y.%2C and Courville%2C A.%3A Deep learning&f=false (last access: 31 December 2020),
2016. a
Hendon, H. H. and Liebmann, B.: Organization of convection within the
Madden-Julian oscillation, J. Geophys. Res.-Atmos., 99,
8073–8083, 1994. a
Jiang, X., Adames, Á. F., Kim, D., Maloney, E. D., Lin, H., Kim, H., Zhang,
C., DeMott, C. A., and Klingaman, N. P.: Fifty Years of Research on the
Madden-Julian Oscillation: Recent Progress, Challenges, and Perspectives,
J. Geophys. Res.-Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911, 2020. a
Lee, H.-T. and NOAA CDR Program: NOAA Climate Data Record (CDR) of Daily Outgoing Longwave Radiation (OLR), Version 1.2, NOAA National Climatic Data Center, https://doi.org/10.7289/V5SJ1HH2, 2011. a, b
Liebmann, B. and Smith, C. A.: Description of a complete (interpolated)
outgoing longwave radiation dataset, B. Am. Meteorol.
Soc., 77, 1275–1277, 1996. a
Madden, R. A. and Julian, P. R.: Detection of a 40–50 day oscillation in the
zonal wind in the tropical Pacific, J. Atmos. Sci., 28,
702–708, 1971. a
Martin, Z., Vitart, F., Wang, S., and Sobel, A.: The impact of the stratosphere
on the MJO in a forecast model, J. Geophys. Res.-Atmos., 125, e2019JD032106,
https://doi.org/10.1029/2019JD032106,
2020. a
Matsuno, T.: Quasi-geostrophic motions in the equatorial area, J.
Meteorol. Soc. Jpn. Ser. II, 44, 25–43, 1966. a
McGovern, A., Lagerquist, R., John Gagne, D., Jergensen, G. E., Elmore, K. L.,
Homeyer, C. R., and Smith, T.: Making the black box more transparent:
Understanding the physical implications of machine learning, B.
Am. Meteorol. Soc., 100, 2175–2199, 2019. a
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R.:
Explaining nonlinear classification decisions with deep taylor decomposition,
Pattern Recogn., 65, 211–222, 2017. a
Monteiro, J. M., Adames, Á. F., Wallace, J. M., and Sukhatme, J. S.:
Interpreting the upper level structure of the Madden-Julian oscillation,
Geophys. Res. Lett., 41, 9158–9165, 2014. a
NOAA: MJO indices, available at: https://www.psl.noaa.gov/mjo/mjoindex/, (last access: 31 December 2020. a
Olah, C., Mordvintsev, A., and Schubert, L.: Feature visualization, Distill, 2, e7,
2017. a
Rasp, S., Pritchard, M. S., and Gentine, P.: Deep learning to represent subgrid
processes in climate models, P. Natl. Acad. Sci. USA,
115, 9684–9689, 2018. a
Roundy, P. E. and Frank, W. M.: Applications of a multiple linear regression
model to the analysis of relationships between eastward-and westward-moving
intraseasonal modes, J. Atmos. Sci., 61, 3041–3048,
2004. a
Roundy, P. E., MacRitchie, K., Asuma, J., and Melino, T.: Modulation of the
global atmospheric circulation by combined activity in the Madden–Julian
oscillation and the El Niño–Southern Oscillation during boreal winter,
J. Climate, 23, 4045–4059, 2010. a
Simonyan, K., Vedaldi, A., and Zisserman, A.: Deep inside convolutional
networks: Visualising image classification models and saliency maps, arXiv
[preprint], arXiv:1312.6034, 2013. a
Sobel, A. and Maloney, E.: Moisture modes and the eastward propagation of the
MJO, J. Atmos. Sci., 70, 187–192, 2013. a
Son, S.-W., Lim, Y., Yoo, C., Hendon, H. H., and Kim, J.: Stratospheric control
of the Madden–Julian oscillation, J. Climate, 30, 1909–1922, 2017. a
Toms, B.: Data for GMD 2020-152 (Version V1), Zenodo [Data set], https://doi.org/10.5281/zenodo.3968896, 2020. a
Toms, B. A., Barnes, E. A., Maloney, E. D., and van den Heever, S. C.: The
Global Teleconnection Signature of the Madden-Julian Oscillation and its
Modulation by the Quasi-Biennial Oscillation, J. Geophys.
Res.-Atmos., 25, e2020JD032653,
https://doi.org/10.1029/2020JD032653, 2020b. a, b
Tseng, K.-C., Maloney, E., and Barnes, E.: The consistency of MJO
teleconnection patterns: an explanation using linear Rossby wave theory,
J. Climate, 32, 531–548, 2019. a
Weyn, J. A., Durran, D. R., and Caruana, R.: Can Machines Learn to Predict
Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height
From Historical Weather Data, J. Adv. Model. Earth Sy.,
11, 2680–2693, 2019. a
Yang, D. and Ingersoll, A. P.: Testing the hypothesis that the MJO is a mixed
Rossby–gravity wave packet, J. Atmos. Sci., 68,
226–239, 2011. a
Yang, D. and Ingersoll, A. P.: Triggered convection, gravity waves, and the
MJO: A shallow-water model, J. Atmos. Sci., 70,
2476–2486, 2013. a
Yoo, C. and Son, S.-W.: Modulation of the boreal wintertime Madden-Julian
oscillation by the stratospheric quasi-biennial oscillation, Geophys.
Res. Lett., 43, 1392–1398, 2016. a
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H.: Understanding
neural networks through deep visualization, arXiv [preprint], arXiv:1506.06579,
2015. a
Zhang, C. and Dong, M.: Seasonality in the Madden–Julian Oscillation, J.
Climate, 17, 3169–3180,
https://doi.org/10.1175/1520-0442(2004)017<3169:SITMO>2.0.CO;2, 2004. a
Zhang, C. and Zhang, B.: QBO-MJO Connection, J. Geophys. Res.-Atmos., 123, 2957–2967, 2018. a
Zhang, C., Adames, Á., Khouider, B., Wang, B., and Yang, D.: Four Theories
of the Madden-Julian Oscillation, Rev. Geophys., 58.3, e2019RG000685,
https://doi.org/10.1029/2019RG000685, 2020. a
Zhao, C., Li, T., and Zhou, T.: Precursor signals and processes associated with
MJO initiation over the tropical Indian Ocean, J. Climate, 26,
291–307, 2013. a
Short summary
We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.
We test whether a type of machine learning called neural networks can be used trustfully within...