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Creators/Authors contains: "Sadowski, P."

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  1. ; ; ( , AAAI Spring Symposium: MLPS, 2020)
    null (Ed.)
    A deep neural network is trained to predict sea surface temperature variations at two important regions of the Atlantic ocean, using 800 years of simulated climate dynamics based on the first-principles physics models. This model is then tested against 60 years of historical data. Our statistical model learns to approximate the physical laws governing the simulation, providing significant improvement over simple statistical forecasts and comparable to most state-of-the-art dynamical/conventional forecast models for a fraction of the computational cost. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ( , AAAI 2020 Spring Symposium)
    null (Ed.)
    The Sentinel-1 satellites equipped with synthetic aperture radars (SAR) provide near global coverage of the world’s oceans every six days. We curate a data set of co-locations between SAR and altimeter satellites, and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on co-locations from 2015-2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 meters to 0.3 meters. 
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    Accepted Manuscript Full Text Available
  3. ; ; ( , Physical Review Letters)