NG012
When We Don't Know Everything: Stochastic Approaches to Predicting and Understanding the Earth System
Session ID#: 157803
Session Description:
Reliable, probabilistic prediction of nonlinear processes that govern the atmosphere, ocean, cryosphere, and land remains a critical challenge to Earth-system science. Uncertain initial and boundary conditions, model imperfection, and chaos hinder predictability of these geophysical systems. Recent advances in stochastic methods, data assimilation, and machine learning provide potential avenues to improve prediction skill and process understanding in the face of these uncertainties.
This session aims to bring together studies addressing uncertainty, chaos, turbulence, and stochasticity in fields such as meteorology, oceanography, and climate science. We welcome submissions related to stochastic parameterization, machine learning-based process emulation and parameterization, as well as advances in data assimilation. Applications that involve forecasting of extreme events, handling of non-Gaussian errors, or bridging the many spatio-temporal scales acting in coupled systems are also encouraged.
Co-Organized:
Index Terms:
3245 Probabilistic forecasting [MATHEMATICAL GEOPHYSICS]
3265 Stochastic processes [MATHEMATICAL GEOPHYSICS]
3275 Uncertainty quantification [MATHEMATICAL GEOPHYSICS]
3337 Global climate models [ATMOSPHERIC PROCESSES]
Primary Convener: Timothy Smith, Cooperative Institute for Research in Environmental Sciences, CU Boulder / PSL / NOAA, Boulder, CO, United States
Conveners: Aaron Wang, CIRES/University of Colorado and Physical Sciences Laboratory/NOAA, Boulder, CO, United States, Aneesh Subramanian, University of Colorado at Boulder, Department of Atmospheric and Oceanic Sciences, Boulder, United States and Paul Williams, University of Reading, Reading, RG6, United Kingdom
Evaluating the effect of targeted in-situ observations on hurricane prediction in the framework of ensemble forecasts and stochastic parameterizations (1195209)
Jih-Wang Aaron Wang1, Prashant D Sardeshmukh2, Laura Slivinski3,4, Gilbert P Compo3, Chesley McColl5, Andrew Kren6 and Gary A Wick7, (1)University of Colorado at Boulder, CIRES, Boulder, CO, United States, (2)CIRES, Boulder, CO, United States, (3)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (4)NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, United States, (5)NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, CO, United States, (6)University of Miami and NOAA/AOML/HRD, Miami, FL, United States, (7)NOAA/ESRL, Boulder, CO, United States
On the Parameterization of Sub-Grid Scale Density Variations in Ocean Circulation Models (1168772)
Niraj Agarwal, University of Colorado Boulder, Boulder, CO, UNITED STATES, Frank Bryan, National Center for Atmospheric Research, Climate and Global Dynamics, Boulder, CO, United States and Ian Grooms, University of Colorado at Boulder, Applied Mathematics, Boulder, United States
Stochastic Inversion with Maximal Updated Densities for Storm Surge Wind Drag Parameter Estimation (1169473)
Carlos del-Castillo-Negrete, Austin, Texas, UNITED STATES, Michael Pilosov, University of Colorado Denver, Denver, United States, Troy Butler, University of Colorado Denver, Denver, CO, United States, Clint Dawson, University of Texas at Austin, Aerospace Engineering & Engineering Mechanics, Austin, TX, United States and Tian Yu Yen, Sandia National Laboratories, Albuquerque, United States
Emulating the chemistry within a chemical transport model using a machine learning surrogate ensemble. (1191010)
Peter Ivatt1,2, Julie M Nicely1,3, Christoph Keller4,5, Melanie B Follette-Cook6,7 and Daniel Tong8, (1)NASA Goddard Space Flight Center, Greenbelt, United States, (2)University of Maryland College Park, College Park, United States, (3)University of Maryland College Park, College Park, MD, United States, (4)Morgan State University, Baltimore, United States, (5)NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, United States, (6)NASA GSFC, Greenbelt, United States, (7)Morgan State University, Baltimore, MD, United States, (8)George Mason University Foundation Inc., Columbia, United States
Predictable and unpredictable sea level changes in observations and models (1065446)
Mikhail Karpytchev, University of La Rochelle, La Rochelle, France, Melanie Becker, CNRS/LIENSS, La Rochelle, France and Aixue Hu, NCAR, Boulder, United States
Implicit Learning of Convective Organization Explains Precipitation Stochasticity (1072082)
Sara Shamekh1, Kara Lamb1, Yu Huang1 and Pierre Gentine2, (1)Columbia University, Earth and Environmental Engineering, New York, United States, (2)Columbia University, Department of Earth and Environmental Engineering, New York, NY, United States
Recurrent Neural Network Emulation for High Resolution Sea Surface Forecasts (1094895)
Timothy Smith, Cooperative Institute for Research in Environmental Sciences, CU Boulder / PSL / NOAA, Boulder, CO, United States, Stephen Gregory Penny, NOAA Boulder, Boulder, MD, United States, Tse-Chun Chen, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States and Jason Platt, University of California San Diego, Physics, La Jolla, CA, United States
Stochastic Approaches to Multidecadal Space Weather (1096287)
James A Wanliss, Presbyterian College, Clinton, SC, United States, Ambaka Le Gregam, Presbyterian College, Physics, Clinton, United States and Cade Owens, Presbyterian College, Clinton, United States
Correcting systematic and state-dependent errors in the NOAA FV3-GFS using neural networks (1100314)
Tse-Chun Chen1, Stephen Gregory Penny2, Jeffrey Whitaker3, Sergey Frolov3, Robert Pincus4 and Stefan Tulich5, (1)Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States, (2)NOAA Boulder, Boulder, MD, United States, (3)NOAA PSL, Boulder, United States, (4)Columbia University, Lamont-Doherty Earth Observatory, Palisades, NY, United States, (5)CIRES/University of Colorado, Longmont, CO, United States
Chasing data-driven probabilistic forecasting tools for heatwaves: the case of analog Markov chains and dimensional reduction via autoencoders (1114882)
George Miloshevich, LSCE Laboratoire des Sciences du Climat et de l'Environnement, Gif-Sur-Yvette Cedex, France, Freddy Bouchet, Ecole Normale Supérieure de Lyon and CNRS, Lyon, France, Dario Lucente, ENS de Lyon, Lyon, France and Pascal Yiou, LSCE Laboratoire des Sciences du Climat et de l'Environnement, Gif-Sur-Yvette, France
A New Paradigm for South Pacific Climate Variability and Predictability (1155801)
Jiale Lou, CIRES CU-Boulder and NOAA-PSL, Boulder, United States, Terence O'Kane, CSIRO Hobart, Hobart, TAS, Australia and Neil John Holbrook, University of Tasmania, Institute for Marine and Antarctic Studies, Hobart, TAS, Australia
Noise-Induced Drift in the First Large-Scale Stochastic Ice Sheet Model (1159616)
Alexander Robel, Georgia Institute of Technology Main Campus, Earth and Atmospheric Science, Atlanta, United States, Vincent Verjans, Georgia Institute of Technology Main Campus, Atlanta, United States, Aminat Ambelorun, Georgia Institute of Technology, Atlanta, GA, United States and Helene L Seroussi, Dartmouth College, Thayer School of Engineering, Hanover, NH, United States
