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Related Experiment Videos

Quantifying predictability in a model with statistical features of the atmosphere.

Richard Kleeman1, Andrew J Majda, Ilya Timofeyev

  • 1Courant Institute of Mathematical Sciences and Center for Atmosphere and Ocean Sciences, New York University, New York 10012, USA. kleeman@cims.nyu.edu

Proceedings of the National Academy of Sciences of the United States of America
|November 14, 2002
PubMed
Summary

Predicting weather is more about the "signal" than ensemble spread. A simple atmospheric model shows the difference between climate and weather states is key for forecast accuracy.

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Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification.

Entropy (Basel, Switzerland)·2020

Area of Science:

  • * Dynamical systems theory
  • * Atmospheric science modeling
  • * Statistical prediction analysis

Background:

  • * The Galerkin truncated inviscid Burgers equation models atmospheric dynamics.
  • * It exhibits properties like long-term variability and short-term weather modes.
  • * Correlation scaling in the model spans decades, explained by a simple theory.

Purpose of the Study:

  • * To analyze predictability in an idealized atmospheric model.
  • * To investigate the utility of statistical predictions using a relative entropy functional.
  • * To determine factors influencing predictive utility variations.

Main Methods:

  • * Utilized a theoretical framework based on relative entropy.
  • * Assumed Gaussian probability distributions for initial conditions.

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  • * Analyzed equilibrium (climatological) and nonequilibrium (prediction) distributions.

    • Main Results:

    • * The difference in first moments (signal) primarily determines predictive utility.
    • * Ensemble dispersion is secondary in explaining utility variations.
    • * Gaussian distributions were found to be applicable for both equilibrium and prediction states.

    Conclusions:

    • * Signal strength, not ensemble spread, is the main driver of prediction accuracy.
    • * Findings challenge conventional ensemble prediction approaches.
    • * Results have significant implications for practical weather forecasting methods.