Effects of stochastic parametrizations in the Lorenz '96 system

By D. Wilks. 2005

253 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5635804a714996c2684117e78e3a06477a76928b

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) (2018, methodology): “…(t) k ∂t =− x(t)k−1(x (t) k−2 − x (t) k+1)− x (t) k + F− g(x (t) k ,θ) + η (t) k (10) g(x (t) k ,θ) = θ1 + θ2x (t) k (11) where F is a constant set to 10 and η (t) k a stochastic perturbation term representing unobserved variables of faster time scales (see Wilks 2005; Dutta et al. 2016).”
Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, methodology): “Initial ‘analyses’ and ensemble members for Equation (17a) were chosen in a manner consistent with the local attractor shape for Equation (16a), as described in Wilks (2005).”
Ensemble forecasting (2008, background): “Studies with low-dimensional dynamical systems and with numerical weather prediction models also emphasise the sensitivity of ensemble dispersion to the specification of the temporal correlations of the model error term [56,57].”
Likelihood-Free Inference by Ratio Estimation (2016, methodology): “…time-series of slow weather variables ( y k , k = 1 , . . . , 40), which follow a coupled stochastic diﬀerential equation (SDE), called the forecast model (Wilks, 2005), where η k is stochastic and represents the uncertainty due to the forcing of the unobserved fast weather variables.”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, methodology): “This issue can be studied for the Lorenz-96 system, for example, by using approximate models as parameterizations of the fast dynamics [e.g., Fatkullin and VandenEijnden, 2004; Wilks, 2005; Crommelin and Vanden-Eijnden, 2008].”
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues (2021, unknown): “Therefore, Wilks 331 deﬁned the closure term using both deterministic and stochastic components.”
Mathematical and physical ideas for climate science (2013, background): “A now classic example of empirical construction and of testing of stochastic parametrizations is given by Wilks (2005), see Fig.”
Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, unknown): “These biases may be due to model errors, insufficient resolution (Weisman et al. 1997; Mullen and Buizza 2002; Szunyogh and Toth 2002; Buizza et al. 2003) or suboptimal parameterizations, suboptimal methods for generating the initial conditions (Barkmeijer et al. 1998, 1999; Hamill et al. 2000, 2003”