A nonlinear dynamical perspective on model error: A proposal for non‐local stochastic‐dynamic parametrization in weather and climate prediction models

By Tim N. Palmer. 2001

560 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/48b7b3db1242af2371ffe75f85eaae2d03c15da6

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Citations in the corpus

Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, methodology): “…system exhibits sensitive dependence on initial conditions (‘chaotic dynamics’), and has been used as an idealisation of atmospheric behaviour in a number of previous studies (e.g. Lorenz 1996; Orrell 2003; Palmer 2001: Smith 2001; Vannitsem & Toth 2002; Roulston & Smith 2003; Wilks 2005).”
A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System (2009, background): “In nature, the flow over a mountain is associated with an increase in kinetic energy from turbulent eddies in the lee of the mountain, whereas in the mountain drag parameterization this effect is modeled by a drag coefficient, thus reducing the turbulent kinetic energy (see Palmer 2001).”
A Comparison of Dynamical and Statistical Predictions of Weekly Tropical Cyclone Activity in the Southern Hemisphere (2010, methodology): “The atmospheric perturbations are produced using the singular vector method (Buizza and Palmer 1995) and by randomly perturbing the tendencies in the atmospheric physics during the model integrations (Buizza et al. 1999; Palmer 2001).”
The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years (2018, background): “Martin invited me to talk about the notion of stochastic parametrisation in a workshop in 1995 on Convection Parametrisation (Palmer, 1996; 2001).”
Multimodel Ensembling in Seasonal Climate Forecasting at IRI (2003, background): “Either or both of these possibilities may be due partly to a lack of accounting for uncertainty associated with the models’ own physical parameterizations (e.g., Toth and Vannitsem 2002; Palmer 2001 ).”
Performance of the New NCEP Global Ensemble Forecast System in a Parallel Experiment (2017, background): “The upgraded stochastic physics suite has three components, including 1) stochastically perturbed physics tendencies (SPPTs; Buizza et al. 1999; Palmer 1997, 2001), 2) stochastically perturbed planetary boundary layer humidity (SHUM), and 3) stochastic kinetic energy backscatter (SKEB; Berner et…”
A simple framework to justify linear response theory (2009, methodology): “The mathematical formulation of FDT as linear response theory for forced dissipative stochastic dynamical systems [Ris89, MAG05, MFK08, Pal01 , MW09] is the most appropriate setting for these applications.”
Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles (2011, background): “[3] A third approach to representing model uncertainty has emerged in recent years and relies on the idea of stochastic parameterisation [Palmer, 2001; Palmer and Williams, 2009; Berner et al., 2011].”
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments (2006, methodology): “Other ways to include model error are to apply statistical adjustment to ensemble forecasts (Hamill and Whitaker 2005) or to use stochastic forecast models and/or stochastic physical parameterizations (e.g., Palmer 2001; Grell and Devenyi 2002).”
Comorbidity: A network perspective (2010, methodology): “Dynamical systems theory can be used to predict the trajectory of a system in the state space; that is, future states of the system can be predicted from earlier states, a technique that is, for instance, widely employed in weather forecasting (e.g., see Palmer 2001).”
Mesoscale spectrum of atmospheric motions investigated in a very fine resolution global general circulation model (2008, background): “ Palmer [2001] notes that the ECMWF spectral GCM, when run at fine resolution, actually simulates flow with a tropospheric kinetic energy spectrum that steepens rather than shallows in the mesoscale.”
Description and evaluation of the bergen climate model: ARPEGE coupled with MICOM (2003, background): “It is difficult to assess whether this also holds for the climate predictions for the next century, but it indicates that the uncertainties in the climate scenarios may be improved by using ensemble means over a large range of different AOGCMs (Palmer 2001; Palmer and Räisänen 2002).”
Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, methodology): “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 parameteriza- tions, suboptimal methods for generating the initial conditions (Barkmeijer et al. 1998, 1999; Hamill et al.”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, background): “Model error, of course, can be introduced from many causes, such as imperfect parameterizations (Buizza et al. 1999; Palmer 2001).”