Stochastic parametrization and model uncertainty
By Tim N. Palmer, Roberto Buizza, Francisco J. Doblas-Reyes, T. Jung, Martin Leutbecher, G. Shutts, M. Steinheimer, Antje Weisheimer. 2009
322 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/ac7162ccd7a48644ce40dd152ff3f822c82fe3bd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Hugh Morrison
- Tim N. Palmer
- Martin Leutbecher
- A. Fridlind
- Roberto Buizza
- Jason A. Milbrandt
- C. Hoose
- Francisco J. Doblas-Reyes
Citations in the corpus
- Stochastic climate theory and modeling (2015, unknown): “Stochastic parameterizations are routinely used to improve the reliability of weather forecasts in the short-6 and medium-range 5,10,123 as well as for seasonal predictions 4,28,165 .”
- Introducing independent patterns into the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme (2017, result): “For example, very similar results were obtained when the iSPPT scheme was evaluated in the older IFS cycle CY37R3 (Arnold, 2013), which differs from CY42R1 in a number of key ways.”
- Using a Stochastic Kinetic Energy Backscatter Scheme to Improve MOGREPS Probabilistic Forecast Skill (2011, background): “Bougeault et al. (2010) describe the GIFS-TIGGE archive, and Park et al. (2008) do an interesting comparison of the model forecasts from the various contributing centers.”
- Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, methodology): “…tendencies to generate forecast ensembles (the stochastic perturbed parameterization tendency approach or SPPT; e.g., Berner et al., 2015; Jankov et al., 2017; Palmer et al., 2009) or by applying multiplicative perturbations to microphysical process tendencies (van Lier‐Walqui et al., 2014).”
- Assessment of representations of model uncertainty in monthly and seasonal forecast ensembles (2011, background): “[Buizza et al., 1999; Palmer et al., 2009] applies univariate Gaussian perturbations to the wind, temperature and humidity tendencies of physical processes in the form of”
- The forecast skill horizon (2015, methodology): “…called with the same parameters), P′j represents the model uncertainty simulated using two model error schemes, the SPPT (Buizza et al., 1999; Palmer et al., 2009) and SKEB (Berner et al., 2008; Palmer et al., 2009) schemes, and the lead time and start date are denoted by t and d, respectively.”