Stochastic parametrizations and model uncertainty in the Lorenz ’96 system
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Summary
The forecasting skill of the parametrizations was found to be linked to their ability to reproduce the climatology of the full model, important in a seamless prediction system, allowing the reliability of short-term forecasts to provide a quantitative constraint on the accuracy of climate predictions from the same system.
- Type
- article
- Published
- 2013-05-28
- Cited by
- 151
- References
- 36
- Access
- Open access
- OpenAlex
- https://openalex.org/W2148474597
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2140830
Keywords
Econometrics, Applied mathematics, Mathematics, Computer science, Statistical physics
References
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- A Climatology of Atmospheric Wavenumber Spectra of Wind and Temperature Observed by Commercial Aircraft
- Using the Stochastic Multicloud Model to Improve Tropical Convective Parameterization: A Paradigm Example
- Using Initial Condition and Model Physics Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective Systems
- Effects of stochastic parametrizations in the Lorenz '96 system
- A New Vector Partition of the Probability Score
- THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research
- The Singular-Vector Structure of the Atmospheric Global Circulation
- VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
- Statistical Methods in the Atmospheric Sciences
- Subgrid-Scale Parameterization with Conditional Markov Chains
- Can multi‐model combination really enhance the prediction skill of probabilistic ensemble forecasts?
- A nonlinear dynamical perspective on model error: A proposal for non‐local stochastic‐dynamic parametrization in weather and climate prediction models
- Towards the probabilistic Earth‐system simulator: a vision for the future of climate and weather prediction
Cited by
- Prévisions d'ensemble à l'échelle saisonnière : mise en place d'une dynamique stochastique
- Seamless evaluation of stochastic physics parametrizations
- Semiparametric modeling: Correcting low-dimensional model error in parametric models
- Architectures and Precision Analysis for Modelling Atmospheric Variables with Chaotic Behaviour
- Extending the Square Root Method to Account for Additive Forecast Noise in Ensemble Methods
- Representing uncertainty in global climate models using stochastic sea ice parameterizations
- On the use of programmable hardware and reduced numerical precision in earth‐system modeling
- Discrete approach to stochastic parametrization and dimension reduction in nonlinear dynamics
- Linear theory for filtering nonlinear multiscale systems with model error
- Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction
- Reliable, robust and realistic: the three R's of next-generation land-surface modelling
- The use of imprecise processing to improve accuracy in weather & climate prediction
- Optimisation of an idealised ocean model, stochastic parameterisation of sub-grid eddies
- Using Proper Divergence Functions to Evaluate Climate Models
- On the use of inexact, pruned hardware in atmospheric modelling
- Initial Error Growth and Predictability of Chaotic Low-dimensional Atmospheric Model
- More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?
- Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization
- Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model
- Understanding the distinctively skewed and heavy tailed character of atmospheric and oceanic probability distributions.
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