The MOGREPS short‐range ensemble prediction system

By Neill Bowler, Alberto Arribas, K. Mylne, K. B. Robertson, Sarah E. Beare. 2008

512 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/196e11ddcfb2809c5ac50af89fadbb7e771251e8

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Citations in the corpus

Introducing independent patterns into the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme (2017, background): “For example, it uses far fewer stochastic patterns than alternative stochastically perturbed parameter approaches (Bowler et al., 2008; Ollinaho et al., 2017).”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “This (one-way) coupling has been used by Zhang et al. (2009) with the simple Lorenz 96 model (Lorenz, 1996), and by Bowler et al. (2008) with MOGREPS (the Met Office Global and Regional Ensemble Prediction System).”
A Description of the Advanced Research WRF Model Version 4 (2019, background): “The stochastically perturbed parameterization scheme (SPP) perturbs the values of uncertain key parameters and thus provides a way to represent uncertainties within a particular parameterization scheme (Bowler et al., 2008; Hacker et al., 2011).”
Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office (2013, methodology): “…on a global hybrid ensemble/variational data assimilation was resumed in late 2008, after the operational implementation of 4D-Var in 2004, and in particular the development and implementation of ‘MOGREPS’ (Bowler et al., 2008), a global and regional ensemble prediction system based on the ETKF.”
A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending (2014, methodology): “Previous work has shown significant short-range forecast uncertainties on mesoscale and in local detail (Zhang et al. 2002; McMurdie and Mass 2004; Bowler et al. 2008).”
The local ETKF and SKEB: Upgrades to the MOGREPS short‐range ensemble prediction system (2009, background): “Further deficiencies have since been noted (Bowler et al., 2007), such as the spread of the ensemble being too low near the surface and too large aloft, and work is currently underway to address these issues.”
Spatial-temporal fractions verification for high-resolution ensemble forecasts (2013, methodology): “To cope with such significant forecast uncertainties of meso-scale severe weather, the use of meso-scale ensemble prediction system (EPS) has also been started in several forecast centres (e.g., Marsigli et al., 2005; Bowler et al., 2008; Wang et al., 2011).”
Medium‐range multimodel ensemble combination and calibration (2009, methodology): “The Met Office 15-day ensemble is an exten sion of the MOGREPS short-range ensemble (Bowler et al., 2008), which is currently run for research purposes as part of the Met Office contribution to THORPEX.”
Impact of Stochastic Physics in a Convection-Permitting Ensemble (2012, methodology): “2011); the multiphysics method, which changes physical parameterizations (or some of their parameters) in a single prediction model (Berner et al. 2011; Bright and Mullen 2002; Gebhardt et al. 2008; Li et al. 2008; Bowler et al. 2008); and stochastic physics methods, which introduce perturbations in”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, background): “Recent applications to operational numerical weather prediction (NWP) systems have obtained promising results (e.g., Houtekamer and Mitchell 2005; Houtekamer et al. 2005; Bowler et al. 2008; Whitaker et al. 2008; Bonavita et al. 2008, 2010; Miyoshi et al. 2010).”
Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey (2012, methodology): “Global and regional ensemble systems [Met Office Global and Regional Ensemble Prediction System (MOGREPS)-G and -R] have also been developed in the same framework (Bowler et al. 2008), and the dynamical seasonal forecasting system was brought into the fold with the introduction of the Met Office Glo”
Stochastic parametrization and model uncertainty (2009, methodology): “In the operational UK Met Office Global and Regional Prediction System (MOGREPS, Bowler et al., 2008) three schemes have been used to simulate the effect of model errors.”