Ensemble forecasting

By Martin Leutbecher, Tim N. Palmer. Journal of Computational Physics, 2008

299 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/98299c87133d0238102e9a305196bce97b8afbcc

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Citations in the corpus

A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending (2014, methodology): “Their large-scale IC perturbations used in blending and LBC perturbations are provided by the first 16 members of the ECWMF-EPS (Leutbecher and Palmer 2008).”
A Spectral Stochastic Kinetic Energy Backscatter Scheme and Its Impact on Flow-Dependent Predictability in the ECMWF Ensemble Prediction System (2009, methodology): “To see to what degree the ensemble system is able to model flow-dependent predictability, we compute spread-reliability diagrams (Leutbecher and Palmer 2008).”
WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting (2020, methodology): “A good ﬁrst order approximation for this is the spread (ensemble standard deviation) to error (RMSE) ratio which should be one (Leutbecher and Palmer, 2008). more stringent test is to use a proper probabilistic scoring rule, for example the continuous ranked probability score (CRPS) (Gneiting and…”
An Approach to Generating Mutually Independent Initial Perturbations for Ensemble Forecasts: Orthogonal Conditional Nonlinear Optimal Perturbations (2016, background): “The ensemble mean may filter the unpredictable parts and leave the common parts of the forecasting members, ultimately decreasing the uncertainties of single forecast results (Leith 1974; Leutbecher and Palmer 2008).”
Spatial Postprocessing of Ensemble Forecasts for Temperature Using Nonhomogeneous Gaussian Regression (2014, methodology): “ account for the various sources of uncertainty in the numerical weather prediction (NWP) model outputs (Lewis, 2005). Such systems have now become the state-of-the-art in meteorological forecasting (Leutbecher and Palmer, 2008). Additionally, the ensemble forecasts are commonly postprocessed using ”
Ensemble size: How suboptimal is less than infinity? (2018, background): “They sample an isotropic Gaussian distribution in the subspace spanned by the singular vectors (Leutbecher and Palmer, 2008).”
FuXi: a cascade machine learning forecasting system for 15-day global weather forecast (2023, methodology): “Following the approach used by ECMWF for ensemble runs, which involves perturbing both initial conditions and model physics [39, 40], we incorporated random Perlin noise [16] into the initial conditions and implemented the Monte Carlo dropout (MC dropout, dropout rate is 0.2) [41] to perturb the…”
Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities* (2015, background): “To account for the various sources of uncertainty in the NWP model output, ensemble prediction systems were developed and have now become state of the art in meteorological forecasting (Buizza et al. 2005; Lewis 2005; Leutbecher and Palmer 2008).”
Stochastic parametrization and model uncertainty (2009, methodology): “All experiments use the same set of singular vector-based initial perturbations (cf. Leutbecher and Palmer 2008) and have 50 perturbed forecasts.”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “In forecasting practice, the problem of predicting uncertainty, thus moving from a “single-realization deterministic forecast” to a “multi-realizations probabilistic forecast,” has been tackled by ensemble prediction schemes (Leutbecher & Palmer, 2008).”
Prediction of the Madden–Julian Oscillation: A Review (2018, methodology): “For a perfectly reliable system, the ensemble spread equals the RMSE between the ensemble mean and observation (e.g., Leutbecher and Palmer 2008).”