ECMWF | Reading | 11-15 April 2016
Workshop description
This joint ECMWF/WWRP workshop provided an opportunity for international experts to discuss the latest developments in diagnosing and characterising model error, and building schemes for simulating model uncertainty in assimilation and prediction systems. The focus was on improving the physical basis of stochastic forcing techniques used to represent the effect of uncertainties in resolved and under-resolved processes in global atmospheric models, convection-permitting models and the longer timescales for land-surface, ocean and sea-ice coupling.
Through a combination of oral presentations, poster presentations and Working Group discussions, we sought to answer the questions:
- What are the fundamental sources of model error?
- How can we improve the diagnosis of model error?
- What are and how do we measure the pros and cons of existing approaches to representing model uncertainty?
- How do we improve the physical basis for model uncertainty schemes?
The workshop yielded recommendations for future research directions.
Brief outline
The workshop comprised 25-minute talks from invited speakers across 2.5 days; an evening poster discussion session, and working group and plenary discussions across 1.5 days.
Presentations and summaries
Monday 11 April
| Presentation | Summary | |
| Welcome Erland Källén (ECMWF) |
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| Weather prediction in a world of uncertainties: should we care about model uncertainty? Roberto Buizza (ECMWF) |
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| Physically-based stochastic parameterisation George Craig (LMU Munich) |
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| Stochastic parametrisation models for GFD Darryl Holm (Imperial College) |
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| Assessing the contribution of mesoscale convective systems to model error using simulated IR imagery and numerical simulation Glenn Shutts (UKMO) |
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| A weather-system perspective on forecast errors Heini Wernli (ETHZ) |
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| Resolved and parametrized energy cascades in the IFS Sylvie Malardel (ECMWF) |
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Tuesday 12 April
| Presentation | Summary | |
| Diagnosing and representing model error in 4DVar Katherine Howes (University of Reading) |
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| New covariance statistics of model error for use in weak-constraint 4DVar Jacky Goddard (ECMWF) |
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| Ensemble data assimilation using a unified representation of model error Chiara Piccolo (UKMO) |
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| Diagnosing systematic numerical weather prediction model bias over the Antarctic from short-term forecast tendencies Steven Cavallo (Oklahoma University) |
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| Using forecast temporal variability to evaluate model behaviour Carolyn Reynolds (NRL) |
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| Using ensemble data assimilation to diagnose model uncertainty Mark Rodwell (ECMWF) |
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| Model uncertainty representations in the IFS Martin Leutbecher (ECMWF) |
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| A review of model-error representation in the Météo-France ensemble systems Laure Raynaud (Météo-France) |
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| Stochastic parameterization development in the NOAA/NCEP Global Forecast System Philip Pegion (NOAA) |
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| Model error representation in the Canadian Ensemble Prediction Systems Leo Separovic (Environment Canada) |
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| Representing model error in the Met Office convection permitting ensemble prediction system Anne McCabe (UKMO) |
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| Model Uncertainty Representation in COSMO-DE-EPS Susanne Theis (DWD) |
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Wednesday 13 April
| Presentation | Summary | |
| Stochastic parameterization: Towards a new view of Weather and Climate Models Judith Berner (NCAR) |
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| Model uncertainty estimation in global ocean models: Stochastic parametrizations of ocean mixing Stephan Juricke (Oxford University) |
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| Representing model uncertainty for climate forecasts Antje Weisheimer (ECMWF) |
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| On the diagnosis of model error statistics using weak-constraint data assimilation Neill Bowler (UKMO) |
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A comparison of the model error and state formulations of weak-constraint 4D-Var Amos Lawless (University of Reading) |
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| Using trajectories of ensemble analyses and tools from weak constraint 4D-Var to represent model uncertainty Craig Bishop (NRL) |
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| Model error representation in ensemble convection-permitting forecasts and ensemble data assimilation Glen Romine (NCAR) |
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| Improving the Stochastically Perturbed Parametrisation Tendencies scheme using High-Resolution Model Simulations Hannah Christensen (Oxford University) |
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| Impact of a stochastic parameterization of cumulus convection, using cellular automata, in Harmon-EPS Lisa Bengtsson (SMHI) |
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| Parameter uncertainty of chaotic systems Heikki Haario (Lappeenranta University) |
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| Inducing Tropical Cyclones to Undergo Brownian Motion Daniel Hodyss (NRL) |
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| Observational based stochastic convection parameterization Jesse Dorrestijn (CWI Amsterdam) |
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Posters
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Representing atmospheric model uncertainties: Applications in seasonal forecasts with CNRM-CM Laurianne Batté (CNRM, Météo-France/CNRS) |
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AEMET-&-SREPS: Convection-permitting EPS Alfons Callado Pallarès (AEMET) |
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Predictability and Model Uncertainty of MJO Convection and Propagation in the ECMWF IFS Stochastic Ensembles Shuyi Chen (RSMAS/University of Miami) |
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Model uncertainty in representing midlatitude atmospheric synoptic variability: a spectral perspective Susanna Corti (ISAC-CNR) |
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Uncertainty quantification of atmospheric transport and dispersion backtracking using an ensemble approach Pieter De Meutter (Royal Meteorological Institute of Belgium) |
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Model uncertainty quantification using ultra-efficient inexact hardware Peter Dueben (University of Oxford) |
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Review of the perturbation methods in the Meteorological Service of Canada (MSC) Global Ensemble Prediction System (GEPS) Normand Gagnon (Meteorological Service of Canada, Environment and Climate Change Canada) |
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Case-study based evaluation of stochastic physics effects in the high-resolution ensemble COSMO-E Christina Klasa (ETH Zurich) |
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Evaluation of the SKEBS impact on WRF-based mesoscale ensemble prediction system Chih-Hsin Li (Central Weather Bureau, Taiwan) |
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Representation of model error using stochastic equation with flow-dependent spatial and temporal correlations and noise amplitude Ekaterina Machulskaya (Deutscher Wetterdienst, DWD) |
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Perturbing hydrology parameters in seasonal forecasts Dave MacLeod (University of Oxford) |
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Bayesian Framework Design for Quantifying Model Uncertainty: A Univariate Probabilistic Mixture Model Approach Husain Najafi (University of Tehran) |
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Assessing spatial precipitation uncertainties in a convective-scale ensemble Robert Plant (University of Reading) |
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The spatial behaviour and evaluation of a convection-permitting ensemble Nigel Roberts (Met Office) |
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Upscale impact of diabatic processes from convective to near-hemispheric scale Tobias Selz (Meteorologisches Institut, LMU) |
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An adaptive additive inflation scheme for ensemble Kalman filters Matthias Sommer (LMU, Munich) |
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Stochastic multi-scale modeling for weather and climate prediction Aneesh Subramanian (University of Oxford) |
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Impacts of a spatially varying random parameterscheme on cloud cover in MOGREPS Warren Tennant (Met Office) |
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Development of Taiwan Central Weather Bureau Global Ensemble Prediction System for Typhoon-track John Chien-Han Tseng (Central Weather Bureau, Taiwan) |
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Impacts of stochastic physics on tropical variability in models Peter Watson (University of Oxford) |
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Working group reports
| Working group 1: What are the sources of model error? | |
| Working group 2: How can we improve the diagnosis of model error? | |
| Working group 3: What are and how do we measure the pros and cons of existing approaches? | |
Organising committee
ECMWF: Sarah-Jane Lock, Mike Fisher, Richard Forbes, Mark Rodwell
WWRP: Judith Berner, Craig Bishop, Paolo Ruti, Richard Swinbank