MODEL ERROR AND ENSEMBLE FORECASTING: A CAUTIONARY TALE

2014 ◽  
Author(s):  
SEAMUS BRADLEY ◽  
ROMAN FRIGG ◽  
HAILIANG DU ◽  
LEONARD A. SMITH
Keyword(s):  
Model Error ◽  
Ensemble Forecasting ◽  
Cautionary Tale

Ensemble Forecasting in a System with Model Error

2005 ◽  
Vol 62 (5) ◽  
pp. 1652-1659 ◽  
Author(s):  
David Orrell
Keyword(s):  
Weather Forecasting ◽  
Initial Conditions ◽  
Model Error ◽  
Ensemble Forecasting ◽  
Observational Error ◽  
Future State ◽  
Ensemble Performance ◽  
Atmospheric State ◽  
Lorenz 96 ◽  
Weather Models

Abstract Error in weather forecasting is due to inaccuracy both in the models used and in the estimate of the current atmospheric state at which the model is initiated. Because weather models are thought to be chaotic and, therefore, sensitive to initial conditions, the technique of ensemble forecasting has been developed in part to address the latter effect. An ensemble of forecasts is made with perturbed initial conditions, with the aim being to produce an estimate of the probability distribution function for the future state of the weather. Some ensemble schemes also include changes to the model. While the ensemble approach is quite widely adopted, however, its verification is complicated, and the effect of model error on ensemble performance is not clear. In this paper, the effect of model error on ensemble behavior for a version of the Lorenz ’96 system is investigated. It is shown that estimates of the model’s ability to shadow the observations, obtained using the model drift, are robust to observational error and smoothing schemes, such as four-dimensional variational data assimilation (4DVAR), and help reveal the effect of model error on ensemble performance. Comparisons are made with full weather models. The aim is to provide a study of ensemble error in the context of the Lorenz ’96 system, which may be useful in formulating questions and experiments for weather models.

An Ensemble Generation Method for Seasonal Forecasting with an Ocean–Atmosphere Coupled Model

2005 ◽  
Vol 133 (2) ◽  
pp. 441-453 ◽  
Author(s):  
Jérôme Vialard ◽  
Frédéric Vitart ◽  
Magdalena A. Balmaseda ◽  
Timothy N. Stockdale ◽  
David L. T. Anderson
Keyword(s):  
Initial Conditions ◽  
Forecast Error ◽  
Random Perturbation ◽  
Coupled Model ◽  
Model Error ◽  
Ensemble Forecasting ◽  
Seasonal Forecasting ◽  
Seasonal Forecasts ◽  
Forecasting System ◽  
Ensemble Generation

Abstract Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.

Police Work Addiction: A Cautionary Tale.

2003 ◽  
Author(s):  
Gerard J. Solan ◽  
Jean M. Casey
Keyword(s):  
Police Work ◽  
Cautionary Tale ◽  
Work Addiction

Reduced model error analysis “application to synchronous machines"

1994 ◽  
Vol 4 (10) ◽  
pp. 1999-2012 ◽  
Author(s):  
Nabil Derbel ◽  
Mohamed B.A. Kamoun ◽  
Michel Poloujadoff
Keyword(s):  
Error Analysis ◽  
Model Error ◽  
Synchronous Machines ◽  
Reduced Model ◽  
Analysis Application

Cervical Myelopathy as a Complication of Untreated Motor Tics: A Cautionary Tale

2020 ◽  
Author(s):  
Mariam Hull ◽  
Mered Parnes
Keyword(s):  
Cervical Myelopathy ◽  
Management Strategies ◽  
Vertebral Artery Dissection ◽  
Tic Disorders ◽  
Artery Dissection ◽  
Cautionary Tale ◽  
Eye Injuries ◽  
Tic Disorder ◽  
Aggressive Management ◽  
Motor Tics

AbstractTic disorders are common, affecting approximately 0.5 to 1% of children and adolescents. Treatment is required only when symptoms are bothersome or impairing to the patient, so many do not require intervention. However, on occasion tics may cause significant morbidity and are referred to as “malignant.” These malignant tics have resulted in cervical myelopathy, subdural hematoma secondary to head banging, biting of lips leading to infection of oral muscles, self-inflicted eye injuries leading to blindness, skeletal fractures, compressive neuropathies, and vertebral artery dissection. We describe a case of malignant tic disorder, with accompanying video segment, resulting in cervical myelopathy and quadriparesis in a child. We also discuss aggressive management strategies for neurologists to prevent potential lifelong disability. This case emphasizes that these malignant tics must be treated with all due haste to prevent such complications.

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