Preventing the Mistraining of Anomaly-Based IDSs through Ensemble Systems

2014 ◽  
Author(s):  
Conor Fellin ◽  
Michael Haney
Keyword(s):  
Ensemble Systems

scholarly journals Sensitivity of forecast skill to the parameterisation of moist convection in a limited-area ensemble forecast system

2018 ◽  
Author(s):  
Matteo Vasconi ◽  
Andrea Montani ◽  
Tiziana Paccagnella
Keyword(s):  
Forecast Skill ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Forecast Errors ◽  
Total Precipitation ◽  
Moist Convection ◽  
Cosmo Model ◽  
Spatio Temporal ◽  
Convection Schemes ◽  
Ensemble Systems

Abstract. The parameterisation of convection in limited-area models is an important source of uncertainty as regards the spatio-temporal forecast of precipitation. As for the limited-area model COSMO, hitherto, only the Tiedtke convection scheme was available for the operational runs of the model in convection-parameterised mode. In addition to this the Bechtold scheme, implemented in ECMWF global model, has recently been adapted for COSMO applications. The development and implementation of ensemble systems in which different convection schemes are used, provides an opportunity to upgrade state-of-the-art probabilistic systems at the convection-parameterised scale. The sensitivity of the COSMO model forecast skill to the use of either the Tietdke or the Bechtold schemes is assessed by performing different sets of experiments. The performance of COSMO model run with the different schemes is investigated in ensemble mode with particular attention to the types of forecast errors (e.g. location, timing, intensity) provided by the different convection schemes in terms of total precipitation. A 10-member ensemble has been run for approximately 2 months with the Bechtold scheme, using the same initial and boundary conditions as members 1–10 of the operational COSMO-LEPS ensemble system (which has 20 members, all run with the Tiedtke scheme). The performance of these members is assessed and compared to that of the system made of members 1–10 of COSMO-LEPS in terms of total precipitation prediction. Finally, the performance of an experimental 20-member ensemble system (which has 10 members run with the Bechtold plus 10 members run with the Tiedtke scheme) is compared to that of operational COSMO-LEPS over the 2-month period. The new system turned out to have higher skill in terms of precipitation forecast with respect to COSMO-LEPS over the period. In this approach the use of the Bechtold scheme is proposed as a perturbation for the COSMO-LEPS ensemble, relatively to how uncertainties in the model representation of the cumulus convection can be described and quantified.

scholarly journals Fixed-Endpoint Optimal Control of Bilinear Ensemble Systems

2017 ◽  
Vol 55 (5) ◽  
pp. 3039-3065 ◽  
Author(s):  
Shuo Wang ◽  
Jr-Shin Li
Keyword(s):  
Optimal Control ◽  
Ensemble Systems

Factors Influencing Ensemble Sensitivity–Based Targeted Observing Predictions at Convection-Allowing Resolutions

2020 ◽  
Vol 148 (11) ◽  
pp. 4497-4517
Author(s):  
Aaron J. Hill ◽  
Christopher C. Weiss ◽  
Brian C. Ancell
Keyword(s):  
Random Perturbation ◽  
Ensemble Prediction ◽  
Ensemble Prediction System ◽  
Small Random Perturbation ◽  
Numerical Noise ◽  
Targeted Observations ◽  
Targeted Observation ◽  
Ensemble Systems ◽  
Short Time ◽  
Impact Predictions

AbstractEnsemble sensitivity analysis (ESA) is applied to select types of observations, in various locations and in advance of forecast convection, to systematically evaluate the effectiveness of ESA-based observation targeting for 10 convection forecasts. To facilitate the analysis, observing system simulation experiments and perfect models are utilized to generate synthetic targeted observations of temperature and pressure for future assimilation with an ensemble prediction system. Various observation assimilation experiments are carried out to assess the impacts of nonlinearity, covariance localization, and numerical noise on ESA-based observation-impact predictions. It is discovered that localization applied during data assimilation restricts targeted-observation increments onto the forecast responses of composite reflectivity and 3-hourly accumulated precipitation, making impact predictions poor. In addition, numerical noise introduced by nonlinear perturbation evolution tends to reduce the correlations between observed and predicted impacts; small, random-perturbation experiments often yielded similar impacts on forecasts as targeted observations. Nonlinearity also manifests in the observation impacts when comparing targeted observations with nontargeted, randomly chosen observations; random observations have seemingly the same impact on forecasts as targeted observations. The results, under idealized conditions and simplified ensemble configurations, demonstrate that ESA-based targeting for nonlinear convection forecasts may be most applicable at short time scales. Important implications for ESA-based targeting methods employed with real-time ensemble systems are also discussed.

scholarly journals “Jumpiness” of the ECMWF and Met Office EPS Control and Ensemble-Mean Forecasts

2009 ◽  
Vol 137 (11) ◽  
pp. 3823-3836 ◽  
Author(s):  
Ervin Zsoter ◽  
Roberto Buizza ◽  
David Richardson
Keyword(s):  
Time Series ◽  
Weak Link ◽  
Forecast Error ◽  
Forecast Uncertainty ◽  
Flip Flop ◽  
Lower Error ◽  
Ensemble Mean ◽  
Analysis Error ◽  
Ensemble Systems

Abstract This work investigates the inconsistency between forecasts issued at different times but valid for the same time, and shows that ensemble-mean forecasts are less inconsistent than corresponding control forecasts. The “jumpiness” index, the concepts of different forecast jumps—the “flip,” “flip-flop,” and “flip-flop-flip”—and the inconsistency correlation between time series of inconsistency indices are introduced to measure the consistency/inconsistency of consecutive forecasts. These new measures are used to compare the behavior of the ECMWF and the Met Office control and ensemble-mean forecasts for an 18-month period over Europe. Results indicate that for both the ECMWF and the Met Office ensembles, the ensemble-mean forecast is less inconsistent than the control forecast. However, they also indicate that the ensemble mean follows its corresponding control forecast more closely than the controls (or the ensemble means) of the two ensemble systems following each other, thus suggesting weaknesses in both ensemble systems in the simulation of forecast uncertainty due to model or analysis error. Results also show that there is only a weak link between forecast jumpiness and forecast error (i.e., forecasts with lower inconsistency do not necessarily have, on average, lower error).

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