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    Potential Forecast Skill of Ensemble Prediction and Spread and Skill Distributions of the ECMWF Ensemble Prediction System

    Source: Monthly Weather Review:;1997:;volume( 125 ):;issue: 001::page 99
    Author:
    Buizza, Roberto
    DOI: 10.1175/1520-0493(1997)125<0099:PFSOEP>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Ensemble forecasting is a feasible method to integrate a deterministic forecast with an estimate of the probability distribution of atmospheric states. At the European Centre for Medium-Range Weather Forecasts (ECMWF), the Ensemble Prediction System (EPS) comprises 32 perturbed and 1 unperturbed nonlinear integrations, at T63 spectral triangular truncation and with 19 vertical levels. The perturbed initial conditions are generated using the most unstable directions growing over a 48-h time period, computed at T42L19 resolution. This work describes the performance of the ECMWF EPS during the first 21 months of daily operation, from 1 May 1994 to 31 January 1996, focusing on the 500-hPa geopotential height fields. First, the EPS is described, and the validation approach followed throughout this work is discussed. In particular, spread and skill distribution functions are introduced to define a more integral validation methodology for ensemble prediction. Then, the potential forecast skill of ensemble prediction is estimated considering one ensemble member as verification (perfect ensemble assumption). In particular, the ratio between ensemble spread and control error is computed, and the potential correlation between ensemble spread and control forecast skill is evaluated. The results obtained within the perfect ensemble hypothesis give estimates of the limits of forecast skill to be expected for the ECMWF EPS. Finally, the EPS is validated against analysis fields, and the EPS skill is compared with the skill of the perfect ensemble. Results indicate that the EPS spread is smaller than the distance between the control forecast and the analysis. Considering ensemble spread?control skill scatter diagrams, a so-called faulty index is introduced to estimate the percentage of wrongly predicted cases with small spread/high control skill. Results suggest that there is some correspondence between small ensemble spread and high control skill. Considering the 500-hPa geopotential height field over the Northern Hemisphere at forecast day 7, approximately 20% (45%) of the perturbed ensemble members have anomaly correlation skill higher than 0.6 during warm (cold) seasons, respectively. The percentage of analysis values lying outside the EPS forecast range is thought to be still too high.
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      Potential Forecast Skill of Ensemble Prediction and Spread and Skill Distributions of the ECMWF Ensemble Prediction System

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4203777
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    contributor authorBuizza, Roberto
    date accessioned2017-06-09T16:11:09Z
    date available2017-06-09T16:11:09Z
    date copyright1997/01/01
    date issued1997
    identifier issn0027-0644
    identifier otherams-62841.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4203777
    description abstractEnsemble forecasting is a feasible method to integrate a deterministic forecast with an estimate of the probability distribution of atmospheric states. At the European Centre for Medium-Range Weather Forecasts (ECMWF), the Ensemble Prediction System (EPS) comprises 32 perturbed and 1 unperturbed nonlinear integrations, at T63 spectral triangular truncation and with 19 vertical levels. The perturbed initial conditions are generated using the most unstable directions growing over a 48-h time period, computed at T42L19 resolution. This work describes the performance of the ECMWF EPS during the first 21 months of daily operation, from 1 May 1994 to 31 January 1996, focusing on the 500-hPa geopotential height fields. First, the EPS is described, and the validation approach followed throughout this work is discussed. In particular, spread and skill distribution functions are introduced to define a more integral validation methodology for ensemble prediction. Then, the potential forecast skill of ensemble prediction is estimated considering one ensemble member as verification (perfect ensemble assumption). In particular, the ratio between ensemble spread and control error is computed, and the potential correlation between ensemble spread and control forecast skill is evaluated. The results obtained within the perfect ensemble hypothesis give estimates of the limits of forecast skill to be expected for the ECMWF EPS. Finally, the EPS is validated against analysis fields, and the EPS skill is compared with the skill of the perfect ensemble. Results indicate that the EPS spread is smaller than the distance between the control forecast and the analysis. Considering ensemble spread?control skill scatter diagrams, a so-called faulty index is introduced to estimate the percentage of wrongly predicted cases with small spread/high control skill. Results suggest that there is some correspondence between small ensemble spread and high control skill. Considering the 500-hPa geopotential height field over the Northern Hemisphere at forecast day 7, approximately 20% (45%) of the perturbed ensemble members have anomaly correlation skill higher than 0.6 during warm (cold) seasons, respectively. The percentage of analysis values lying outside the EPS forecast range is thought to be still too high.
    publisherAmerican Meteorological Society
    titlePotential Forecast Skill of Ensemble Prediction and Spread and Skill Distributions of the ECMWF Ensemble Prediction System
    typeJournal Paper
    journal volume125
    journal issue1
    journal titleMonthly Weather Review
    identifier doi10.1175/1520-0493(1997)125<0099:PFSOEP>2.0.CO;2
    journal fristpage99
    journal lastpage119
    treeMonthly Weather Review:;1997:;volume( 125 ):;issue: 001
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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