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Comparison of different representations of model error in ensemble forecasts
Author(s) -
Piccolo Chiara,
Cullen Mike J. P.,
Tennant Warren J.,
Semple Adrian T.
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3348
Subject(s) - data assimilation , ensemble forecasting , ensemble average , error analysis , forecast error , statistics , mathematics , errors in variables models , computer science , meteorology , econometrics , climatology , artificial intelligence , geography , geology
The use of analysis increments to represent model error in the Met Office ensemble prediction system is compared with the use of stochastic parametrizations. Since analysis increments can take into account more possible sources of forecast error than stochastic parametrizations which only represent specific sources of error, the spread of the ensemble and the reliability are markedly improved. There is an increase in the rms error of the ensemble mean for some fields. This may be because analysis increments cannot represent state‐dependent statistics, but may also result from the use of initial condition perturbations from the operational ETKF rather than an ensemble data assimilation with a consistent treatment of model error.