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Linus Magnusson
and
Erland Källén

Abstract

During the past 30 years the skill in ECMWF numerical forecasts has steadily improved. There are three major contributing factors: 1) improvements in the forecast model, 2) improvements in the data assimilation, and 3) the increased number of available observations. In this study the authors are investigating the relative contribution from these three components by using the simple error growth model introduced in a previous study by Lorenz and extended in another study by Dalcher and Kalnay, together with the results from the ECMWF Re-Analysis Interim (ERA-Interim) forecasts where the improvement is only due to an increased number of observations. The authors are also applying the growth model on “lagged” forecast differences in order to investigate the usefulness of the forecast jumpiness as a diagnostic tool for improvements in the forecasts. The main finding is that the main contribution to the reduced forecast error comes from significant initial condition error reductions between 1996 and 2001 together with continuous model improvements. The changes in the available observations contributed to a lesser degree, but the authors note that all the ERA-Interim forecasts are from the satellite era and here the focus is on the midtroposphere in the extratropics. Regarding the jumpiness in the forecasts, this is mainly a function of the error in the initial conditions and is therefore an insufficient tool to investigate improvements in the full forecasting system.

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Linus Magnusson
,
Martin Leutbecher
, and
Erland Källén

Abstract

In this paper a study aimed at comparing the perturbation methodologies based on the singular vector ensemble prediction system (SV-EPS) and the breeding vector ensemble prediction system (BV-EPS) in the same model environment is presented. A simple breeding system (simple BV-EPS) as well as one with regional rescaling dependent on an estimate of the analysis error variance (masked BV-EPS) were used. The ECMWF Integrated Forecast System has been used and the three experiments are compared for 46 forecast cases between 1 December 2005 and 15 January 2006. By studying the distribution of the perturbation energy it was possible to see large differences between the experiments initially, but after 48 h the distributions have converged. Using probabilistic scores, these results show that SV-EPS has a somewhat better performance for the Northern Hemisphere compared to BV-EPS. For the Southern Hemisphere masked BV-EPS and SV-EPS yield almost equal results. For the tropics the masked breeding ensemble shows the best performance during the first 6 days. One reason for this is the current setup of the singular vector ensemble at ECMWF yielding in general very low initial perturbation amplitudes in the tropics.

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Lisa K. Bengtsson
,
Linus Magnusson
, and
Erland Källén

Abstract

One desirable property within an ensemble forecast system is to have a one-to-one ratio between the root-mean-square error (rmse) of the ensemble mean and the standard deviation of the ensemble (spread). The ensemble spread and forecast error within the ECMWF ensemble prediction system has been extrapolated beyond 10 forecast days using a simple model for error growth. The behavior of the ensemble spread and the rmse at the time of the deterministic predictability are compared with derived relations of rmse at the infinite forecast length and the characteristic variability of the atmosphere in the limit of deterministic predictability. Utilizing this methodology suggests that the forecast model and the atmosphere do not have the same variability, which raises the question of how to obtain a perfect ensemble.

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Xiang-Yu Huang
,
Annette Cederskov
, and
Erland Källén

Abstract

The objective of this study is to examine the performance of the adiabatic digital filtering initialization scheme of Lynch and Huang, the diabatic digital filtering initialization scheme of Huang and Lynch, and the diabatic nonlinear normal-mode initialization scheme of Cederskov in a complete data assimilation system. In particular, the authors wish to examine the handling of observations and the changes that the initialization makes to the analysis in an intermittent data assimilation cycle. As a reference the authors use the adiabatic nonlinear normal-mode initialization of Machenhauer, formulated according to Bijlsma and Hafkenscheid, which is the current operational initialization scheme at the, Danish Meteorological Institute.

The initialization schemes tested are found to produce a well-balanced model state that is at least as good as that produced by the reference scheme. Furthermore, the changes to the analysis made by the different initialization schemes are similar and the observations are therefore treated similarly with the different schemes. It is thus found that the introduction of a new initialization procedure has no detrimental effect on the data assimilation cycle. On the contrary, the two diabatic schemes reduce the noise level considerably compared to the adiabatic ones albeit at an increased computational cost. Considering the advantages of a diabatic scheme, in particular the future possibility of including cloud properties in the initialization procedure (Huang and Sundqvist), the use of a diabatic scheme seems well justified. The noise reduction is perhaps not the most important aspect as all schemes behave identically in the handling of observations. Instead, the possibility of including satellite-derived cloudiness and precipitation data in the analysis and initialization cycle is a much move important aspect. From this point of view the digital filter has a clear advantage over the normal-mode initialization scheme as all dependent variables of the model are initialized.

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