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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2017-298
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 05 Mar 2018

Methods for assessment of models | 05 Mar 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.

Data assimilation cycle length and observation impact in mesoscale ocean forecasting

Paul Sandery Paul Sandery
  • CSIRO Oceans and Atmosphere, Castray Esplanade Battery Point TAS 7008

Abstract. A brief examination of the relationship between data assimilation cycle length and observation impact in a practical global mesoscale ocean forecasting setting is provided. Behind real-time reanalyses and forecasts from two different cycle length systems are compared and skill is quantified using all observations typically available for ocean forecasting. A 1-day Ensemble Optimal Interpolation (EnOI) cycle is compared to a 3-day cycle. The mean analysis increments for the 1-day system are significantly smaller suggesting a less biased system. Mean Absolute Increment is used to compare observation impact between the two systems. This shows that the 1-day system has larger mean absolute increments than the 3-day system indicating the observations are having a greater impact with the shorter cycle length. Whilst this alone does not guarantee a better forecast system, analysis of 7-day parallel forecasts shows that the 1-day cycle system delivers improvement in predictability, particularly for western boundary current regions and the sub-surface when compared to all available independent observations. The results are dependent on region, model and observing system, however, suggest the 1-day cycle provides better overall forecast skill. This is thought to come from less biased initial conditions, greater observation impact and improved consistency with respect to the timing of model and observations.

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Paul Sandery
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Interactive discussion
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Paul Sandery
Paul Sandery
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Latest update: 20 Sep 2018
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Short summary
This research was carried out as a contribution to understanding system wide impact of observations and the role of model bias in ocean forecasting. Also promoted is the use of all forward observations for forecast system verification. Improvements in ocean forecasting can be made with less biased initial conditions, improved observation impact and consistency with respect to the timing of model and observations.
This research was carried out as a contribution to understanding system wide impact of...
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