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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 10
Geosci. Model Dev., 11, 4011-4019, 2018
https://doi.org/10.5194/gmd-11-4011-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 4011-4019, 2018
https://doi.org/10.5194/gmd-11-4011-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 05 Oct 2018

Methods for assessment of models | 05 Oct 2018

Data assimilation cycle length and observation impact in mesoscale ocean forecasting

Paul Sandery Paul Sandery
  • CSIRO Oceans and Atmosphere, Castray Esplanade, Battery Point TAS 7004, Australia

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. Comparison of mean absolute increments identifies observations have greater impact in the 1-day system. Whilst smaller mean increments and greater observation impact do not guarantee a better forecast system, analysis of 7-day parallel forecasts show that the 1-day cycle system delivers improvement in predictability, particularly for the subsurface. This improvement appears to mainly come from less biased initial conditions and suggests greater retention of memory from observations and improved balance in the model.

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This article compares global mesoscale ocean forecasts with different data assimilation cycle lengths. Mean absolute increment is used to quantify differences in the overall impact of observations. Greater observation impact does not necessarily improve a forecast system. Experiments show a 1-day cycle generates improved 7-day forecasts when compared to a 3-day cycle. Cycle length is an important choice that influences system bias and predictability.
This article compares global mesoscale ocean forecasts with different data assimilation cycle...
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