Application of Data Assimilation to Ocean and Climate Prediction

Michael J. Bell (), Matthew J. Martin () and Nancy K. Nichols ()
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Michael J. Bell: Met Office
Matthew J. Martin: Met Office
Nancy K. Nichols: University of Reading, School of Mathematical and Physical Sciences

A chapter in UK Success Stories in Industrial Mathematics, 2016, pp 3-10 from Springer

Abstract: Abstract Ocean prediction systems are now able to analyse and predict temperature, salinity and velocity structures within the ocean by assimilating measurements of the ocean’s temperature, salinity and height into physically based ocean models. Data assimilation combines current estimates of state variables, such as temperature, salinity and height from a computational model with measurements of the ocean and atmosphere in order to improve forecasts and reduce uncertainty in the forecast accuracy. Data assimilation generally works well with ocean models away from the equator but has been found to induce vigorous and unrealistic overturning circulations near the equator. A pressure correction method was developed at the University of Reading and the Met Office to control these circulations using ideas from control theory and an understanding of equatorial dynamics. The method has been used for the last 10 years in seasonal forecasting and ocean prediction systems at the Met Office and European Centre for Medium-range Weather Forecasting (ECMWF). It has been an important element in recent re-analyses of the ocean heat uptake that mitigates climate change.

Keywords: Data Assimilation; Shallow Water Equation; Assimilation System; Seasonal Forecast; Pressure Correction (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-25454-8_1

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DOI: 10.1007/978-3-319-25454-8_1

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