Pushing the boundaries of hydrography research
Advancing Best Practices
A EuroGO-SHIP Key Exploitable Result
Numerical Modelling Description
Assessing statistical features of time series through Trend Detection Time method
This study focussed on how expected improvements in data quality and coverage will enable a better assessment of trends in ocean nutrient changes, deoxygenation and acidification, following the implementation of a possible EuroGO-SHIP Research Infrastructure (RI) based on key marine health indicators as defined in the Marine Strategy Framework Directive.
Effects of improved data quality as well as temporal coverage are quantified in terms of their impacts on the Trend Detection Time (TDT), a statistical metric that quantifies the number of years of data that are necessary to robustly detect a trend in a time series data set.
The presentation provides an overview of the study.
The full study and its results are described here: D4.1 Science Case for Numerical Modelling.
Houda Beghoura
Houda is a postdoctoral researcher at Geophysical Institut at University of Bergen and part of the Bjerknes Centre for Climate Research, Norway.
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