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Spatial trend analysis of gridded temperature data sets at varying spatial scales

Banff International Research Station for Mathematical Innovation and Discovery

In general, reliable trend estimates for temperature data may be challenging to obtain, mainly due to data scarcity. Short data series represent an intrinsic problem, whereas spatial sparsity may, in the case of spatially correlated data, be managed by adding appropriate spatial structure to the model. In this study, we analyse European temperature data over a period of 65 years. We search for trends in seasonal means and investigate the effect of varying the data grid resolution on the significance of the trend estimates obtained. We consider a set of models with different temporal and spatial structures and compare the resulting spatial trends along axes of model complexity and data grid resolution. This is ongoing work and the presentation will sketch the idea and give some preliminary results.

Mathematics; Statistics; Computer science; Data mining

video/mp4

Author affiliation: Norwegian Computing Center

2018-05-01

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/65712

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/