|From||Roger Brugge <email@example.com>|
|Date||Mon, 8 Apr 2019 17:01:07 +0000|
The PhD-candidate will set-up an ensemble of runs with the model TSMP-PDAF for the African continent,
which simulate water, energy and biogeochemical cycles for the subsurface and land surface including multiscale assimilation of various remote sensing products. The simulations allow the quantification of the impact of land use land cover change and human
water use on the changes of the terrestrial water, energy and carbon cycles over the African continent, conditioned to remotely sensed satellite products. In order to explore the large amount of output data generated by the ensemble simulations at high spatial
resolution (more than 100 TB), parallel big data analytical methods are an important tool. Besides classical uni-, bi- and multivariate statistics, and time series analysis, also methods suited to detect more complex patterns in space and time like wavelet
analysis and machine learning (ML) algorithms will be used.
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