Spatial regression of multi-fidelity meteorological observations using a proxy-based measurement error model
Jouke H. S. de Baar
CORRESPONDING AUTHOR
Royal Netherlands Metereological Institute (KNMI), De Bilt, the Netherlands
Irene Garcia-Marti
Royal Netherlands Metereological Institute (KNMI), De Bilt, the Netherlands
Gerard van der Schrier
Royal Netherlands Metereological Institute (KNMI), De Bilt, the Netherlands
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In this work, we present the most recent updates in the E-OBS gridded data set for daily mean wind speed over Europe. The data set is provided as an ensemble of equally likely realisations. In addition, we make a preliminary study into possible causes of the observed terrestrial wind stilling effect, such as local changes in surface roughness length. As one of the results, we do observe a terrestrial wind stilling effect, however, the trend varies locally over Europe.
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Gerard van der Schrier and Rob Groenland
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A. A. Cimatoribus, S. S. Drijfhout, V. Livina, and G. van der Schrier
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Short summary
Combining high-fidelity official meteorological observations with low-fidelity crowd-sourced data in a single climate or weather map is challenging because of the significant bias and noise in the low-fidelity data. In this work, we present a method to treat this bias and noise in a statistical framework. In addition, we show that we can make an additional improvement in the quality of the map when we add high-resolution land use information.
Combining high-fidelity official meteorological observations with low-fidelity crowd-sourced...