Articles | Volume 20
https://doi.org/10.5194/asr-20-49-2023
https://doi.org/10.5194/asr-20-49-2023
06 Jun 2023
 | 06 Jun 2023

Spatial regression of multi-fidelity meteorological observations using a proxy-based measurement error model

Jouke H. S. de Baar, Irene Garcia-Marti, and Gerard van der Schrier

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Cited articles

CBS: Geografische data, https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/ (last access: January 2023), 2023. a
Crameri, F.: Scientific colour maps, Zenodo [code], https://doi.org/10.5281/zenodo.5501399, 2018. 
de Baar, J. H. S. and Garcia-Marti, I.: Recent improvements in spatial regression of climate data, in: NATO-AVT-354 workshop on multi-fidelity methods for military vehicle design, 26–28 September 2022, Varna, Bulgaria, https://doi.org/10.13140/RG.2.2.25417.52325, 2022. a, b
de Baar, J. H. S. and Garcia-Marti, I.: Dataset associated to the project “Spatial regression of multi-fidelity meteorological observations using a proxy-based measurement error model”, Version 1, 4TU.ResearchData [code and data set], https://doi.org/10.4121/ffa71bf6-b605-4719-acb5-83b733208e4b, 2023. a
de Baar, J. H. S., Percin, M., Dwight, R. P., van Oudheusden, B. W., and Bijl, H.: Kriging regression of PIV data using a local error estimate, Exp. Fluids, 55, 1650, https://doi.org/10.1007/s00348-013-1650-z, 2014. a
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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.