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

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

doi:https://doi.org/10.5194/asr-20-49-2023

Articles | Volume 20

https://doi.org/10.5194/asr-20-49-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/asr-20-49-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 20

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

Data sets

Archived 5-min rainfall accumulations from a radar dataset for the Netherlands, Version 1 A. Overeem and R. Imhoff https://doi.org/10.4121/uuid:05a7abc4-8f74-43f4-b8b1-7ed7f5629a01

Dataset associated to the project "Spatial regression of multi-fidelity meteorological observations using a proxy-based measurement error model", Version 1 J. H. S. de Baar and I. Garcia-Marti https://doi.org/10.4121/ffa71bf6-b605-4719-acb5-83b733208e4b

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.