MIdASv0.2.1 – MultI-scale bias AdjuStment

Berg, Peter; Bosshard, Thomas; Yang, Wei; Zimmermann, Klaus

doi:https://doi.org/10.5194/gmd-15-6165-2022

Articles | Volume 15, issue 15

https://doi.org/10.5194/gmd-15-6165-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-6165-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 15

Methods for assessment of models

|

05 Aug 2022

Methods for assessment of models |

| 05 Aug 2022

MIdASv0.2.1 – MultI-scale bias AdjuStment

Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann

Data sets

MIdAS: Bias adjustment intercomparison and evaluation scripts Peter Berg, Thomas Bosshard, and Wei Yang https://doi.org/10.5281/zenodo.6043222

Model code and software

MIdAS (MultI-scale bias AdjuStment) Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann zenodo.6624233

MIdAS (MultI-scale bias AdjuStment) Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann https://git.smhi.se/midas/midas

Short summary

When performing impact analyses with climate models, one is often confronted with the issue that the models have significant bias. Commonly, the modelled climatological temperature deviates from the observed climate by a few degrees or it rains excessively in the model. MIdAS employs a novel statistical model to translate the model climatology toward that observed using novel methodologies and modern tools. The coding platform allows opportunities to develop methods for high-resolution models.