Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
18 Aug 2023
Methods for assessment of models |
| 18 Aug 2023
| 18 Aug 2023
Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Christopher L. Follett
Department of Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool L69 3GP
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Jacob Bien
Data Sciences and Operations, University of Southern California, Los Angeles, California, USA
Stephanie Dutkiewicz
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Sangwon Hyun
Department of Statistics, University of California Santa Cruz, Santa Cruz, California, USA
Gemma Kulk
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Gael L. Forget
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Christian Müller
Department of Statistics, LMU/HMGU Munich, Munich, Germany
Marie-Fanny Racault
School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Christopher N. Hill
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Thomas Jackson
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
Shubha Sathyendranath
Plymouth Marine Laboratory, Prospect Place, Plymouth PL1 3DH, UK
National Centre for Earth Observation, Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
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Short summary
While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
While biogeochemical models and satellite-derived ocean color data provide unprecedented...
