Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-273
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
13 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
On the Effect of Model Parameters on Forecast Objects
Caren Marzban1,2, Corinne Jones2, Ning Li2, and Scott Sandgathe1 1Applied Physics Laboratory, Univ. of Washington, Seattle, WA 98195 USA
2Department of Statistics, Univ. of Washington, Seattle, WA 98195 USA
Abstract. Many physics-based numerical models produce a gridded, spatial field of forecasts, e.g., a temperature "map". However, the field for some quantities such as precipitation generally consists of spatially coherent and disconnected "objects". Certain features of these objects (e.g., number, size, and intensity) are generally of interest. Here, a methodology is developed for assessing the impact of model parameters on features of forecast objects. Although, in principle, the objects can be defined by any means, here they are identified via clustering algorithms. The methodology is demonstrated on precipitation forecasts from a mesoscale numerical weather prediction model.

Citation: Marzban, C., Jones, C., Li, N., and Sandgathe, S.: On the Effect of Model Parameters on Forecast Objects, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-273, in review, 2017.
Caren Marzban et al.
Caren Marzban et al.

Data sets

Precipitation Sensitivity
C. Marzban
https://doi.org/10.5281/zenodo.1043542
Caren Marzban et al.

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Short summary
Some numerical models generate "maps" – for example, temperature or precipitation maps produced by numerical weather prediction models. These maps often contain "objects" – for example, a storm. Features of these objects are generally affected by parameters of the numerical model. This paper puts forth a methodology for exposing both the strength and the statistical significance of the effect of the model parameters on object features.
Some numerical models generate "maps" – for example, temperature or precipitation maps produced...
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