Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Discussion papers
https://doi.org/10.5194/gmd-2019-90
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-90
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 24 Apr 2019

Methods for assessment of models | 24 Apr 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.

Assessment of wavelet-based spatial verification by means of a stochastic precipitation model (wv_verif v0.1.0)

Sebastian Buschow, Jakiw Pidstrigach, and Petra Friederichs Sebastian Buschow et al.
  • Institute of Geoscience and Meteorology, University of Bonn

Abstract. The quality of precipitation forecasts is difficult to evaluate objectively because images with disjoint features surrounded by zero intensities cannot easily be compared pixel by pixel: Any displacement between observed and predicted field is punished twice, generally leading to better marks for coarser models. To answer the question whether a highly resolved model truly delivers an improved representation of precipitation processes, alternative tools are thus needed. Wavelet transformations can be used to summarize high-dimensional data in a few numbers which characterize the field's texture. A comparison of the transformed fields judges models solely based on their ability to predict spatial correlations. The fidelity of the forecast's overall structure is thus investigated separately from potential errors in feature location. This study introduces several new wavelet based structure-scores for the verification of deterministic as well as ensemble predictions. Their properties are rigorously tested in an idealized setting: A recently developed stochastic model for precipitation extremes generates realistic pairs of synthetic observations and forecasts with prespecified spatial correlations. The wavelet-scores are found to react sensitively to differences in structural properties, meaning that the objectively best forecast can be determined even in cases where this task is difficult to accomplish by naked eye. Random rain fields prove to be a useful test-bed for any verification tool that aims for an assessment of structure.

Sebastian Buschow et al.
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Sebastian Buschow et al.
Sebastian Buschow et al.
Viewed  
Total article views: 223 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
168 51 4 223 4 2
  • HTML: 168
  • PDF: 51
  • XML: 4
  • Total: 223
  • BibTeX: 4
  • EndNote: 2
Views and downloads (calculated since 24 Apr 2019)
Cumulative views and downloads (calculated since 24 Apr 2019)
Viewed (geographical distribution)  
Total article views: 199 (including HTML, PDF, and XML) Thereof 198 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited  
Saved  
No saved metrics found.
Discussed  
No discussed metrics found.
Latest update: 20 Jul 2019
Publications Copernicus
Download
Short summary
Highly resolved forecasts of precipitation fields are difficult to evaluate since individual rain features are typically not placed precisely at the right location. Instead of comparing forecasts and observations pixel by pixel, we base our verification on the fields' wavelet transforms which compactly summarize the overall structure. The methodology is rigorously tested using randomly generated rain fields for which that structure can be determined at will.
Highly resolved forecasts of precipitation fields are difficult to evaluate since individual...
Citation