An object-based approach for verification of precipitation estimation

Abstract

Verification has become an integral component in the development of precipitation algorithms used in satellite-based precipitation products and evaluation of numerical weather prediction models. A number of object-based verification methods have been developed to quantify the errors related to spatial patterns and placement of precipitation. In this study, an image processing technique known as watershed transformation, capable of detecting closely spaced, but separable precipitation areas, is adopted in the object-based approach. Several key attributes of the segmented precipitation objects are selected and interest values of those attributes are estimated based on the distance measurement of the estimated and reference images. An overall interest score is estimated from all the selected attributes and their interest values. The proposed object-based approach is implemented to validate satellite-based precipitation estimation against ground radar observations. The results indicate that the watershed segmentation technique is capable of separating the closely spaced local-scale precipitation areas. In addition, three verification metrics, including the object-based false alarm ratio, object-based missing ratio, and overall interest score, reveal the skill of precipitation estimates in depicting the spatial and geometric characteristics of the precipitation structure against observations.

Acknowledgements

The authors would like to acknowledge the MODE developers (Davis et al. 2009) for inspiring the research direction. The authors would also like to thank Yudong Tian, Christa Peters-Lidard, and Phil Arkin for their helpful suggestions. Furthermore, the authors would like to thank Dan Braithwaite for his assistance with the data preparation and the reviewers for their valuable comments and suggestions that led to the improvement of this work.