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.
Highlights
Accurate representation of observed precipitation spatial patterns and structures is essential for hydrologic applications
An object-based approach is presented in this article to evaluate simulated precipitation by focusing on separable local-scale precipitation areas
The application of the approach is demonstrated in the validation of the satellite precipitation product PERSIANN against the stage IV rain analysis on a daily scale for the summer of 2008
Summary
Accurate representation of observed precipitation spatial patterns and structures is essential for hydrologic applications. It has been noted that the spatial variability of precipitation has a major impact on the accuracy of modelled runoff volumes (Faurès et al 1995; Goodrich et al 1995). In distributed hydrological modelling, the spatial patterns and locations of precipitation events are important to describe the spatial heterogeneity of precipitation (Foufoula-Georgiou and Vuruputur 2001). Satellite-based precipitation products and numerical weather prediction (NWP) models have provided precipitation estimates and forecasts, respectively, with high spatial and temporal resolution suitable for hydrologic modelling and watershed management. The quality of the simulated precipitation datasets is a vital factor in the decision to use these estimates for practical applications. It is imperative that verification be an integral component of precipitation algorithms and dataset development
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call